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Analysis of covariance\n\n\n* ANOVA: explanatory variables categorical (divide data into groups)\n\n* traditionally, analysis of covariance has categorical $x$'s plus one numerical $x$ (\"covariate\") to be adjusted for.\n\n* `lm` handles this too.\n\n* Simple example: two treatments (drugs) (`a` and `b`), with before and after scores. \n\n\n* Does knowing before score and/or treatment help to predict after score?\n\n* Is after score different by treatment/before score?\n\n\n\n\n\n## Data\n\nTreatment, before, after: \n\n\n\\scriptsize\n```\na 5 20\na 10 23\na 12 30\na 9 25\na 23 34\na 21 40\na 14 27\na 18 38\na 6 24\na 13 31\nb 7 19\nb 12 26\nb 27 33\nb 24 35\nb 18 30\nb 22 31\nb 26 34\nb 21 28\nb 14 23\nb 9 22\n```\n\\normalsize\n\n\n## Packages\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\nlibrary(broom)\nlibrary(marginaleffects)\n```\n:::\n\n\nthe last of these for predictions.\n\n## Read in data\n\n\n::: {.cell}\n\n```{.r .cell-code}\nurl <- \"http://ritsokiguess.site/datafiles/ancova.txt\"\nprepost <- read_delim(url, \" \")\nprepost\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n\n\n## Making a plot\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(prepost, aes(x = before, y = after, colour = drug)) +\n geom_point()\n```\n\n::: {.cell-output-display}\n![](ancova_files/figure-revealjs/ancova-plot-1.png){width=960}\n:::\n:::\n\n\n\n\n## Comments \n\n* As before score goes up, after score goes up.\n\n* Red points (drug A) generally above blue points (drug B), for\ncomparable before score.\n\n* Suggests before score effect *and* drug effect.\n\n\n## The means\n\n::: {.cell}\n\n```{.r .cell-code}\nprepost %>%\n group_by(drug) %>%\n summarize(\n before_mean = mean(before),\n after_mean = mean(after)\n )\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n \n\n\n* Mean \"after\" score slightly higher for treatment A.\n\n* Mean \"before\" score much higher for treatment B.\n\n* Greater *improvement* on treatment A. \n\n\n\n## Testing for interaction\n\n::: {.cell}\n\n```{.r .cell-code}\nprepost.1 <- lm(after ~ before * drug, data = prepost)\nanova(prepost.1)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n \n\n\n* Interaction not significant. Will remove later.\n\n## Predictions\n\nSet up values to predict for:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(prepost)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n drug before after \n Length:20 Min. : 5.00 Min. :19.00 \n Class :character 1st Qu.: 9.75 1st Qu.:23.75 \n Mode :character Median :14.00 Median :29.00 \n Mean :15.55 Mean :28.65 \n 3rd Qu.:21.25 3rd Qu.:33.25 \n Max. :27.00 Max. :40.00 \n```\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nnew <- datagrid(before = c(9.75, 14, 21.25), \n drug = c(\"a\", \"b\"), model = prepost.1)\n```\n:::\n\n\n## and then\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(prepost.1, newdata = new)) %>% \n select(drug, before, estimate)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\\normalsize\n\n## Predictions (with interaction included), plotted\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_cap(model = prepost.1, condition = c(\"before\", \"drug\"))\n```\n\n::: {.cell-output-display}\n![](ancova_files/figure-revealjs/unnamed-chunk-4-1.png){width=960}\n:::\n:::\n\n\nLines almost parallel, but not quite.\n\n\n\n## Taking out interaction\n\\small\n\n::: {.cell}\n\n```{.r .cell-code}\nprepost.2 <- update(prepost.1, . ~ . - before:drug)\nanova(prepost.2)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n \n\\normalsize\n\n\n* Take out non-significant interaction.\n\n* `before` and `drug` strongly significant.\n\n* Do predictions again and plot them.\n\n## Predictions\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(prepost.2, newdata = new)) %>% \n select(drug, before, estimate)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n\n## Plot of predicted values\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_cap(prepost.2, condition = c(\"before\", \"drug\"))\n```\n\n::: {.cell-output-display}\n![](ancova_files/figure-revealjs/unnamed-chunk-6-1.png){width=960}\n:::\n:::\n\n\nThis time the lines are *exactly* parallel. No-interaction model forces them\nto have the same slope. \n\n\n\n## Different look at model output\n\n\n* `anova(prepost.2)` tests for significant effect of\nbefore score and of drug, but doesn't help with interpretation.\n\n* `summary(prepost.2)` views as regression with slopes:\n\n\\scriptsize\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(prepost.2)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\nCall:\nlm(formula = after ~ before + drug, data = prepost)\n\nResiduals:\n Min 1Q Median 3Q Max \n-3.6348 -2.5099 -0.2038 1.8871 4.7453 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 18.3600 1.5115 12.147 8.35e-10 ***\nbefore 0.8275 0.0955 8.665 1.21e-07 ***\ndrugb -5.1547 1.2876 -4.003 0.000921 ***\n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 2.682 on 17 degrees of freedom\nMultiple R-squared: 0.817,\tAdjusted R-squared: 0.7955 \nF-statistic: 37.96 on 2 and 17 DF, p-value: 5.372e-07\n```\n:::\n:::\n\n\n\\normalsize \n\n\n\n\n## Understanding those slopes\n\n\\footnotesize\n\n::: {.cell}\n\n```{.r .cell-code}\ntidy(prepost.2)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n\\normalsize\n\n\n\n* `before` ordinary numerical variable; `drug`\ncategorical. \n\n* `lm` uses first category `druga` as baseline.\n\n* Intercept is prediction of after score for before score 0 and\n*drug A*.\n\n* `before` slope is predicted change in after score when\nbefore score increases by 1 (usual slope)\n\n* Slope for `drugb` is *change* in predicted after\nscore for being on drug B rather than drug A. Same for *any*\nbefore score (no interaction).\n\n\n\n## Summary\n\n\n* ANCOVA model: fits different regression line for each group,\npredicting response from covariate.\n\n* ANCOVA model with interaction between factor and covariate\nallows different slopes for each line.\n\n* Sometimes those lines can cross over!\n\n* If interaction not significant, take out. Lines then parallel.\n\n* With parallel lines, groups have consistent effect regardless\nof value of covariate.\n\n", + "markdown": "---\ntitle: \"Analysis of Covariance\"\n---\n\n\n\n\n## Analysis of covariance\n\n\n* ANOVA: explanatory variables categorical (divide data into groups)\n\n* traditionally, analysis of covariance has categorical $x$'s plus one numerical $x$ (\"covariate\") to be adjusted for.\n\n* `lm` handles this too.\n\n* Simple example: two treatments (drugs) (`a` and `b`), with before and after scores. \n\n\n* Does knowing before score and/or treatment help to predict after score?\n\n* Is after score different by treatment/before score?\n\n\n\n\n\n## Data\n\nTreatment, before, after: \n\n\n\\scriptsize\n```\na 5 20\na 10 23\na 12 30\na 9 25\na 23 34\na 21 40\na 14 27\na 18 38\na 6 24\na 13 31\nb 7 19\nb 12 26\nb 27 33\nb 24 35\nb 18 30\nb 22 31\nb 26 34\nb 21 28\nb 14 23\nb 9 22\n```\n\\normalsize\n\n\n## Packages\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\nlibrary(broom)\nlibrary(marginaleffects)\n```\n:::\n\n\nthe last of these for predictions.\n\n## Read in data\n\n\n::: {.cell}\n\n```{.r .cell-code}\nurl <- \"http://ritsokiguess.site/datafiles/ancova.txt\"\nprepost <- read_delim(url, \" \")\nprepost\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n\n\n## Making a plot\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(prepost, aes(x = before, y = after, colour = drug)) +\n geom_point()\n```\n\n::: {.cell-output-display}\n![](ancova_files/figure-revealjs/ancova-plot-1.png){width=960}\n:::\n:::\n\n\n\n\n## Comments \n\n* As before score goes up, after score goes up.\n\n* Red points (drug A) generally above blue points (drug B), for\ncomparable before score.\n\n* Suggests before score effect *and* drug effect.\n\n\n## The means\n\n::: {.cell}\n\n```{.r .cell-code}\nprepost %>%\n group_by(drug) %>%\n summarize(\n before_mean = mean(before),\n after_mean = mean(after)\n )\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n \n\n\n* Mean \"after\" score slightly higher for treatment A.\n\n* Mean \"before\" score much higher for treatment B.\n\n* Greater *improvement* on treatment A. \n\n\n\n## Testing for interaction\n\n::: {.cell}\n\n```{.r .cell-code}\nprepost.1 <- lm(after ~ before * drug, data = prepost)\nanova(prepost.1)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n \n\n\n* Interaction not significant. Will remove later.\n\n## Predictions\n\nSet up values to predict for:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(prepost)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n drug before after \n Length:20 Min. : 5.00 Min. :19.00 \n Class :character 1st Qu.: 9.75 1st Qu.:23.75 \n Mode :character Median :14.00 Median :29.00 \n Mean :15.55 Mean :28.65 \n 3rd Qu.:21.25 3rd Qu.:33.25 \n Max. :27.00 Max. :40.00 \n```\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nnew <- datagrid(before = c(9.75, 14, 21.25), \n drug = c(\"a\", \"b\"), model = prepost.1)\n```\n:::\n\n\n## and then\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(prepost.1, newdata = new)) %>% \n select(drug, before, estimate)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\\normalsize\n\n## Predictions (with interaction included), plotted\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_predictions(model = prepost.1, condition = c(\"before\", \"drug\"))\n```\n\n::: {.cell-output-display}\n![](ancova_files/figure-revealjs/unnamed-chunk-4-1.png){width=960}\n:::\n:::\n\n\nLines almost parallel, but not quite.\n\n\n\n## Taking out interaction\n\\small\n\n::: {.cell}\n\n```{.r .cell-code}\nprepost.2 <- update(prepost.1, . ~ . - before:drug)\nanova(prepost.2)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n \n\\normalsize\n\n\n* Take out non-significant interaction.\n\n* `before` and `drug` strongly significant.\n\n* Do predictions again and plot them.\n\n## Predictions\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(prepost.2, newdata = new)) %>% \n select(drug, before, estimate)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n\n## Plot of predicted values\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_predictions(prepost.2, condition = c(\"before\", \"drug\"))\n```\n\n::: {.cell-output-display}\n![](ancova_files/figure-revealjs/unnamed-chunk-6-1.png){width=960}\n:::\n:::\n\n\nThis time the lines are *exactly* parallel. No-interaction model forces them\nto have the same slope. \n\n\n\n## Different look at model output\n\n\n* `anova(prepost.2)` tests for significant effect of\nbefore score and of drug, but doesn't help with interpretation.\n\n* `summary(prepost.2)` views as regression with slopes:\n\n\\scriptsize\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(prepost.2)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\nCall:\nlm(formula = after ~ before + drug, data = prepost)\n\nResiduals:\n Min 1Q Median 3Q Max \n-3.6348 -2.5099 -0.2038 1.8871 4.7453 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 18.3600 1.5115 12.147 8.35e-10 ***\nbefore 0.8275 0.0955 8.665 1.21e-07 ***\ndrugb -5.1547 1.2876 -4.003 0.000921 ***\n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 2.682 on 17 degrees of freedom\nMultiple R-squared: 0.817,\tAdjusted R-squared: 0.7955 \nF-statistic: 37.96 on 2 and 17 DF, p-value: 5.372e-07\n```\n:::\n:::\n\n\n\\normalsize \n\n\n\n\n## Understanding those slopes\n\n\\footnotesize\n\n::: {.cell}\n\n```{.r .cell-code}\ntidy(prepost.2)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n\\normalsize\n\n\n\n* `before` ordinary numerical variable; `drug`\ncategorical. \n\n* `lm` uses first category `druga` as baseline.\n\n* Intercept is prediction of after score for before score 0 and\n*drug A*.\n\n* `before` slope is predicted change in after score when\nbefore score increases by 1 (usual slope)\n\n* Slope for `drugb` is *change* in predicted after\nscore for being on drug B rather than drug A. Same for *any*\nbefore score (no interaction).\n\n\n\n## Summary\n\n\n* ANCOVA model: fits different regression line for each group,\npredicting response from covariate.\n\n* ANCOVA model with interaction between factor and covariate\nallows different slopes for each line.\n\n* Sometimes those lines can cross over!\n\n* If interaction not significant, take out. Lines then parallel.\n\n* With parallel lines, groups have consistent effect regardless\nof value of covariate.\n\n", "supporting": [ "ancova_files/figure-revealjs" ], diff --git a/_freeze/ancova/execute-results/tex.json b/_freeze/ancova/execute-results/tex.json index 89a1cf8..e0e3a78 100644 --- a/_freeze/ancova/execute-results/tex.json +++ b/_freeze/ancova/execute-results/tex.json @@ -1,7 +1,7 @@ { - "hash": "39e30dfec49e66a4ffc46ebb3db688c2", + "hash": "1a46e4f6302227d07b496793324ee69b", "result": { - "markdown": "---\ntitle: \"Analysis of Covariance\"\n---\n\n\n\n\n\n## Analysis of covariance\n\n\n* ANOVA: explanatory variables categorical (divide data into groups)\n\n* traditionally, analysis of covariance has categorical $x$'s plus one numerical $x$ (\"covariate\") to be adjusted for.\n\n* `lm` handles this too.\n\n* Simple example: two treatments (drugs) (`a` and `b`), with before and after scores. \n\n\n* Does knowing before score and/or treatment help to predict after score?\n\n* Is after score different by treatment/before score?\n\n\n\n\n\n## Data\n\nTreatment, before, after: \n\n\n\\scriptsize\n```\na 5 20\na 10 23\na 12 30\na 9 25\na 23 34\na 21 40\na 14 27\na 18 38\na 6 24\na 13 31\nb 7 19\nb 12 26\nb 27 33\nb 24 35\nb 18 30\nb 22 31\nb 26 34\nb 21 28\nb 14 23\nb 9 22\n```\n\\normalsize\n\n\n## Packages\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\nlibrary(broom)\nlibrary(marginaleffects)\n```\n:::\n\n\n\nthe last of these for predictions.\n\n## Read in data\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nurl <- \"http://ritsokiguess.site/datafiles/ancova.txt\"\nprepost <- read_delim(url, \" \")\nprepost\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 20 x 3\n drug before after\n \n 1 a 5 20\n 2 a 10 23\n 3 a 12 30\n 4 a 9 25\n 5 a 23 34\n 6 a 21 40\n 7 a 14 27\n 8 a 18 38\n 9 a 6 24\n10 a 13 31\n11 b 7 19\n12 b 12 26\n13 b 27 33\n14 b 24 35\n15 b 18 30\n16 b 22 31\n17 b 26 34\n18 b 21 28\n19 b 14 23\n20 b 9 22\n```\n:::\n:::\n\n\n\n\n\n## Making a plot\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(prepost, aes(x = before, y = after, colour = drug)) +\n geom_point()\n```\n\n::: {.cell-output-display}\n![](ancova_files/figure-beamer/ancova-plot-1.pdf)\n:::\n:::\n\n\n\n\n\n## Comments \n\n* As before score goes up, after score goes up.\n\n* Red points (drug A) generally above blue points (drug B), for\ncomparable before score.\n\n* Suggests before score effect *and* drug effect.\n\n\n## The means\n\n\n::: {.cell}\n\n```{.r .cell-code}\nprepost %>%\n group_by(drug) %>%\n summarize(\n before_mean = mean(before),\n after_mean = mean(after)\n )\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 2 x 3\n drug before_mean after_mean\n \n1 a 13.1 29.2\n2 b 18 28.1\n```\n:::\n:::\n\n\n \n\n\n* Mean \"after\" score slightly higher for treatment A.\n\n* Mean \"before\" score much higher for treatment B.\n\n* Greater *improvement* on treatment A. \n\n\n\n## Testing for interaction\n\n\n::: {.cell}\n\n```{.r .cell-code}\nprepost.1 <- lm(after ~ before * drug, data = prepost)\nanova(prepost.1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nAnalysis of Variance Table\n\nResponse: after\n Df Sum Sq Mean Sq F value Pr(>F) \nbefore 1 430.92 430.92 62.6894 6.34e-07 ***\ndrug 1 115.31 115.31 16.7743 0.0008442 ***\nbefore:drug 1 12.34 12.34 1.7948 0.1990662 \nResiduals 16 109.98 6.87 \n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n```\n:::\n:::\n\n\n \n\n\n* Interaction not significant. Will remove later.\n\n## Predictions\n\nSet up values to predict for:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(prepost)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n drug before after \n Length:20 Min. : 5.00 Min. :19.00 \n Class :character 1st Qu.: 9.75 1st Qu.:23.75 \n Mode :character Median :14.00 Median :29.00 \n Mean :15.55 Mean :28.65 \n 3rd Qu.:21.25 3rd Qu.:33.25 \n Max. :27.00 Max. :40.00 \n```\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nnew <- datagrid(before = c(9.75, 14, 21.25), \n drug = c(\"a\", \"b\"), model = prepost.1)\n```\n:::\n\n\n\n## and then\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(prepost.1, newdata = new)) %>% \n select(drug, before, estimate)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n drug before estimate\n1 a 9.75 25.93250\n2 b 9.75 22.14565\n3 a 14.00 30.07784\n4 b 14.00 25.21304\n5 a 21.25 37.14929\n6 b 21.25 30.44565\n```\n:::\n:::\n\n\n\\normalsize\n\n## Predictions (with interaction included), plotted\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_cap(model = prepost.1, condition = c(\"before\", \"drug\"))\n```\n\n::: {.cell-output-display}\n![](ancova_files/figure-beamer/unnamed-chunk-4-1.pdf)\n:::\n:::\n\n\n\nLines almost parallel, but not quite.\n\n\n\n## Taking out interaction\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nprepost.2 <- update(prepost.1, . ~ . - before:drug)\nanova(prepost.2)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nAnalysis of Variance Table\n\nResponse: after\n Df Sum Sq Mean Sq F value Pr(>F) \nbefore 1 430.92 430.92 59.890 5.718e-07 ***\ndrug 1 115.31 115.31 16.025 0.0009209 ***\nResiduals 17 122.32 7.20 \n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n```\n:::\n:::\n\n\n \n\\normalsize\n\n\n* Take out non-significant interaction.\n\n* `before` and `drug` strongly significant.\n\n* Do predictions again and plot them.\n\n## Predictions\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(prepost.2, newdata = new)) %>% \n select(drug, before, estimate)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n drug before estimate\n1 a 9.75 26.42794\n2 b 9.75 21.27328\n3 a 14.00 29.94473\n4 b 14.00 24.79007\n5 a 21.25 35.94397\n6 b 21.25 30.78931\n```\n:::\n:::\n\n\n\n\n## Plot of predicted values\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_cap(prepost.2, condition = c(\"before\", \"drug\"))\n```\n\n::: {.cell-output-display}\n![](ancova_files/figure-beamer/unnamed-chunk-6-1.pdf)\n:::\n:::\n\n\n\nThis time the lines are *exactly* parallel. No-interaction model forces them\nto have the same slope. \n\n\n\n## Different look at model output\n\n\n* `anova(prepost.2)` tests for significant effect of\nbefore score and of drug, but doesn't help with interpretation.\n\n* `summary(prepost.2)` views as regression with slopes:\n\n\\scriptsize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(prepost.2)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\nCall:\nlm(formula = after ~ before + drug, data = prepost)\n\nResiduals:\n Min 1Q Median 3Q Max \n-3.6348 -2.5099 -0.2038 1.8871 4.7453 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 18.3600 1.5115 12.147 8.35e-10 ***\nbefore 0.8275 0.0955 8.665 1.21e-07 ***\ndrugb -5.1547 1.2876 -4.003 0.000921 ***\n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 2.682 on 17 degrees of freedom\nMultiple R-squared: 0.817,\tAdjusted R-squared: 0.7955 \nF-statistic: 37.96 on 2 and 17 DF, p-value: 5.372e-07\n```\n:::\n:::\n\n\n\n\\normalsize \n\n\n\n\n## Understanding those slopes\n\n\\footnotesize\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntidy(prepost.2)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 3 x 5\n term estimate std.error statistic p.value\n \n1 (Intercept) 18.4 1.51 12.1 8.35e-10\n2 before 0.827 0.0955 8.66 1.21e- 7\n3 drugb -5.15 1.29 -4.00 9.21e- 4\n```\n:::\n:::\n\n\n\n\\normalsize\n\n\n\n* `before` ordinary numerical variable; `drug`\ncategorical. \n\n* `lm` uses first category `druga` as baseline.\n\n* Intercept is prediction of after score for before score 0 and\n*drug A*.\n\n* `before` slope is predicted change in after score when\nbefore score increases by 1 (usual slope)\n\n* Slope for `drugb` is *change* in predicted after\nscore for being on drug B rather than drug A. Same for *any*\nbefore score (no interaction).\n\n\n\n## Summary\n\n\n* ANCOVA model: fits different regression line for each group,\npredicting response from covariate.\n\n* ANCOVA model with interaction between factor and covariate\nallows different slopes for each line.\n\n* Sometimes those lines can cross over!\n\n* If interaction not significant, take out. Lines then parallel.\n\n* With parallel lines, groups have consistent effect regardless\nof value of covariate.\n\n", + "markdown": "---\ntitle: \"Analysis of Covariance\"\n---\n\n\n\n\n\n## Analysis of covariance\n\n\n* ANOVA: explanatory variables categorical (divide data into groups)\n\n* traditionally, analysis of covariance has categorical $x$'s plus one numerical $x$ (\"covariate\") to be adjusted for.\n\n* `lm` handles this too.\n\n* Simple example: two treatments (drugs) (`a` and `b`), with before and after scores. \n\n\n* Does knowing before score and/or treatment help to predict after score?\n\n* Is after score different by treatment/before score?\n\n\n\n\n\n## Data\n\nTreatment, before, after: \n\n\n\\scriptsize\n```\na 5 20\na 10 23\na 12 30\na 9 25\na 23 34\na 21 40\na 14 27\na 18 38\na 6 24\na 13 31\nb 7 19\nb 12 26\nb 27 33\nb 24 35\nb 18 30\nb 22 31\nb 26 34\nb 21 28\nb 14 23\nb 9 22\n```\n\\normalsize\n\n\n## Packages\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\nlibrary(broom)\nlibrary(marginaleffects)\n```\n:::\n\n\n\nthe last of these for predictions.\n\n## Read in data\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nurl <- \"http://ritsokiguess.site/datafiles/ancova.txt\"\nprepost <- read_delim(url, \" \")\nprepost\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 20 x 3\n drug before after\n \n 1 a 5 20\n 2 a 10 23\n 3 a 12 30\n 4 a 9 25\n 5 a 23 34\n 6 a 21 40\n 7 a 14 27\n 8 a 18 38\n 9 a 6 24\n10 a 13 31\n11 b 7 19\n12 b 12 26\n13 b 27 33\n14 b 24 35\n15 b 18 30\n16 b 22 31\n17 b 26 34\n18 b 21 28\n19 b 14 23\n20 b 9 22\n```\n:::\n:::\n\n\n\n\n\n## Making a plot\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(prepost, aes(x = before, y = after, colour = drug)) +\n geom_point()\n```\n\n::: {.cell-output-display}\n![](ancova_files/figure-beamer/ancova-plot-1.pdf)\n:::\n:::\n\n\n\n\n\n## Comments \n\n* As before score goes up, after score goes up.\n\n* Red points (drug A) generally above blue points (drug B), for\ncomparable before score.\n\n* Suggests before score effect *and* drug effect.\n\n\n## The means\n\n\n::: {.cell}\n\n```{.r .cell-code}\nprepost %>%\n group_by(drug) %>%\n summarize(\n before_mean = mean(before),\n after_mean = mean(after)\n )\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 2 x 3\n drug before_mean after_mean\n \n1 a 13.1 29.2\n2 b 18 28.1\n```\n:::\n:::\n\n\n \n\n\n* Mean \"after\" score slightly higher for treatment A.\n\n* Mean \"before\" score much higher for treatment B.\n\n* Greater *improvement* on treatment A. \n\n\n\n## Testing for interaction\n\n\n::: {.cell}\n\n```{.r .cell-code}\nprepost.1 <- lm(after ~ before * drug, data = prepost)\nanova(prepost.1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nAnalysis of Variance Table\n\nResponse: after\n Df Sum Sq Mean Sq F value Pr(>F) \nbefore 1 430.92 430.92 62.6894 6.34e-07 ***\ndrug 1 115.31 115.31 16.7743 0.0008442 ***\nbefore:drug 1 12.34 12.34 1.7948 0.1990662 \nResiduals 16 109.98 6.87 \n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n```\n:::\n:::\n\n\n \n\n\n* Interaction not significant. Will remove later.\n\n## Predictions\n\nSet up values to predict for:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(prepost)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n drug before after \n Length:20 Min. : 5.00 Min. :19.00 \n Class :character 1st Qu.: 9.75 1st Qu.:23.75 \n Mode :character Median :14.00 Median :29.00 \n Mean :15.55 Mean :28.65 \n 3rd Qu.:21.25 3rd Qu.:33.25 \n Max. :27.00 Max. :40.00 \n```\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nnew <- datagrid(before = c(9.75, 14, 21.25), \n drug = c(\"a\", \"b\"), model = prepost.1)\n```\n:::\n\n\n\n## and then\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(prepost.1, newdata = new)) %>% \n select(drug, before, estimate)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n drug before estimate\n1 a 9.75 25.93250\n2 b 9.75 22.14565\n3 a 14.00 30.07784\n4 b 14.00 25.21304\n5 a 21.25 37.14929\n6 b 21.25 30.44565\n```\n:::\n:::\n\n\n\\normalsize\n\n## Predictions (with interaction included), plotted\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_predictions(model = prepost.1, condition = c(\"before\", \"drug\"))\n```\n\n::: {.cell-output-display}\n![](ancova_files/figure-beamer/unnamed-chunk-4-1.pdf)\n:::\n:::\n\n\n\nLines almost parallel, but not quite.\n\n\n\n## Taking out interaction\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nprepost.2 <- update(prepost.1, . ~ . - before:drug)\nanova(prepost.2)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nAnalysis of Variance Table\n\nResponse: after\n Df Sum Sq Mean Sq F value Pr(>F) \nbefore 1 430.92 430.92 59.890 5.718e-07 ***\ndrug 1 115.31 115.31 16.025 0.0009209 ***\nResiduals 17 122.32 7.20 \n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n```\n:::\n:::\n\n\n \n\\normalsize\n\n\n* Take out non-significant interaction.\n\n* `before` and `drug` strongly significant.\n\n* Do predictions again and plot them.\n\n## Predictions\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(prepost.2, newdata = new)) %>% \n select(drug, before, estimate)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n drug before estimate\n1 a 9.75 26.42794\n2 b 9.75 21.27328\n3 a 14.00 29.94473\n4 b 14.00 24.79007\n5 a 21.25 35.94397\n6 b 21.25 30.78931\n```\n:::\n:::\n\n\n\n\n## Plot of predicted values\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_predictions(prepost.2, condition = c(\"before\", \"drug\"))\n```\n\n::: {.cell-output-display}\n![](ancova_files/figure-beamer/unnamed-chunk-6-1.pdf)\n:::\n:::\n\n\n\nThis time the lines are *exactly* parallel. No-interaction model forces them\nto have the same slope. \n\n\n\n## Different look at model output\n\n\n* `anova(prepost.2)` tests for significant effect of\nbefore score and of drug, but doesn't help with interpretation.\n\n* `summary(prepost.2)` views as regression with slopes:\n\n\\scriptsize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(prepost.2)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\nCall:\nlm(formula = after ~ before + drug, data = prepost)\n\nResiduals:\n Min 1Q Median 3Q Max \n-3.6348 -2.5099 -0.2038 1.8871 4.7453 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 18.3600 1.5115 12.147 8.35e-10 ***\nbefore 0.8275 0.0955 8.665 1.21e-07 ***\ndrugb -5.1547 1.2876 -4.003 0.000921 ***\n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 2.682 on 17 degrees of freedom\nMultiple R-squared: 0.817,\tAdjusted R-squared: 0.7955 \nF-statistic: 37.96 on 2 and 17 DF, p-value: 5.372e-07\n```\n:::\n:::\n\n\n\n\\normalsize \n\n\n\n\n## Understanding those slopes\n\n\\footnotesize\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntidy(prepost.2)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 3 x 5\n term estimate std.error statistic p.value\n \n1 (Intercept) 18.4 1.51 12.1 8.35e-10\n2 before 0.827 0.0955 8.66 1.21e- 7\n3 drugb -5.15 1.29 -4.00 9.21e- 4\n```\n:::\n:::\n\n\n\n\\normalsize\n\n\n\n* `before` ordinary numerical variable; `drug`\ncategorical. \n\n* `lm` uses first category `druga` as baseline.\n\n* Intercept is prediction of after score for before score 0 and\n*drug A*.\n\n* `before` slope is predicted change in after score when\nbefore score increases by 1 (usual slope)\n\n* Slope for `drugb` is *change* in predicted after\nscore for being on drug B rather than drug A. Same for *any*\nbefore score (no interaction).\n\n\n\n## Summary\n\n\n* ANCOVA model: fits different regression line for each group,\npredicting response from covariate.\n\n* ANCOVA model with interaction between factor and covariate\nallows different slopes for each line.\n\n* Sometimes those lines can cross over!\n\n* If interaction not significant, take out. 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diff --git a/_freeze/bootstrap_R/execute-results/html.json b/_freeze/bootstrap_R/execute-results/html.json index 89f90fb..f323c82 100644 --- a/_freeze/bootstrap_R/execute-results/html.json +++ b/_freeze/bootstrap_R/execute-results/html.json @@ -1,7 +1,7 @@ { - "hash": "b39d3bfbe34481136f1fea8772965337", + "hash": "7c19f4123ee146ced18cb67d9535aa9a", "result": { - "markdown": "---\ntitle: \"Bootstrap for sampling distribution of sample mean\"\n---\n\n\n\n## Assessing assumptions\n\n- Our $t$-tests assume normality of variable being tested\n- but, Central Limit Theorem says that normality matters less if sample is \"large\"\n- in practice \"approximate normality\" is enough, but how do we assess whether what we have is normal enough?\n- so far, use histogram/boxplot and make a call, allowing for sample size.\n\n## What actually has to be normal\n\n- is: **sampling distribution of sample mean**\n- the distribution of sample mean over *all possible samples*\n- but we only have *one* sample!\n- Idea: assume our sample is representative of the population, and draw samples from our sample (!), with replacement.\n- This gives an idea of what different samples from the population might look like.\n- Called *bootstrap*, after expression \"to pull yourself up by your own bootstraps\".\n\n## Packages\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\n```\n:::\n\n\n\n## Blue Jays attendances\n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\njays$attendance\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 48414 17264 15086 14433 21397 34743 44794 14184 15606 18581 19217 21519\n[13] 21312 30430 42917 42419 29306 15062 16402 19014 21195 33086 37929 15168\n[25] 17276\n```\n:::\n:::\n\n\n- A bootstrap sample:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ns <- sample(jays$attendance, replace = TRUE)\ns\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 21195 34743 21312 44794 16402 19014 34743 21195 17264 18581 19014 19217\n[13] 34743 19217 14433 15062 16402 15062 34743 15062 15086 15168 15086 48414\n[25] 30430\n```\n:::\n:::\n\n\n## Getting mean of bootstrap sample\n\n- A bootstrap sample is same size as original, but contains repeated values (eg. 15062) and missing ones (42917).\n- We need the mean of our bootstrap sample:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmean(s)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 23055.28\n```\n:::\n:::\n\n\n- This is a little different from the mean of our actual sample:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmean(jays$attendance)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 25070.16\n```\n:::\n:::\n\n\n- Want a sense of how the sample mean might vary, if we were able to take repeated samples from our population.\n- Idea: take lots of *bootstrap* samples, and see how *their* sample means vary.\n\n## Setting up bootstrap sampling\n\n- Begin by setting up a dataframe that contains a row for each bootstrap sample. I usually call this column `sim`. Do just 4 to get the idea:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:4)\n```\n\n::: {.cell-output-display}\n`````{=html}\n

\n`````\n:::\n:::\n\n\n## Drawing the bootstrap samples\n\n- Then set up to work one row at a time, and draw a bootstrap sample of the attendances in each row:\n\n\\small\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:4) %>% \n rowwise() %>% \n mutate(sample = list(sample(jays$attendance, replace = TRUE)))\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\\normalsize\n\n- Each row of our dataframe contains *all* of a bootstrap sample of 25 observations drawn with replacement from the attendances.\n\n\\normalsize\n\n## Sample means\n\n- Find the mean of each sample:\n\n\\footnotesize\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:4) %>% \n rowwise() %>% \n mutate(sample = list(sample(jays$attendance, replace = TRUE))) %>% \n mutate(my_mean = mean(sample))\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\\normalsize\n\n- These are (four simulated values of) the bootstrapped sampling distribution of the sample mean.\n\n## Make a histogram of them\n\n- rather pointless here, but to get the idea:\n\n\\footnotesize\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:4) %>% \n rowwise() %>% \n mutate(sample = list(sample(jays$attendance, replace = TRUE))) %>% \n mutate(my_mean = mean(sample)) %>% \n ggplot(aes(x = my_mean)) + geom_histogram(bins = 3) -> g\n```\n:::\n\n\\normalsize\n\n## The (pointless) histogram\n\n\n::: {.cell}\n\n```{.r .cell-code}\ng\n```\n\n::: {.cell-output-display}\n![](bootstrap_R_files/figure-revealjs/bootstrap-R-10-1.png){width=960}\n:::\n:::\n\n\n## Now do again with a decent number of bootstrap samples\n\n- say 1000, and put a decent number of bins on the histogram also:\n\n\\footnotesize\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:1000) %>% \n rowwise() %>% \n mutate(sample = list(sample(jays$attendance, replace = TRUE))) %>% \n mutate(my_mean = mean(sample)) %>% \n ggplot(aes(x = my_mean)) + geom_histogram(bins = 10) -> g\n```\n:::\n\n\\normalsize\n\n## The (better) histogram\n\n\n::: {.cell}\n\n```{.r .cell-code}\ng\n```\n\n::: {.cell-output-display}\n![](bootstrap_R_files/figure-revealjs/bootstrap-R-12-1.png){width=960}\n:::\n:::\n\n\n## Comments\n\n- This is very close to normal\n- The bootstrap says that the sampling distribution of the sample mean is close to normal, even though the distribution of the data is not\n- A sample size of 25 is big enough to overcome the skewness that we saw\n- This is the Central Limit Theorem in practice\n- It is surprisingly powerful.\n- Thus, the $t$-test is actually perfectly good here.\n\n## Comments on the code 1/2\n\n- You might have been wondering about this:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:4) %>% \n rowwise() %>% \n mutate(sample = list(sample(jays$attendance, replace = TRUE)))\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n## Comments on the code 2/2\n\n\n- how did we squeeze all 25 sample values into one cell?\n - sample is a so-called \"list-column\" that can contain anything.\n- why did we have to put `list()` around the `sample()`?\n - because `sample` produces a collection of numbers, not just a single one\n - the `list()` signals this: \"make a list-column of samples\".\n \n \n## Two samples\n\n- Assumption: *both* samples are from a normal distribution.\n- In practice, each sample is \"normal enough\" given its sample size, since Central Limit Theorem will help.\n- Use bootstrap on each group independently, as above.\n\n## Kids learning to read\n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(kids, aes(x=group, y=score)) + geom_boxplot()\n```\n\n::: {.cell-output-display}\n![](bootstrap_R_files/figure-revealjs/bootstrap-R-15-1.png){width=960}\n:::\n:::\n\n\n\n## Getting just the control group \n\n- Use `filter` to select rows where something is true:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nkids %>% filter(group==\"c\") -> controls\ncontrols\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n## Bootstrap these\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:1000) %>% \n rowwise() %>% \n mutate(sample = list(sample(controls$score, replace = TRUE))) %>% \n mutate(my_mean = mean(sample)) %>% \n ggplot(aes(x = my_mean)) + geom_histogram(bins = 10) \n```\n\n::: {.cell-output-display}\n![](bootstrap_R_files/figure-revealjs/bootstrap-R-17-1.png){width=960}\n:::\n:::\n\n\n## ... and the treatment group:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nkids %>% filter(group==\"t\") -> treats\ntibble(sim = 1:1000) %>% \n rowwise() %>% \n mutate(sample = list(sample(treats$score, replace = TRUE))) %>% \n mutate(my_mean = mean(sample)) %>% \n ggplot(aes(x = my_mean)) + geom_histogram(bins = 10) \n```\n\n::: {.cell-output-display}\n![](bootstrap_R_files/figure-revealjs/bootstrap-R-19-1.png){width=960}\n:::\n:::\n\n\n## Comments\n\n- sampling distributions of sample means both look pretty normal\n- as we thought, no problems with our two-sample $t$ at all.\n\n", + "markdown": "---\ntitle: \"Bootstrap for sampling distribution of sample mean\"\n---\n\n\n## Assessing assumptions\n\n- Our $t$-tests assume normality of variable being tested\n- but, Central Limit Theorem says that normality matters less if\n sample is \"large\"\n- in practice \"approximate normality\" is enough, but how do we assess\n whether what we have is normal enough?\n- so far, use histogram/boxplot and make a call, allowing for sample\n size.\n\n## What actually has to be normal\n\n- is: **sampling distribution of sample mean**\n- the distribution of sample mean over *all possible samples*\n- but we only have *one* sample!\n- Idea: assume our sample is representative of the population, and\n draw samples from our sample (!), with replacement.\n- This gives an idea of what different samples from the population\n might look like.\n- Called *bootstrap*, after expression \"to pull yourself up by your\n own bootstraps\".\n\n## Packages\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\n```\n:::\n\n\n## Blue Jays attendances\n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\njays$attendance\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 48414 17264 15086 14433 21397 34743 44794 14184 15606 18581 19217 21519\n[13] 21312 30430 42917 42419 29306 15062 16402 19014 21195 33086 37929 15168\n[25] 17276\n```\n:::\n:::\n\n\n- A bootstrap sample:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ns <- sample(jays$attendance, replace = TRUE)\ns\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 21195 34743 21312 44794 16402 19014 34743 21195 17264 18581 19014 19217\n[13] 34743 19217 14433 15062 16402 15062 34743 15062 15086 15168 15086 48414\n[25] 30430\n```\n:::\n:::\n\n\n- It is easier to see what is happening if we sort both the actual\n attendances and the bootstrap sample:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsort(jays$attendance)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 14184 14433 15062 15086 15168 15606 16402 17264 17276 18581 19014 19217\n[13] 21195 21312 21397 21519 29306 30430 33086 34743 37929 42419 42917 44794\n[25] 48414\n```\n:::\n\n```{.r .cell-code}\nsort(s)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 14433 15062 15062 15062 15086 15086 15168 16402 16402 17264 18581 19014\n[13] 19014 19217 19217 21195 21195 21312 30430 34743 34743 34743 34743 44794\n[25] 48414\n```\n:::\n:::\n\n\n## Getting mean of bootstrap sample\n\n- A bootstrap sample is same size as original, but contains repeated\n values (eg. 15062) and missing ones (42917).\n- We need the mean of our bootstrap sample:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmean(s)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 23055.28\n```\n:::\n:::\n\n\n- This is a little different from the mean of our actual sample:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmean(jays$attendance)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 25070.16\n```\n:::\n:::\n\n\n- Want a sense of how the sample mean might vary, if we were able to\n take repeated samples from our population.\n- Idea: take lots of *bootstrap* samples, and see how *their* sample\n means vary.\n\n## Setting up bootstrap sampling\n\n- Begin by setting up a dataframe that contains a row for each\n bootstrap sample. I usually call this column `sim`. Do just 4 to get\n the idea:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:4)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n## Drawing the bootstrap samples\n\n- Then set up to work one row at a time, and draw a bootstrap sample\n of the attendances in each row:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:4) %>% \n rowwise() %>% \n mutate(sample = list(sample(jays$attendance, replace = TRUE)))\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n- Each row of our dataframe contains *all* of a bootstrap sample of 25\n observations drawn with replacement from the attendances.\n\n## Sample means\n\n- Find the mean of each sample:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:4) %>% \n rowwise() %>% \n mutate(sample = list(sample(jays$attendance, replace = TRUE))) %>% \n mutate(my_mean = mean(sample))\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n- These are (four simulated values of) the bootstrapped sampling\n distribution of the sample mean.\n\n## Make a histogram of them\n\n- rather pointless here, but to get the idea:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:4) %>% \n rowwise() %>% \n mutate(sample = list(sample(jays$attendance, replace = TRUE))) %>% \n mutate(my_mean = mean(sample)) %>% \n ggplot(aes(x = my_mean)) + geom_histogram(bins = 3) -> g\n```\n:::\n\n\n## The (pointless) histogram\n\n\n::: {.cell}\n\n```{.r .cell-code}\ng\n```\n\n::: {.cell-output-display}\n![](bootstrap_R_files/figure-revealjs/bootstrap-R-10-1.png){width=960}\n:::\n:::\n\n\n## Now do again with a decent number of bootstrap samples\n\n- say 1000, and put a decent number of bins on the histogram also:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:1000) %>% \n rowwise() %>% \n mutate(sample = list(sample(jays$attendance, replace = TRUE))) %>% \n mutate(my_mean = mean(sample)) %>% \n ggplot(aes(x = my_mean)) + geom_histogram(bins = 10) -> g\n```\n:::\n\n\n## The (better) histogram\n\n\n::: {.cell}\n\n```{.r .cell-code}\ng\n```\n\n::: {.cell-output-display}\n![](bootstrap_R_files/figure-revealjs/bootstrap-R-12-1.png){width=960}\n:::\n:::\n\n\n## Comments\n\n- This is very close to normal\n- The bootstrap says that the sampling distribution of the sample mean\n is close to normal, even though the distribution of the data is not\n- A sample size of 25 is big enough to overcome the skewness that we\n saw\n- This is the Central Limit Theorem in practice\n- It is surprisingly powerful.\n- Thus, the $t$-test is actually perfectly good here.\n\n## Comments on the code 1/2\n\n- You might have been wondering about this:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:4) %>% \n rowwise() %>% \n mutate(sample = list(sample(jays$attendance, replace = TRUE)))\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n## Comments on the code 2/2\n\n- how did we squeeze all 25 sample values into one cell?\n - sample is a so-called \"list-column\" that can contain anything.\n- why did we have to put `list()` around the `sample()`?\n - because `sample` produces a collection of numbers, not just a\n single one\n - the `list()` signals this: \"make a list-column of samples\".\n\n## Two samples\n\n- Assumption: *both* samples are from a normal distribution.\n- In this case, each sample should be \"normal enough\" given its sample\n size, since Central Limit Theorem will help.\n- Use bootstrap on each group independently, as above.\n\n## Kids learning to read\n\n\n::: {.cell}\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(kids, aes(x=group, y=score)) + geom_boxplot()\n```\n\n::: {.cell-output-display}\n![](bootstrap_R_files/figure-revealjs/bootstrap-R-15-1.png){width=960}\n:::\n:::\n\n\n## Getting just the control group\n\n- Use `filter` to select rows where something is true:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nkids %>% filter(group==\"c\") -> controls\ncontrols\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n## Bootstrap these\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:1000) %>% \n rowwise() %>% \n mutate(sample = list(sample(controls$score, replace = TRUE))) %>% \n mutate(my_mean = mean(sample)) %>% \n ggplot(aes(x = my_mean)) + geom_histogram(bins = 10) \n```\n\n::: {.cell-output-display}\n![](bootstrap_R_files/figure-revealjs/bootstrap-R-17-1.png){width=960}\n:::\n:::\n\n\n## ... and the treatment group:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nkids %>% filter(group==\"t\") -> treats\ntibble(sim = 1:1000) %>% \n rowwise() %>% \n mutate(sample = list(sample(treats$score, replace = TRUE))) %>% \n mutate(my_mean = mean(sample)) %>% \n ggplot(aes(x = my_mean)) + geom_histogram(bins = 10) \n```\n\n::: {.cell-output-display}\n![](bootstrap_R_files/figure-revealjs/bootstrap-R-19-1.png){width=960}\n:::\n:::\n\n\n## Comments\n\n- sampling distributions of sample means both look pretty normal\n- as we thought, no problems with our two-sample $t$ at all.\n", "supporting": [ "bootstrap_R_files/figure-revealjs" ], diff --git a/_freeze/bootstrap_R/execute-results/tex.json b/_freeze/bootstrap_R/execute-results/tex.json index efd6dd4..d9ae06d 100644 --- a/_freeze/bootstrap_R/execute-results/tex.json +++ b/_freeze/bootstrap_R/execute-results/tex.json @@ -1,7 +1,7 @@ { - "hash": "b39d3bfbe34481136f1fea8772965337", + "hash": "7c19f4123ee146ced18cb67d9535aa9a", "result": { - "markdown": "---\ntitle: \"Bootstrap for sampling distribution of sample mean\"\n---\n\n\n\n\n## Assessing assumptions\n\n- Our $t$-tests assume normality of variable being tested\n- but, Central Limit Theorem says that normality matters less if sample is \"large\"\n- in practice \"approximate normality\" is enough, but how do we assess whether what we have is normal enough?\n- so far, use histogram/boxplot and make a call, allowing for sample size.\n\n## What actually has to be normal\n\n- is: **sampling distribution of sample mean**\n- the distribution of sample mean over *all possible samples*\n- but we only have *one* sample!\n- Idea: assume our sample is representative of the population, and draw samples from our sample (!), with replacement.\n- This gives an idea of what different samples from the population might look like.\n- Called *bootstrap*, after expression \"to pull yourself up by your own bootstraps\".\n\n## Packages\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\n```\n:::\n\n\n\n\n## Blue Jays attendances\n\n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\njays$attendance\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 48414 17264 15086 14433 21397 34743 44794 14184 15606 18581 19217 21519\n[13] 21312 30430 42917 42419 29306 15062 16402 19014 21195 33086 37929 15168\n[25] 17276\n```\n:::\n:::\n\n\n\n- A bootstrap sample:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ns <- sample(jays$attendance, replace = TRUE)\ns\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 21195 34743 21312 44794 16402 19014 34743 21195 17264 18581 19014 19217\n[13] 34743 19217 14433 15062 16402 15062 34743 15062 15086 15168 15086 48414\n[25] 30430\n```\n:::\n:::\n\n\n\n## Getting mean of bootstrap sample\n\n- A bootstrap sample is same size as original, but contains repeated values (eg. 15062) and missing ones (42917).\n- We need the mean of our bootstrap sample:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmean(s)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 23055.28\n```\n:::\n:::\n\n\n\n- This is a little different from the mean of our actual sample:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmean(jays$attendance)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 25070.16\n```\n:::\n:::\n\n\n\n- Want a sense of how the sample mean might vary, if we were able to take repeated samples from our population.\n- Idea: take lots of *bootstrap* samples, and see how *their* sample means vary.\n\n## Setting up bootstrap sampling\n\n- Begin by setting up a dataframe that contains a row for each bootstrap sample. I usually call this column `sim`. Do just 4 to get the idea:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:4)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 4 x 1\n sim\n \n1 1\n2 2\n3 3\n4 4\n```\n:::\n:::\n\n\n\n## Drawing the bootstrap samples\n\n- Then set up to work one row at a time, and draw a bootstrap sample of the attendances in each row:\n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:4) %>% \n rowwise() %>% \n mutate(sample = list(sample(jays$attendance, replace = TRUE)))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 4 x 2\n# Rowwise: \n sim sample \n \n1 1 \n2 2 \n3 3 \n4 4 \n```\n:::\n:::\n\n\n\\normalsize\n\n- Each row of our dataframe contains *all* of a bootstrap sample of 25 observations drawn with replacement from the attendances.\n\n\\normalsize\n\n## Sample means\n\n- Find the mean of each sample:\n\n\\footnotesize\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:4) %>% \n rowwise() %>% \n mutate(sample = list(sample(jays$attendance, replace = TRUE))) %>% \n mutate(my_mean = mean(sample))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 4 x 3\n# Rowwise: \n sim sample my_mean\n \n1 1 28472.\n2 2 28648.\n3 3 23329.\n4 4 24808.\n```\n:::\n:::\n\n\n\\normalsize\n\n- These are (four simulated values of) the bootstrapped sampling distribution of the sample mean.\n\n## Make a histogram of them\n\n- rather pointless here, but to get the idea:\n\n\\footnotesize\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:4) %>% \n rowwise() %>% \n mutate(sample = list(sample(jays$attendance, replace = TRUE))) %>% \n mutate(my_mean = mean(sample)) %>% \n ggplot(aes(x = my_mean)) + geom_histogram(bins = 3) -> g\n```\n:::\n\n\n\\normalsize\n\n## The (pointless) histogram\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ng\n```\n\n::: {.cell-output-display}\n![](bootstrap_R_files/figure-beamer/bootstrap-R-10-1.pdf)\n:::\n:::\n\n\n\n## Now do again with a decent number of bootstrap samples\n\n- say 1000, and put a decent number of bins on the histogram also:\n\n\\footnotesize\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:1000) %>% \n rowwise() %>% \n mutate(sample = list(sample(jays$attendance, replace = TRUE))) %>% \n mutate(my_mean = mean(sample)) %>% \n ggplot(aes(x = my_mean)) + geom_histogram(bins = 10) -> g\n```\n:::\n\n\n\\normalsize\n\n## The (better) histogram\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ng\n```\n\n::: {.cell-output-display}\n![](bootstrap_R_files/figure-beamer/bootstrap-R-12-1.pdf)\n:::\n:::\n\n\n\n## Comments\n\n- This is very close to normal\n- The bootstrap says that the sampling distribution of the sample mean is close to normal, even though the distribution of the data is not\n- A sample size of 25 is big enough to overcome the skewness that we saw\n- This is the Central Limit Theorem in practice\n- It is surprisingly powerful.\n- Thus, the $t$-test is actually perfectly good here.\n\n## Comments on the code 1/2\n\n- You might have been wondering about this:\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:4) %>% \n rowwise() %>% \n mutate(sample = list(sample(jays$attendance, replace = TRUE)))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 4 x 2\n# Rowwise: \n sim sample \n \n1 1 \n2 2 \n3 3 \n4 4 \n```\n:::\n:::\n\n\n\n## Comments on the code 2/2\n\n\n- how did we squeeze all 25 sample values into one cell?\n - sample is a so-called \"list-column\" that can contain anything.\n- why did we have to put `list()` around the `sample()`?\n - because `sample` produces a collection of numbers, not just a single one\n - the `list()` signals this: \"make a list-column of samples\".\n \n \n## Two samples\n\n- Assumption: *both* samples are from a normal distribution.\n- In practice, each sample is \"normal enough\" given its sample size, since Central Limit Theorem will help.\n- Use bootstrap on each group independently, as above.\n\n## Kids learning to read\n\n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(kids, aes(x=group, y=score)) + geom_boxplot()\n```\n\n::: {.cell-output-display}\n![](bootstrap_R_files/figure-beamer/bootstrap-R-15-1.pdf)\n:::\n:::\n\n\n\n\n## Getting just the control group \n\n- Use `filter` to select rows where something is true:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nkids %>% filter(group==\"c\") -> controls\ncontrols\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 23 x 2\n group score\n \n 1 c 42\n 2 c 33\n 3 c 46\n 4 c 37\n 5 c 43\n 6 c 41\n 7 c 10\n 8 c 42\n 9 c 55\n10 c 19\n# i 13 more rows\n```\n:::\n:::\n\n\n\n## Bootstrap these\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:1000) %>% \n rowwise() %>% \n mutate(sample = list(sample(controls$score, replace = TRUE))) %>% \n mutate(my_mean = mean(sample)) %>% \n ggplot(aes(x = my_mean)) + geom_histogram(bins = 10) \n```\n\n::: {.cell-output-display}\n![](bootstrap_R_files/figure-beamer/bootstrap-R-17-1.pdf)\n:::\n:::\n\n\n\n## ... and the treatment group:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nkids %>% filter(group==\"t\") -> treats\ntibble(sim = 1:1000) %>% \n rowwise() %>% \n mutate(sample = list(sample(treats$score, replace = TRUE))) %>% \n mutate(my_mean = mean(sample)) %>% \n ggplot(aes(x = my_mean)) + geom_histogram(bins = 10) \n```\n\n::: {.cell-output-display}\n![](bootstrap_R_files/figure-beamer/bootstrap-R-19-1.pdf)\n:::\n:::\n\n\n\n## Comments\n\n- sampling distributions of sample means both look pretty normal\n- as we thought, no problems with our two-sample $t$ at all.\n\n", + "markdown": "---\ntitle: \"Bootstrap for sampling distribution of sample mean\"\n---\n\n\n\n## Assessing assumptions\n\n- Our $t$-tests assume normality of variable being tested\n- but, Central Limit Theorem says that normality matters less if\n sample is \"large\"\n- in practice \"approximate normality\" is enough, but how do we assess\n whether what we have is normal enough?\n- so far, use histogram/boxplot and make a call, allowing for sample\n size.\n\n## What actually has to be normal\n\n- is: **sampling distribution of sample mean**\n- the distribution of sample mean over *all possible samples*\n- but we only have *one* sample!\n- Idea: assume our sample is representative of the population, and\n draw samples from our sample (!), with replacement.\n- This gives an idea of what different samples from the population\n might look like.\n- Called *bootstrap*, after expression \"to pull yourself up by your\n own bootstraps\".\n\n## Packages\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\n```\n:::\n\n\n\n## Blue Jays attendances\n\n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\njays$attendance\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 48414 17264 15086 14433 21397 34743 44794 14184 15606 18581 19217 21519\n[13] 21312 30430 42917 42419 29306 15062 16402 19014 21195 33086 37929 15168\n[25] 17276\n```\n:::\n:::\n\n\n\n- A bootstrap sample:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ns <- sample(jays$attendance, replace = TRUE)\ns\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 21195 34743 21312 44794 16402 19014 34743 21195 17264 18581 19014 19217\n[13] 34743 19217 14433 15062 16402 15062 34743 15062 15086 15168 15086 48414\n[25] 30430\n```\n:::\n:::\n\n\n\n- It is easier to see what is happening if we sort both the actual\n attendances and the bootstrap sample:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsort(jays$attendance)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 14184 14433 15062 15086 15168 15606 16402 17264 17276 18581 19014 19217\n[13] 21195 21312 21397 21519 29306 30430 33086 34743 37929 42419 42917 44794\n[25] 48414\n```\n:::\n\n```{.r .cell-code}\nsort(s)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 14433 15062 15062 15062 15086 15086 15168 16402 16402 17264 18581 19014\n[13] 19014 19217 19217 21195 21195 21312 30430 34743 34743 34743 34743 44794\n[25] 48414\n```\n:::\n:::\n\n\n\n## Getting mean of bootstrap sample\n\n- A bootstrap sample is same size as original, but contains repeated\n values (eg. 15062) and missing ones (42917).\n- We need the mean of our bootstrap sample:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmean(s)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 23055.28\n```\n:::\n:::\n\n\n\n- This is a little different from the mean of our actual sample:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmean(jays$attendance)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 25070.16\n```\n:::\n:::\n\n\n\n- Want a sense of how the sample mean might vary, if we were able to\n take repeated samples from our population.\n- Idea: take lots of *bootstrap* samples, and see how *their* sample\n means vary.\n\n## Setting up bootstrap sampling\n\n- Begin by setting up a dataframe that contains a row for each\n bootstrap sample. I usually call this column `sim`. Do just 4 to get\n the idea:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:4)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 4 x 1\n sim\n \n1 1\n2 2\n3 3\n4 4\n```\n:::\n:::\n\n\n\n## Drawing the bootstrap samples\n\n- Then set up to work one row at a time, and draw a bootstrap sample\n of the attendances in each row:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:4) %>% \n rowwise() %>% \n mutate(sample = list(sample(jays$attendance, replace = TRUE)))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 4 x 2\n# Rowwise: \n sim sample \n \n1 1 \n2 2 \n3 3 \n4 4 \n```\n:::\n:::\n\n\n\n- Each row of our dataframe contains *all* of a bootstrap sample of 25\n observations drawn with replacement from the attendances.\n\n## Sample means\n\n- Find the mean of each sample:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:4) %>% \n rowwise() %>% \n mutate(sample = list(sample(jays$attendance, replace = TRUE))) %>% \n mutate(my_mean = mean(sample))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 4 x 3\n# Rowwise: \n sim sample my_mean\n \n1 1 28472.\n2 2 28648.\n3 3 23329.\n4 4 24808.\n```\n:::\n:::\n\n\n\n- These are (four simulated values of) the bootstrapped sampling\n distribution of the sample mean.\n\n## Make a histogram of them\n\n- rather pointless here, but to get the idea:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:4) %>% \n rowwise() %>% \n mutate(sample = list(sample(jays$attendance, replace = TRUE))) %>% \n mutate(my_mean = mean(sample)) %>% \n ggplot(aes(x = my_mean)) + geom_histogram(bins = 3) -> g\n```\n:::\n\n\n\n## The (pointless) histogram\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ng\n```\n\n::: {.cell-output-display}\n![](bootstrap_R_files/figure-beamer/bootstrap-R-10-1.pdf)\n:::\n:::\n\n\n\n## Now do again with a decent number of bootstrap samples\n\n- say 1000, and put a decent number of bins on the histogram also:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:1000) %>% \n rowwise() %>% \n mutate(sample = list(sample(jays$attendance, replace = TRUE))) %>% \n mutate(my_mean = mean(sample)) %>% \n ggplot(aes(x = my_mean)) + geom_histogram(bins = 10) -> g\n```\n:::\n\n\n\n## The (better) histogram\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ng\n```\n\n::: {.cell-output-display}\n![](bootstrap_R_files/figure-beamer/bootstrap-R-12-1.pdf)\n:::\n:::\n\n\n\n## Comments\n\n- This is very close to normal\n- The bootstrap says that the sampling distribution of the sample mean\n is close to normal, even though the distribution of the data is not\n- A sample size of 25 is big enough to overcome the skewness that we\n saw\n- This is the Central Limit Theorem in practice\n- It is surprisingly powerful.\n- Thus, the $t$-test is actually perfectly good here.\n\n## Comments on the code 1/2\n\n- You might have been wondering about this:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:4) %>% \n rowwise() %>% \n mutate(sample = list(sample(jays$attendance, replace = TRUE)))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 4 x 2\n# Rowwise: \n sim sample \n \n1 1 \n2 2 \n3 3 \n4 4 \n```\n:::\n:::\n\n\n\n## Comments on the code 2/2\n\n- how did we squeeze all 25 sample values into one cell?\n - sample is a so-called \"list-column\" that can contain anything.\n- why did we have to put `list()` around the `sample()`?\n - because `sample` produces a collection of numbers, not just a\n single one\n - the `list()` signals this: \"make a list-column of samples\".\n\n## Two samples\n\n- Assumption: *both* samples are from a normal distribution.\n- In this case, each sample should be \"normal enough\" given its sample\n size, since Central Limit Theorem will help.\n- Use bootstrap on each group independently, as above.\n\n## Kids learning to read\n\n\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 44 x 2\n group score\n \n 1 t 24\n 2 t 61\n 3 t 59\n 4 t 46\n 5 t 43\n 6 t 44\n 7 t 52\n 8 t 43\n 9 t 58\n10 t 67\n# i 34 more rows\n```\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(kids, aes(x=group, y=score)) + geom_boxplot()\n```\n\n::: {.cell-output-display}\n![](bootstrap_R_files/figure-beamer/bootstrap-R-15-1.pdf)\n:::\n:::\n\n\n\n## Getting just the control group\n\n- Use `filter` to select rows where something is true:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nkids %>% filter(group==\"c\") -> controls\ncontrols\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 23 x 2\n group score\n \n 1 c 42\n 2 c 33\n 3 c 46\n 4 c 37\n 5 c 43\n 6 c 41\n 7 c 10\n 8 c 42\n 9 c 55\n10 c 19\n# i 13 more rows\n```\n:::\n:::\n\n\n\n## Bootstrap these\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntibble(sim = 1:1000) %>% \n rowwise() %>% \n mutate(sample = list(sample(controls$score, replace = TRUE))) %>% \n mutate(my_mean = mean(sample)) %>% \n ggplot(aes(x = my_mean)) + geom_histogram(bins = 10) \n```\n\n::: {.cell-output-display}\n![](bootstrap_R_files/figure-beamer/bootstrap-R-17-1.pdf)\n:::\n:::\n\n\n\n## ... and the treatment group:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nkids %>% filter(group==\"t\") -> treats\ntibble(sim = 1:1000) %>% \n rowwise() %>% \n mutate(sample = list(sample(treats$score, replace = TRUE))) %>% \n mutate(my_mean = mean(sample)) %>% \n ggplot(aes(x = my_mean)) + geom_histogram(bins = 10) \n```\n\n::: {.cell-output-display}\n![](bootstrap_R_files/figure-beamer/bootstrap-R-19-1.pdf)\n:::\n:::\n\n\n\n## Comments\n\n- sampling distributions of sample means both look pretty normal\n- as we thought, no problems with our two-sample $t$ at all.\n", "supporting": [ "bootstrap_R_files/figure-beamer" ], diff --git a/_freeze/bootstrap_R/figure-beamer/bootstrap-R-10-1.pdf b/_freeze/bootstrap_R/figure-beamer/bootstrap-R-10-1.pdf index 4ad461baad7d1f12dd3453fbb663985969231396..2f877c4e4d02d403879901916d43e6c59aa9a676 100644 GIT binary patch delta 257 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zFd9wf6;kCxlGHUYnd~4WiY}Ba6lQLupb?T9q2QBQmZ|_W(MUffwM^e7H7PSOPg5bg zAh9H)I5knBEVZaOGe1wkOwU-)1f*p1Lm@xLI8zrl3nw!dQzJuTOD9Vc6AM=(BO_N+ sM>j_c0|N_3Gdl$vf=Xf)?CiLTOA?DpDvDCmxJ)d}47gNPUH#p-0Mt4{p#T5? diff --git a/_freeze/choosing/execute-results/tex.json b/_freeze/choosing/execute-results/tex.json index b6df1ec..d682186 100644 --- a/_freeze/choosing/execute-results/tex.json +++ b/_freeze/choosing/execute-results/tex.json @@ -1,7 +1,7 @@ { - "hash": "1a27c83e2f5ea4aee31a32c65b194163", + "hash": "8a1b88b16954fb13b78c31ee298ba634", "result": { - "markdown": "---\ntitle: \"Choosing things in dataframes\"\n---\n\n\n\n## Packages\n\nThe usual:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\n```\n:::\n\n\n\n\n## Doing things with data frames\nLet’s go back to our Australian athletes: \n\n\n\n::: {.cell}\n\n:::\n\n\n\n\\scriptsize\n\n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM\n \n 1 female Netba~ 4.56 13.3 42.2 13.6 20 19.2 49 11.3 53.1\n 2 female Netba~ 4.15 6 38 12.7 59 21.2 110. 25.3 47.1\n 3 female Netba~ 4.16 7.6 37.5 12.3 22 21.4 89 19.4 53.4\n 4 female Netba~ 4.32 6.4 37.7 12.3 30 21.0 98.3 19.6 48.8\n 5 female Netba~ 4.06 5.8 38.7 12.8 78 21.8 122. 23.1 56.0\n 6 female Netba~ 4.12 6.1 36.6 11.8 21 21.4 90.4 16.9 56.4\n 7 female Netba~ 4.17 5 37.4 12.7 109 21.5 107. 21.3 53.1\n 8 female Netba~ 3.8 6.6 36.5 12.4 102 24.4 157. 26.6 54.4\n 9 female Netba~ 3.96 5.5 36.3 12.4 71 22.6 101. 17.9 56.0\n10 female Netba~ 4.44 9.7 41.4 14.1 64 22.8 126. 25.0 51.6\n# i 192 more rows\n# i 2 more variables: Ht , Wt \n```\n:::\n:::\n\n\n\\normalsize\n\n## Choosing a column\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(Sport)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 1\n Sport \n \n 1 Netball\n 2 Netball\n 3 Netball\n 4 Netball\n 5 Netball\n 6 Netball\n 7 Netball\n 8 Netball\n 9 Netball\n10 Netball\n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Choosing several columns\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(Sport, Hg, BMI)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 3\n Sport Hg BMI\n \n 1 Netball 13.6 19.2\n 2 Netball 12.7 21.2\n 3 Netball 12.3 21.4\n 4 Netball 12.3 21.0\n 5 Netball 12.8 21.8\n 6 Netball 11.8 21.4\n 7 Netball 12.7 21.5\n 8 Netball 12.4 24.4\n 9 Netball 12.4 22.6\n10 Netball 14.1 22.8\n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Choosing consecutive columns\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(Sex:WCC)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 4\n Sex Sport RCC WCC\n \n 1 female Netball 4.56 13.3\n 2 female Netball 4.15 6 \n 3 female Netball 4.16 7.6\n 4 female Netball 4.32 6.4\n 5 female Netball 4.06 5.8\n 6 female Netball 4.12 6.1\n 7 female Netball 4.17 5 \n 8 female Netball 3.8 6.6\n 9 female Netball 3.96 5.5\n10 female Netball 4.44 9.7\n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Choosing all-but some columns\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(-(RCC:LBM))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 4\n Sex Sport Ht Wt\n \n 1 female Netball 177. 59.9\n 2 female Netball 173. 63 \n 3 female Netball 176 66.3\n 4 female Netball 170. 60.7\n 5 female Netball 183 72.9\n 6 female Netball 178. 67.9\n 7 female Netball 177. 67.5\n 8 female Netball 174. 74.1\n 9 female Netball 174. 68.2\n10 female Netball 174. 68.8\n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Select-helpers\nOther ways to select columns: those whose name:\n\n- `starts_with` something\n- `ends_with` something\n- `contains` something\n- `matches` a “regular expression”\n- `everything()` select all the columns\n\n## Columns whose names begin with S \n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(starts_with(\"S\"))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 3\n Sex Sport SSF\n \n 1 female Netball 49 \n 2 female Netball 110. \n 3 female Netball 89 \n 4 female Netball 98.3\n 5 female Netball 122. \n 6 female Netball 90.4\n 7 female Netball 107. \n 8 female Netball 157. \n 9 female Netball 101. \n10 female Netball 126. \n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Columns whose names end with C\n\neither uppercase or lowercase:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(ends_with(\"c\"))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 3\n RCC WCC Hc\n \n 1 4.56 13.3 42.2\n 2 4.15 6 38 \n 3 4.16 7.6 37.5\n 4 4.32 6.4 37.7\n 5 4.06 5.8 38.7\n 6 4.12 6.1 36.6\n 7 4.17 5 37.4\n 8 3.8 6.6 36.5\n 9 3.96 5.5 36.3\n10 4.44 9.7 41.4\n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Case-sensitive\n\nThis works with any of the select-helpers:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(ends_with(\"C\", ignore.case=FALSE))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 2\n RCC WCC\n \n 1 4.56 13.3\n 2 4.15 6 \n 3 4.16 7.6\n 4 4.32 6.4\n 5 4.06 5.8\n 6 4.12 6.1\n 7 4.17 5 \n 8 3.8 6.6\n 9 3.96 5.5\n10 4.44 9.7\n# i 192 more rows\n```\n:::\n:::\n\n\n\n\n## Column names containing letter R\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(contains(\"r\"))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 3\n Sport RCC Ferr\n \n 1 Netball 4.56 20\n 2 Netball 4.15 59\n 3 Netball 4.16 22\n 4 Netball 4.32 30\n 5 Netball 4.06 78\n 6 Netball 4.12 21\n 7 Netball 4.17 109\n 8 Netball 3.8 102\n 9 Netball 3.96 71\n10 Netball 4.44 64\n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Exactly two characters, ending with T\n\nIn regular expression terms, this is `^.t$`:\n\n- `^` means “start of text”\n- `.` means “exactly one character, but could be anything”\n- `$` means “end of text”.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(matches(\"^.t$\"))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 2\n Ht Wt\n \n 1 177. 59.9\n 2 173. 63 \n 3 176 66.3\n 4 170. 60.7\n 5 183 72.9\n 6 178. 67.9\n 7 177. 67.5\n 8 174. 74.1\n 9 174. 68.2\n10 174. 68.8\n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Choosing columns by property\n\n- Use `where` as with summarizing several columns\n- eg, to choose text columns:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(where(is.character))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 2\n Sex Sport \n \n 1 female Netball\n 2 female Netball\n 3 female Netball\n 4 female Netball\n 5 female Netball\n 6 female Netball\n 7 female Netball\n 8 female Netball\n 9 female Netball\n10 female Netball\n# i 192 more rows\n```\n:::\n:::\n\n\n\n\n## Choosing rows by number \n\n\\tiny\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% slice(16:25)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 10 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM\n \n 1 female Netba~ 4.25 10.7 39.5 13.2 127 24.5 157. 26.5 54.5\n 2 female Netba~ 4.46 10.9 39.7 13.7 102 24.0 116. 23.0 57.2\n 3 female Netba~ 4.4 9.3 40.4 13.6 86 26.2 182. 30.1 54.4\n 4 female Netba~ 4.83 8.4 41.8 13.4 40 20.0 71.6 13.9 57.6\n 5 female Netba~ 4.23 6.9 38.3 12.6 50 25.7 144. 26.6 61.5\n 6 female Netba~ 4.24 8.4 37.6 12.5 58 25.6 201. 35.5 53.5\n 7 female Netba~ 3.95 6.6 38.4 12.8 33 19.9 68.9 15.6 54.1\n 8 female Netba~ 4.03 8.5 37.7 13 51 23.4 104. 19.6 55.4\n 9 female BBall 3.96 7.5 37.5 12.3 60 20.6 109. 19.8 63.3\n10 female BBall 4.41 8.3 38.2 12.7 68 20.7 103. 21.3 58.6\n# i 2 more variables: Ht , Wt \n```\n:::\n:::\n\n\n\\normalsize\n\n\n\n## Non-consecutive rows \n\n\\tiny\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% \n slice(10, 13, 17, 42)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 4 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM\n \n1 female Netball 4.44 9.7 41.4 14.1 64 22.8 126. 25.0 51.6\n2 female Netball 4.02 9.1 37.7 12.7 107 23.0 77 18.1 57.3\n3 female Netball 4.46 10.9 39.7 13.7 102 24.0 116. 23.0 57.2\n4 female Row 4.37 8.1 41.8 14.3 53 23.5 98 21.8 63.0\n# i 2 more variables: Ht , Wt \n```\n:::\n:::\n\n\n\n\\normalsize\n\n## A random sample of rows\n\n\\tiny\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% slice_sample(n=8)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 8 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM\n \n1 female Row 4.87 6.4 44.8 15 64 20.2 99.8 20.1 52.7\n2 male Tennis 5.66 8.3 50.2 17.7 38 23.8 56.5 10.0 72 \n3 male T400m 4.55 5.55 42.6 14.4 106 21.2 34.1 6.06 57 \n4 female BBall 4.35 7.8 41.4 14.1 30 22.0 118. 23.3 48.3\n5 male Row 5.22 6 46.6 15.7 72 25.1 43.1 7.49 83 \n6 male WPolo 4.63 14.3 44.8 15 133 25.4 49.5 8.97 79 \n7 male WPolo 4.91 10.2 45 15.2 234 23.7 56.5 10.1 68 \n8 male Row 5.22 8.4 47.5 16.2 89 25.3 44.5 9.36 79 \n# i 2 more variables: Ht , Wt \n```\n:::\n:::\n\n\n\\normalsize\n\n## Rows for which something is true\n\n\\tiny\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% filter(Sport == \"Tennis\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 11 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM\n \n 1 female Tennis 4 4.2 36.6 12 57 25.4 109 20.9 56.6\n 2 female Tennis 4.4 4 40.8 13.9 73 22.1 98.1 19.6 56.0\n 3 female Tennis 4.38 7.9 39.8 13.5 88 21.2 80.6 17.1 46.5\n 4 female Tennis 4.08 6.6 37.8 12.1 182 20.5 68.3 15.3 51.8\n 5 female Tennis 4.98 6.4 44.8 14.8 80 17.1 47.6 11.1 42.2\n 6 female Tennis 5.16 7.2 44.3 14.5 88 18.3 61.9 12.9 48.8\n 7 female Tennis 4.66 6.4 40.9 13.9 109 18.4 38.2 8.45 41.9\n 8 male Tennis 5.66 8.3 50.2 17.7 38 23.8 56.5 10.0 72 \n 9 male Tennis 5.03 6.4 42.7 14.3 122 22.0 47.6 8.51 68 \n10 male Tennis 4.97 8.8 43 14.9 233 22.3 60.4 11.5 63 \n11 male Tennis 5.38 6.3 46 15.7 32 21.1 34.9 6.26 72 \n# i 2 more variables: Ht , Wt \n```\n:::\n:::\n\n\n\\normalsize\n\n## More complicated selections\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% filter(Sport == \"Tennis\", RCC < 5)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 7 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM\n \n1 female Tennis 4 4.2 36.6 12 57 25.4 109 20.9 56.6\n2 female Tennis 4.4 4 40.8 13.9 73 22.1 98.1 19.6 56.0\n3 female Tennis 4.38 7.9 39.8 13.5 88 21.2 80.6 17.1 46.5\n4 female Tennis 4.08 6.6 37.8 12.1 182 20.5 68.3 15.3 51.8\n5 female Tennis 4.98 6.4 44.8 14.8 80 17.1 47.6 11.1 42.2\n6 female Tennis 4.66 6.4 40.9 13.9 109 18.4 38.2 8.45 41.9\n7 male Tennis 4.97 8.8 43 14.9 233 22.3 60.4 11.5 63 \n# i 2 more variables: Ht , Wt \n```\n:::\n:::\n\n\n\n## Another way to do \"and\"\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% filter(Sport == \"Tennis\") %>% \n filter(RCC < 5)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 7 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM\n \n1 female Tennis 4 4.2 36.6 12 57 25.4 109 20.9 56.6\n2 female Tennis 4.4 4 40.8 13.9 73 22.1 98.1 19.6 56.0\n3 female Tennis 4.38 7.9 39.8 13.5 88 21.2 80.6 17.1 46.5\n4 female Tennis 4.08 6.6 37.8 12.1 182 20.5 68.3 15.3 51.8\n5 female Tennis 4.98 6.4 44.8 14.8 80 17.1 47.6 11.1 42.2\n6 female Tennis 4.66 6.4 40.9 13.9 109 18.4 38.2 8.45 41.9\n7 male Tennis 4.97 8.8 43 14.9 233 22.3 60.4 11.5 63 \n# i 2 more variables: Ht , Wt \n```\n:::\n:::\n\n\n\n\n## Either/Or\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% filter(Sport == \"Tennis\" | RCC > 5)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 66 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM\n \n 1 female Row 5.02 6.4 44.8 15.2 48 19.8 91 19.2 53.6\n 2 female T400m 5.31 9.5 47.1 15.9 29 21.4 57.9 11.1 57.5\n 3 female Field 5.33 9.3 47 15 62 25.3 103. 19.5 59.9\n 4 female TSprnt 5.16 8.2 45.3 14.7 34 20.3 46.1 10.2 51.5\n 5 female Tennis 4 4.2 36.6 12 57 25.4 109 20.9 56.6\n 6 female Tennis 4.4 4 40.8 13.9 73 22.1 98.1 19.6 56.0\n 7 female Tennis 4.38 7.9 39.8 13.5 88 21.2 80.6 17.1 46.5\n 8 female Tennis 4.08 6.6 37.8 12.1 182 20.5 68.3 15.3 51.8\n 9 female Tennis 4.98 6.4 44.8 14.8 80 17.1 47.6 11.1 42.2\n10 female Tennis 5.16 7.2 44.3 14.5 88 18.3 61.9 12.9 48.8\n# i 56 more rows\n# i 2 more variables: Ht , Wt \n```\n:::\n:::\n\n\n\n## Sorting into order\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% arrange(RCC)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM\n \n 1 female Netba~ 3.8 6.6 36.5 12.4 102 24.4 157. 26.6 54.4\n 2 female Netba~ 3.9 6.3 35.9 12.1 78 20.1 70 15.0 57.3\n 3 female T400m 3.9 6 38.9 13.5 16 19.4 48.4 10.5 53.7\n 4 female Row 3.91 7.3 37.6 12.9 43 22.3 126. 25.2 54.8\n 5 female Netba~ 3.95 6.6 38.4 12.8 33 19.9 68.9 15.6 54.1\n 6 female Row 3.95 3.3 36.9 12.5 40 24.5 74.9 16.4 63.0\n 7 female Netba~ 3.96 5.5 36.3 12.4 71 22.6 101. 17.9 56.0\n 8 female BBall 3.96 7.5 37.5 12.3 60 20.6 109. 19.8 63.3\n 9 female Tennis 4 4.2 36.6 12 57 25.4 109 20.9 56.6\n10 female Netba~ 4.02 9.1 37.7 12.7 107 23.0 77 18.1 57.3\n# i 192 more rows\n# i 2 more variables: Ht , Wt \n```\n:::\n:::\n\n\n\n## Breaking ties by another variable\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% arrange(RCC, BMI)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM\n \n 1 female Netba~ 3.8 6.6 36.5 12.4 102 24.4 157. 26.6 54.4\n 2 female T400m 3.9 6 38.9 13.5 16 19.4 48.4 10.5 53.7\n 3 female Netba~ 3.9 6.3 35.9 12.1 78 20.1 70 15.0 57.3\n 4 female Row 3.91 7.3 37.6 12.9 43 22.3 126. 25.2 54.8\n 5 female Netba~ 3.95 6.6 38.4 12.8 33 19.9 68.9 15.6 54.1\n 6 female Row 3.95 3.3 36.9 12.5 40 24.5 74.9 16.4 63.0\n 7 female BBall 3.96 7.5 37.5 12.3 60 20.6 109. 19.8 63.3\n 8 female Netba~ 3.96 5.5 36.3 12.4 71 22.6 101. 17.9 56.0\n 9 female Tennis 4 4.2 36.6 12 57 25.4 109 20.9 56.6\n10 female Netba~ 4.02 9.1 37.7 12.7 107 23.0 77 18.1 57.3\n# i 192 more rows\n# i 2 more variables: Ht , Wt \n```\n:::\n:::\n\n\n\n## Descending order\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% arrange(desc(BMI))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM\n \n 1 male Field 5.48 6.2 48.2 16.3 94 34.4 82.7 13.9 106 \n 2 male Field 4.96 8.3 45.3 15.7 141 33.7 114. 17.4 89 \n 3 male Field 5.48 4.6 49.4 18 132 32.5 55.7 8.51 102 \n 4 female Field 4.75 7.5 43.8 15.2 90 31.9 132. 23.0 73.0\n 5 male Field 5.01 8.9 46 15.9 212 30.2 112. 19.9 78 \n 6 male Field 5.01 8.9 46 15.9 212 30.2 96.9 18.1 80 \n 7 male Field 5.09 8.9 46.3 15.4 44 30.0 71.1 14.0 88 \n 8 female Field 4.58 5.8 42.1 14.7 164 28.6 110. 21.3 68.9\n 9 female Field 4.51 9 39.7 14.3 36 28.1 136. 24.9 63.0\n10 male WPolo 5.34 6.2 49.8 17.2 143 27.8 75.7 13.5 82 \n# i 192 more rows\n# i 2 more variables: Ht , Wt \n```\n:::\n:::\n\n\n\n## “The top ones”\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>%\n arrange(desc(Wt)) %>%\n slice(1:7) %>%\n select(Sport, Wt)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 7 x 2\n Sport Wt\n \n1 Field 123.\n2 BBall 114.\n3 Field 111.\n4 Field 108.\n5 Field 103.\n6 WPolo 101 \n7 BBall 100.\n```\n:::\n:::\n\n\n\n## Another way\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% \n slice_max(order_by = Wt, n=7) %>% \n select(Sport, Wt)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 7 x 2\n Sport Wt\n \n1 Field 123.\n2 BBall 114.\n3 Field 111.\n4 Field 108.\n5 Field 103.\n6 WPolo 101 \n7 BBall 100.\n```\n:::\n:::\n\n\n\n\n## Create new variables from old ones\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>%\n mutate(wt_lb = Wt * 2.2) %>%\n select(Sport, Sex, Wt, wt_lb) %>% \n arrange(Wt)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 4\n Sport Sex Wt wt_lb\n \n 1 Gym female 37.8 83.2\n 2 Gym female 43.8 96.4\n 3 Gym female 45.1 99.2\n 4 Tennis female 45.8 101. \n 5 Tennis female 47.4 104. \n 6 Gym female 47.8 105. \n 7 T400m female 49.2 108. \n 8 Row female 49.8 110. \n 9 T400m female 50.9 112. \n10 Netball female 51.9 114. \n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Turning the result into a number\nOutput is always data frame unless you explicitly turn it into something\nelse, eg. the weight of the heaviest athlete, as a number:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% arrange(desc(Wt)) %>% pluck(\"Wt\", 1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 123.2\n```\n:::\n:::\n\n\n\nOr the 20 heaviest weights in descending order:\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>%\n arrange(desc(Wt)) %>%\n slice(1:20) %>%\n pluck(\"Wt\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 123.20 113.70 111.30 108.20 102.70 101.00 100.20 98.00 97.90\n[10] 97.90 97.00 96.90 96.30 94.80 94.80 94.70 94.70 94.60\n[19] 94.25 94.20\n```\n:::\n:::\n\n\n\n## Another way to do the last one\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>%\n arrange(desc(Wt)) %>%\n slice(1:20) %>%\n pull(\"Wt\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 123.20 113.70 111.30 108.20 102.70 101.00 100.20 98.00 97.90\n[10] 97.90 97.00 96.90 96.30 94.80 94.80 94.70 94.70 94.60\n[19] 94.25 94.20\n```\n:::\n:::\n\n\n\n`pull` grabs the column you name *as a vector* (of whatever it contains).\n\n## To find the mean height of the women athletes\nTwo ways:\n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% group_by(Sex) %>% summarize(m = mean(Ht))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 2 x 2\n Sex m\n \n1 female 175.\n2 male 186.\n```\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>%\n filter(Sex == \"female\") %>%\n summarize(m = mean(Ht))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 1 x 1\n m\n \n1 175.\n```\n:::\n:::\n\n\n\n\\normalsize\n\n## Summary of data selection/arrangement \"verbs\" {.smaller}\n\n | Verb | Purpose|\n |:-----|:-------------------------------|\n |`select` | Choose columns|\n |`slice` | Choose rows by number|\n |`slice_sample` | Choose random rows |\n |`slice_max` | Choose rows with largest values on a variable (also `slice_min`) |\n |`filter` | Choose rows satisfying conditions|\n | `arrange` | Sort in order by column(s) |\n | `mutate` | Create new variables | \n | `group_by` | Create groups to work with|\n |`summarize` | Calculate summary statistics (by groups if defined) |\n | `pluck` | Extract items from data frame |\n | `pull` | Extract a single column from a data frame as a vector|\n\n \n## Looking things up in another data frame\n\n- Suppose you are working in the nails department of a hardware store and you find that you have sold these items:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \"http://ritsokiguess.site/datafiles/nail_sales.csv\"\nsales <- read_csv(my_url)\nsales\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 6 x 2\n product_code sales\n \n1 061-5344-6 10\n2 161-0090-0 6\n3 061-5388-2 2\n4 161-0199-4 8\n5 061-5375-2 5\n6 061-4525-2 3\n```\n:::\n:::\n\n\n\n\n## Product descriptions and prices\n\n- but you don't remember what these product codes are, and you would like to know the total revenue from these sales.\n\n- Fortunately you found a list of product descriptions and prices:\n\n\\small\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \"http://ritsokiguess.site/datafiles/nail_desc.csv\"\ndesc <- read_csv(my_url)\ndesc\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 7 x 5\n product_code description size qty price\n \n1 061-4525-2 spike nail \"10\\\"\" 1 1.49\n2 061-5329-4 masonry nail \"1.5\\\"\" 112 8.19\n3 061-5344-6 finishing nail \"1\\\"\" 1298 6.99\n4 061-5375-2 roofing nail \"1.25\\\"\" 192 6.99\n5 061-5388-2 framing nail \"4\\\"\" 25 8.19\n6 161-0090-0 wood nail \"1\\\"\" 25 2.39\n7 161-0199-4 panel nail \"1-5/8\\\"\" 20 4.69\n```\n:::\n:::\n\n\n\n- the `size` values are measured in inches (symbol `\"`), but R uses the same symbol for the start and end of text, so the `\"` representing \"inches\" is \"escaped\". Hence the odd look.\n\n\\normalsize\n\n## The lookup\n\n- How do you \"look up\" the product codes to find the product descriptions and prices?\n- `left_join`.\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales %>% left_join(desc)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 6 x 6\n product_code sales description size qty price\n \n1 061-5344-6 10 finishing nail \"1\\\"\" 1298 6.99\n2 161-0090-0 6 wood nail \"1\\\"\" 25 2.39\n3 061-5388-2 2 framing nail \"4\\\"\" 25 8.19\n4 161-0199-4 8 panel nail \"1-5/8\\\"\" 20 4.69\n5 061-5375-2 5 roofing nail \"1.25\\\"\" 192 6.99\n6 061-4525-2 3 spike nail \"10\\\"\" 1 1.49\n```\n:::\n:::\n\n\n\n## What we have\n\n- this looks up all the rows in the *first* dataframe that are also in the *second*. \n- by default matches all columns with same name in two dataframes (`product_code` here)\n- get *all* columns in *both* dataframes. The rows are the ones for that `product_code`.\n\nSo now can work out how much the total revenue was:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales %>% left_join(desc) %>% \n mutate(product_revenue = sales*price) %>% \n summarize(total_revenue = sum(product_revenue))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 1 x 1\n total_revenue\n \n1 178.\n```\n:::\n:::\n\n\n\n\n## More comments\n\n- if any product codes are not matched, you get NA in the added columns\n- anything in the *second* dataframe that was not in the first does not appear (here, any products that were not sold)\n- other variations (examples follow):\n - if there are two columns with the same name in the two dataframes, and you only want to match on one, use `by` with one column name\n - if the columns you want to look up have different names in the two dataframes, use `by` with a \"named list\"\n\n## Matching on only some matching names\n\n- Suppose the `sales` dataframe *also* had a column `qty` (which was the quantity sold): \n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales %>% rename(\"qty\"=\"sales\") -> sales1\nsales1\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 6 x 2\n product_code qty\n \n1 061-5344-6 10\n2 161-0090-0 6\n3 061-5388-2 2\n4 161-0199-4 8\n5 061-5375-2 5\n6 061-4525-2 3\n```\n:::\n:::\n\n\n\\normalsize\n\n- The `qty` in `sales1` is the quantity sold, but the `qty` in `desc` is the number of nails in a package. These should *not* be matched: they are different things.\n\n## Matching only on product code\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales1 %>% \n left_join(desc, join_by(product_code))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 6 x 6\n product_code qty.x description size qty.y price\n \n1 061-5344-6 10 finishing nail \"1\\\"\" 1298 6.99\n2 161-0090-0 6 wood nail \"1\\\"\" 25 2.39\n3 061-5388-2 2 framing nail \"4\\\"\" 25 8.19\n4 161-0199-4 8 panel nail \"1-5/8\\\"\" 20 4.69\n5 061-5375-2 5 roofing nail \"1.25\\\"\" 192 6.99\n6 061-4525-2 3 spike nail \"10\\\"\" 1 1.49\n```\n:::\n:::\n\n\n\n- Get `qty.x` (from `sales1`) and `qty.y` (from `desc`).\n\n## Matching on different names 1/2\n\n- Suppose the product code in `sales` was just `code`:\n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales %>% rename(\"code\" = \"product_code\") -> sales2\nsales2\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 6 x 2\n code sales\n \n1 061-5344-6 10\n2 161-0090-0 6\n3 061-5388-2 2\n4 161-0199-4 8\n5 061-5375-2 5\n6 061-4525-2 3\n```\n:::\n:::\n\n\n\\normalsize\n\n- How to match the two product codes that have different names?\n\n## Matching on different names 2/2\n\n- Use `by`, but like this:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales2 %>% \n left_join(desc, join_by(code == product_code))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 6 x 6\n code sales description size qty price\n \n1 061-5344-6 10 finishing nail \"1\\\"\" 1298 6.99\n2 161-0090-0 6 wood nail \"1\\\"\" 25 2.39\n3 061-5388-2 2 framing nail \"4\\\"\" 25 8.19\n4 161-0199-4 8 panel nail \"1-5/8\\\"\" 20 4.69\n5 061-5375-2 5 roofing nail \"1.25\\\"\" 192 6.99\n6 061-4525-2 3 spike nail \"10\\\"\" 1 1.49\n```\n:::\n:::\n\n\n\n## Other types of join\n\n- `right_join`: interchanges roles, looking up keys from second dataframe in first.\n- `anti_join`: give me all the rows in the first dataframe that are *not* in the second. (Use this eg. to see whether the product descriptions are incomplete.)\n- `full_join`: give me all the rows in both dataframes, with missings as needed.\n\n## Full join here\n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales %>% full_join(desc)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 7 x 6\n product_code sales description size qty price\n \n1 061-5344-6 10 finishing nail \"1\\\"\" 1298 6.99\n2 161-0090-0 6 wood nail \"1\\\"\" 25 2.39\n3 061-5388-2 2 framing nail \"4\\\"\" 25 8.19\n4 161-0199-4 8 panel nail \"1-5/8\\\"\" 20 4.69\n5 061-5375-2 5 roofing nail \"1.25\\\"\" 192 6.99\n6 061-4525-2 3 spike nail \"10\\\"\" 1 1.49\n7 061-5329-4 NA masonry nail \"1.5\\\"\" 112 8.19\n```\n:::\n:::\n\n\n\\normalsize\n\n- The missing `sales` for \"masonry nail\" says that it was in the lookup table `desc`, but we didn't sell any.\n\n\n## The same thing, but with `anti_join`\n\nAnything in first df but not in second?\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndesc %>% anti_join(sales)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 1 x 5\n product_code description size qty price\n \n1 061-5329-4 masonry nail \"1.5\\\"\" 112 8.19\n```\n:::\n:::\n\n\n\nMasonry nails are the only thing in our product description file that we did not sell any of.\n\n", + "markdown": "---\ntitle: \"Choosing things in dataframes\"\n---\n\n\n\n## Packages\n\nThe usual:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\n```\n:::\n\n\n\n## Doing things with data frames\n\nLet's go back to our Australian athletes:\n\n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM Ht\n \n 1 female Netball 4.56 13.3 42.2 13.6 20 19.2 49 11.3 53.1 177.\n 2 female Netball 4.15 6 38 12.7 59 21.2 110. 25.3 47.1 173.\n 3 female Netball 4.16 7.6 37.5 12.3 22 21.4 89 19.4 53.4 176 \n 4 female Netball 4.32 6.4 37.7 12.3 30 21.0 98.3 19.6 48.8 170.\n 5 female Netball 4.06 5.8 38.7 12.8 78 21.8 122. 23.1 56.0 183 \n 6 female Netball 4.12 6.1 36.6 11.8 21 21.4 90.4 16.9 56.4 178.\n 7 female Netball 4.17 5 37.4 12.7 109 21.5 107. 21.3 53.1 177.\n 8 female Netball 3.8 6.6 36.5 12.4 102 24.4 157. 26.6 54.4 174.\n 9 female Netball 3.96 5.5 36.3 12.4 71 22.6 101. 17.9 56.0 174.\n10 female Netball 4.44 9.7 41.4 14.1 64 22.8 126. 25.0 51.6 174.\n# i 192 more rows\n# i 1 more variable: Wt \n```\n:::\n:::\n\n\n\n## Choosing a column\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(Sport)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 1\n Sport \n \n 1 Netball\n 2 Netball\n 3 Netball\n 4 Netball\n 5 Netball\n 6 Netball\n 7 Netball\n 8 Netball\n 9 Netball\n10 Netball\n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Choosing several columns\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(Sport, Hg, BMI)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 3\n Sport Hg BMI\n \n 1 Netball 13.6 19.2\n 2 Netball 12.7 21.2\n 3 Netball 12.3 21.4\n 4 Netball 12.3 21.0\n 5 Netball 12.8 21.8\n 6 Netball 11.8 21.4\n 7 Netball 12.7 21.5\n 8 Netball 12.4 24.4\n 9 Netball 12.4 22.6\n10 Netball 14.1 22.8\n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Choosing consecAutive columns\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(Sex:WCC)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 4\n Sex Sport RCC WCC\n \n 1 female Netball 4.56 13.3\n 2 female Netball 4.15 6 \n 3 female Netball 4.16 7.6\n 4 female Netball 4.32 6.4\n 5 female Netball 4.06 5.8\n 6 female Netball 4.12 6.1\n 7 female Netball 4.17 5 \n 8 female Netball 3.8 6.6\n 9 female Netball 3.96 5.5\n10 female Netball 4.44 9.7\n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Choosing all-but some columns\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(-(RCC:LBM))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 4\n Sex Sport Ht Wt\n \n 1 female Netball 177. 59.9\n 2 female Netball 173. 63 \n 3 female Netball 176 66.3\n 4 female Netball 170. 60.7\n 5 female Netball 183 72.9\n 6 female Netball 178. 67.9\n 7 female Netball 177. 67.5\n 8 female Netball 174. 74.1\n 9 female Netball 174. 68.2\n10 female Netball 174. 68.8\n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Select-helpers\n\nOther ways to select columns: those whose name:\n\n- `starts_with` something\n- `ends_with` something\n- `contains` something\n- `matches` a \"regular expression\"\n- `everything()` select all the columns\n\n## Columns whose names Abegin with S\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(starts_with(\"S\"))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 3\n Sex Sport SSF\n \n 1 female Netball 49 \n 2 female Netball 110. \n 3 female Netball 89 \n 4 female Netball 98.3\n 5 female Netball 122. \n 6 female Netball 90.4\n 7 female Netball 107. \n 8 female Netball 157. \n 9 female Netball 101. \n10 female Netball 126. \n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Columns whose names end with C\n\neither uppercase or lowercase:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(ends_with(\"c\"))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 3\n RCC WCC Hc\n \n 1 4.56 13.3 42.2\n 2 4.15 6 38 \n 3 4.16 7.6 37.5\n 4 4.32 6.4 37.7\n 5 4.06 5.8 38.7\n 6 4.12 6.1 36.6\n 7 4.17 5 37.4\n 8 3.8 6.6 36.5\n 9 3.96 5.5 36.3\n10 4.44 9.7 41.4\n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Case-sensitive\n\nThis works with any of the select-helpers:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(ends_with(\"C\", ignore.case=FALSE))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 2\n RCC WCC\n \n 1 4.56 13.3\n 2 4.15 6 \n 3 4.16 7.6\n 4 4.32 6.4\n 5 4.06 5.8\n 6 4.12 6.1\n 7 4.17 5 \n 8 3.8 6.6\n 9 3.96 5.5\n10 4.44 9.7\n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Column names containing letter R\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(contains(\"r\"))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 3\n Sport RCC Ferr\n \n 1 Netball 4.56 20\n 2 Netball 4.15 59\n 3 Netball 4.16 22\n 4 Netball 4.32 30\n 5 Netball 4.06 78\n 6 Netball 4.12 21\n 7 Netball 4.17 109\n 8 Netball 3.8 102\n 9 Netball 3.96 71\n10 Netball 4.44 64\n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Exactly two characters, ending with T\n\nIn regular expression terms, this is `^.t$`:\n\n- `^` means \"start of text\"\n- `.` means \"exactly one character, but could be anything\"\n- `$` means \"end of text\".\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(matches(\"^.t$\"))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 2\n Ht Wt\n \n 1 177. 59.9\n 2 173. 63 \n 3 176 66.3\n 4 170. 60.7\n 5 183 72.9\n 6 178. 67.9\n 7 177. 67.5\n 8 174. 74.1\n 9 174. 68.2\n10 174. 68.8\n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Choosing columns by property\n\n- Use `where` as with summarizing several columns\n- eg, to choose text columns:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(where(is.character))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 2\n Sex Sport \n \n 1 female Netball\n 2 female Netball\n 3 female Netball\n 4 female Netball\n 5 female Netball\n 6 female Netball\n 7 female Netball\n 8 female Netball\n 9 female Netball\n10 female Netball\n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Choosing rows by number\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% slice(16:25)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 10 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM Ht\n \n 1 female Netball 4.25 10.7 39.5 13.2 127 24.5 157. 26.5 54.5 174 \n 2 female Netball 4.46 10.9 39.7 13.7 102 24.0 116. 23.0 57.2 176 \n 3 female Netball 4.4 9.3 40.4 13.6 86 26.2 182. 30.1 54.4 172.\n 4 female Netball 4.83 8.4 41.8 13.4 40 20.0 71.6 13.9 57.6 183.\n 5 female Netball 4.23 6.9 38.3 12.6 50 25.7 144. 26.6 61.5 180.\n 6 female Netball 4.24 8.4 37.6 12.5 58 25.6 201. 35.5 53.5 180.\n 7 female Netball 3.95 6.6 38.4 12.8 33 19.9 68.9 15.6 54.1 180.\n 8 female Netball 4.03 8.5 37.7 13 51 23.4 104. 19.6 55.4 172.\n 9 female BBall 3.96 7.5 37.5 12.3 60 20.6 109. 19.8 63.3 196.\n10 female BBall 4.41 8.3 38.2 12.7 68 20.7 103. 21.3 58.6 190.\n# i 1 more variable: Wt \n```\n:::\n:::\n\n\n\n## Non-consecutive rows\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% \n slice(10, 13, 17, 42)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 4 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM Ht\n \n1 female Netball 4.44 9.7 41.4 14.1 64 22.8 126. 25.0 51.6 174.\n2 female Netball 4.02 9.1 37.7 12.7 107 23.0 77 18.1 57.3 174.\n3 female Netball 4.46 10.9 39.7 13.7 102 24.0 116. 23.0 57.2 176 \n4 female Row 4.37 8.1 41.8 14.3 53 23.5 98 21.8 63.0 185.\n# i 1 more variable: Wt \n```\n:::\n:::\n\n\n\n## A random sample of rows\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% slice_sample(n=8)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 8 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM Ht\n \n1 female T400m 4.2 6.5 39.1 13 51 20.1 36.8 9.91 47.6 162 \n2 female Row 4.57 6.6 42.8 14.5 85 20.8 114. 21.5 52.8 180.\n3 male TSprnt 4.64 9 42.9 14.9 122 24.0 38.9 7.52 77 186 \n4 male Field 5.11 9.6 48.2 16.7 103 27.4 65.9 11.7 83 186.\n5 male Row 5.18 6.5 45.4 14.9 93 19.7 54 10.8 48 165.\n6 female Row 4.51 8.3 43.7 14.7 34 21.3 69.9 18.0 56.3 180.\n7 female BBall 4.14 5 36.4 11.6 21 21.9 105. 19.9 55.4 178.\n8 female Row 4.41 5.9 41.1 13.5 41 24.0 124. 22.4 61.7 182.\n# i 1 more variable: Wt \n```\n:::\n:::\n\n\n\n## Rows for which something is true\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% filter(Sport == \"Tennis\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 11 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM Ht\n \n 1 female Tennis 4 4.2 36.6 12 57 25.4 109 20.9 56.6 168.\n 2 female Tennis 4.4 4 40.8 13.9 73 22.1 98.1 19.6 56.0 178.\n 3 female Tennis 4.38 7.9 39.8 13.5 88 21.2 80.6 17.1 46.5 162.\n 4 female Tennis 4.08 6.6 37.8 12.1 182 20.5 68.3 15.3 51.8 172.\n 5 female Tennis 4.98 6.4 44.8 14.8 80 17.1 47.6 11.1 42.2 167.\n 6 female Tennis 5.16 7.2 44.3 14.5 88 18.3 61.9 12.9 48.8 175 \n 7 female Tennis 4.66 6.4 40.9 13.9 109 18.4 38.2 8.45 41.9 158.\n 8 male Tennis 5.66 8.3 50.2 17.7 38 23.8 56.5 10.0 72 184.\n 9 male Tennis 5.03 6.4 42.7 14.3 122 22.0 47.6 8.51 68 183.\n10 male Tennis 4.97 8.8 43 14.9 233 22.3 60.4 11.5 63 178.\n11 male Tennis 5.38 6.3 46 15.7 32 21.1 34.9 6.26 72 191.\n# i 1 more variable: Wt \n```\n:::\n:::\n\n\n\n## More complicated selections\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% filter(Sport == \"Tennis\", RCC < 5)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 7 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM Ht\n \n1 female Tennis 4 4.2 36.6 12 57 25.4 109 20.9 56.6 168.\n2 female Tennis 4.4 4 40.8 13.9 73 22.1 98.1 19.6 56.0 178.\n3 female Tennis 4.38 7.9 39.8 13.5 88 21.2 80.6 17.1 46.5 162.\n4 female Tennis 4.08 6.6 37.8 12.1 182 20.5 68.3 15.3 51.8 172.\n5 female Tennis 4.98 6.4 44.8 14.8 80 17.1 47.6 11.1 42.2 167.\n6 female Tennis 4.66 6.4 40.9 13.9 109 18.4 38.2 8.45 41.9 158.\n7 male Tennis 4.97 8.8 43 14.9 233 22.3 60.4 11.5 63 178.\n# i 1 more variable: Wt \n```\n:::\n:::\n\n\n\n## Another way to do \"and\"\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% filter(Sport == \"Tennis\") %>% \n filter(RCC < 5)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 7 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM Ht\n \n1 female Tennis 4 4.2 36.6 12 57 25.4 109 20.9 56.6 168.\n2 female Tennis 4.4 4 40.8 13.9 73 22.1 98.1 19.6 56.0 178.\n3 female Tennis 4.38 7.9 39.8 13.5 88 21.2 80.6 17.1 46.5 162.\n4 female Tennis 4.08 6.6 37.8 12.1 182 20.5 68.3 15.3 51.8 172.\n5 female Tennis 4.98 6.4 44.8 14.8 80 17.1 47.6 11.1 42.2 167.\n6 female Tennis 4.66 6.4 40.9 13.9 109 18.4 38.2 8.45 41.9 158.\n7 male Tennis 4.97 8.8 43 14.9 233 22.3 60.4 11.5 63 178.\n# i 1 more variable: Wt \n```\n:::\n:::\n\n\n\n## Either/Or\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% filter(Sport == \"Tennis\" | RCC > 5)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 66 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM Ht\n \n 1 female Row 5.02 6.4 44.8 15.2 48 19.8 91 19.2 53.6 183.\n 2 female T400m 5.31 9.5 47.1 15.9 29 21.4 57.9 11.1 57.5 174.\n 3 female Field 5.33 9.3 47 15 62 25.3 103. 19.5 59.9 172.\n 4 female TSprnt 5.16 8.2 45.3 14.7 34 20.3 46.1 10.2 51.5 168 \n 5 female Tennis 4 4.2 36.6 12 57 25.4 109 20.9 56.6 168.\n 6 female Tennis 4.4 4 40.8 13.9 73 22.1 98.1 19.6 56.0 178.\n 7 female Tennis 4.38 7.9 39.8 13.5 88 21.2 80.6 17.1 46.5 162.\n 8 female Tennis 4.08 6.6 37.8 12.1 182 20.5 68.3 15.3 51.8 172.\n 9 female Tennis 4.98 6.4 44.8 14.8 80 17.1 47.6 11.1 42.2 167.\n10 female Tennis 5.16 7.2 44.3 14.5 88 18.3 61.9 12.9 48.8 175 \n# i 56 more rows\n# i 1 more variable: Wt \n```\n:::\n:::\n\n\n\n## Sorting into order\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% arrange(RCC)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM Ht\n \n 1 female Netball 3.8 6.6 36.5 12.4 102 24.4 157. 26.6 54.4 174.\n 2 female Netball 3.9 6.3 35.9 12.1 78 20.1 70 15.0 57.3 183.\n 3 female T400m 3.9 6 38.9 13.5 16 19.4 48.4 10.5 53.7 176 \n 4 female Row 3.91 7.3 37.6 12.9 43 22.3 126. 25.2 54.8 181.\n 5 female Netball 3.95 6.6 38.4 12.8 33 19.9 68.9 15.6 54.1 180.\n 6 female Row 3.95 3.3 36.9 12.5 40 24.5 74.9 16.4 63.0 175.\n 7 female Netball 3.96 5.5 36.3 12.4 71 22.6 101. 17.9 56.0 174.\n 8 female BBall 3.96 7.5 37.5 12.3 60 20.6 109. 19.8 63.3 196.\n 9 female Tennis 4 4.2 36.6 12 57 25.4 109 20.9 56.6 168.\n10 female Netball 4.02 9.1 37.7 12.7 107 23.0 77 18.1 57.3 174.\n# i 192 more rows\n# i 1 more variable: Wt \n```\n:::\n:::\n\n\n\n## Breaking ties by another variable\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% arrange(RCC, BMI)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM Ht\n \n 1 female Netball 3.8 6.6 36.5 12.4 102 24.4 157. 26.6 54.4 174.\n 2 female T400m 3.9 6 38.9 13.5 16 19.4 48.4 10.5 53.7 176 \n 3 female Netball 3.9 6.3 35.9 12.1 78 20.1 70 15.0 57.3 183.\n 4 female Row 3.91 7.3 37.6 12.9 43 22.3 126. 25.2 54.8 181.\n 5 female Netball 3.95 6.6 38.4 12.8 33 19.9 68.9 15.6 54.1 180.\n 6 female Row 3.95 3.3 36.9 12.5 40 24.5 74.9 16.4 63.0 175.\n 7 female BBall 3.96 7.5 37.5 12.3 60 20.6 109. 19.8 63.3 196.\n 8 female Netball 3.96 5.5 36.3 12.4 71 22.6 101. 17.9 56.0 174.\n 9 female Tennis 4 4.2 36.6 12 57 25.4 109 20.9 56.6 168.\n10 female Netball 4.02 9.1 37.7 12.7 107 23.0 77 18.1 57.3 174.\n# i 192 more rows\n# i 1 more variable: Wt \n```\n:::\n:::\n\n\n\n## Descending order\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% arrange(desc(BMI))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM Ht\n \n 1 male Field 5.48 6.2 48.2 16.3 94 34.4 82.7 13.9 106 189.\n 2 male Field 4.96 8.3 45.3 15.7 141 33.7 114. 17.4 89 179.\n 3 male Field 5.48 4.6 49.4 18 132 32.5 55.7 8.51 102 185 \n 4 female Field 4.75 7.5 43.8 15.2 90 31.9 132. 23.0 73.0 172.\n 5 male Field 5.01 8.9 46 15.9 212 30.2 112. 19.9 78 180.\n 6 male Field 5.01 8.9 46 15.9 212 30.2 96.9 18.1 80 180.\n 7 male Field 5.09 8.9 46.3 15.4 44 30.0 71.1 14.0 88 185.\n 8 female Field 4.58 5.8 42.1 14.7 164 28.6 110. 21.3 68.9 175 \n 9 female Field 4.51 9 39.7 14.3 36 28.1 136. 24.9 63.0 173.\n10 male WPolo 5.34 6.2 49.8 17.2 143 27.8 75.7 13.5 82 185.\n# i 192 more rows\n# i 1 more variable: Wt \n```\n:::\n:::\n\n\n\n## \"The top ones\"\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>%\n arrange(desc(Wt)) %>%\n slice(1:7) %>%\n select(Sport, Wt)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 7 x 2\n Sport Wt\n \n1 Field 123.\n2 BBall 114.\n3 Field 111.\n4 Field 108.\n5 Field 103.\n6 WPolo 101 \n7 BBall 100.\n```\n:::\n:::\n\n\n\n## Another way\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% \n slice_max(order_by = Wt, n=7) %>% \n select(Sport, Wt)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 7 x 2\n Sport Wt\n \n1 Field 123.\n2 BBall 114.\n3 Field 111.\n4 Field 108.\n5 Field 103.\n6 WPolo 101 \n7 BBall 100.\n```\n:::\n:::\n\n\n\n## Create new variables from old ones\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>%\n mutate(wt_lb = Wt * 2.2) %>%\n select(Sport, Sex, Wt, wt_lb) %>% \n arrange(Wt)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 4\n Sport Sex Wt wt_lb\n \n 1 Gym female 37.8 83.2\n 2 Gym female 43.8 96.4\n 3 Gym female 45.1 99.2\n 4 Tennis female 45.8 101. \n 5 Tennis female 47.4 104. \n 6 Gym female 47.8 105. \n 7 T400m female 49.2 108. \n 8 Row female 49.8 110. \n 9 T400m female 50.9 112. \n10 Netball female 51.9 114. \n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Turning the result into a number\n\nOutput is always data frame unless you explicitly turn it into something\nelse, eg. the weight of the heaviest athlete, as a number:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% arrange(desc(Wt)) %>% pluck(\"Wt\", 1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 123.2\n```\n:::\n:::\n\n\n\nOr the 20 heaviest weights in descending order:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>%\n arrange(desc(Wt)) %>%\n slice(1:20) %>%\n pluck(\"Wt\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 123.20 113.70 111.30 108.20 102.70 101.00 100.20 98.00 97.90 97.90\n[11] 97.00 96.90 96.30 94.80 94.80 94.70 94.70 94.60 94.25 94.20\n```\n:::\n:::\n\n\n\n## Another way to do the last one\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>%\n arrange(desc(Wt)) %>%\n slice(1:20) %>%\n pull(\"Wt\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 123.20 113.70 111.30 108.20 102.70 101.00 100.20 98.00 97.90 97.90\n[11] 97.00 96.90 96.30 94.80 94.80 94.70 94.70 94.60 94.25 94.20\n```\n:::\n:::\n\n\n\n`pull` grabs the column you name *as a vector* (of whatever it\ncontains).\n\n## To find the mean height of the women athletes\n\nTwo ways:\n\n\\small\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% group_by(Sex) %>% summarize(m = mean(Ht))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 2 x 2\n Sex m\n \n1 female 175.\n2 male 186.\n```\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>%\n filter(Sex == \"female\") %>%\n summarize(m = mean(Ht))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 1 x 1\n m\n \n1 175.\n```\n:::\n:::\n\n\n\n\\normalsize\n\n## Summary of data selection/arrangement \"verbs\" {.smaller}\n\n| Verb | Purpose |\n|:---------------|:-------------------------------------------------------|\n| `select` | Choose columns |\n| `slice` | Choose rows by number |\n| `slice_sample` | Choose random rows |\n| `slice_max` | Choose rows with largest values on a variable (also `slice_min`) |\n| `filter` | Choose rows satisfying conditions |\n| `arrange` | Sort in order by column(s) |\n| `mutate` | Create new variables |\n| `group_by` | Create groups to work with |\n| `summarize` | Calculate summary statistics (by groups if defined) |\n| `pluck` | Extract items from data frame |\n| `pull` | Extract a single column from a data frame as a vector |\n\n## Looking things up in another data frame\n\n- Suppose you are working in the nails department of a hardware store\n and you find that you have sold these items:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \"http://ritsokiguess.site/datafiles/nail_sales.csv\"\nsales <- read_csv(my_url)\nsales\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 6 x 2\n product_code sales\n \n1 061-5344-6 10\n2 161-0090-0 6\n3 061-5388-2 2\n4 161-0199-4 8\n5 061-5375-2 5\n6 061-4525-2 3\n```\n:::\n:::\n\n\n\n## Product descriptions and prices\n\n- but you don't remember what these product codes are, and you would\n like to know the total revenue from these sales.\n\n- Fortunately you found a list of product descriptions and prices:\n\n\\small\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \"http://ritsokiguess.site/datafiles/nail_desc.csv\"\ndesc <- read_csv(my_url)\ndesc\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 7 x 5\n product_code description size qty price\n \n1 061-4525-2 spike nail \"10\\\"\" 1 1.49\n2 061-5329-4 masonry nail \"1.5\\\"\" 112 8.19\n3 061-5344-6 finishing nail \"1\\\"\" 1298 6.99\n4 061-5375-2 roofing nail \"1.25\\\"\" 192 6.99\n5 061-5388-2 framing nail \"4\\\"\" 25 8.19\n6 161-0090-0 wood nail \"1\\\"\" 25 2.39\n7 161-0199-4 panel nail \"1-5/8\\\"\" 20 4.69\n```\n:::\n:::\n\n\n\n- the `size` values are measured in inches (symbol `\"`), but R uses\n the same symbol for the start and end of text, so the `\"`\n representing \"inches\" is \"escaped\". Hence the odd look.\n\n\\normalsize\n\n## The lookup\n\n- How do you \"look up\" the product codes to find the product\n descriptions and prices?\n- `left_join`.\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales %>% left_join(desc)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 6 x 6\n product_code sales description size qty price\n \n1 061-5344-6 10 finishing nail \"1\\\"\" 1298 6.99\n2 161-0090-0 6 wood nail \"1\\\"\" 25 2.39\n3 061-5388-2 2 framing nail \"4\\\"\" 25 8.19\n4 161-0199-4 8 panel nail \"1-5/8\\\"\" 20 4.69\n5 061-5375-2 5 roofing nail \"1.25\\\"\" 192 6.99\n6 061-4525-2 3 spike nail \"10\\\"\" 1 1.49\n```\n:::\n:::\n\n\n\n## What we have\n\n- this looks up all the rows in the *first* dataframe that are also in\n the *second*.\n- by default matches all columns with same name in two dataframes\n (`product_code` here)\n- get *all* columns in *both* dataframes. The rows are the ones for\n that `product_code`.\n\nSo now can work out how much the total revenue was:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales %>% left_join(desc) %>% \n mutate(product_revenue = sales*price) %>% \n summarize(total_revenue = sum(product_revenue))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 1 x 1\n total_revenue\n \n1 178.\n```\n:::\n:::\n\n\n\n## More comments\n\n- if any product codes are not matched, you get NA in the added\n columns\n- anything in the *second* dataframe that was not in the first does\n not appear (here, any products that were not sold)\n- other variations (examples follow):\n - if there are two columns with the same name in the two\n dataframes, and you only want to match on one, use `by` with one\n column name\n - if the columns you want to look up have different names in the\n two dataframes, use `by` with a \"named list\"\n\n## Matching on only some matching names\n\n- Suppose the `sales` dataframe *also* had a column `qty` (which was\n the quantity sold):\n\n\\small\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales %>% rename(\"qty\"=\"sales\") -> sales1\nsales1\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 6 x 2\n product_code qty\n \n1 061-5344-6 10\n2 161-0090-0 6\n3 061-5388-2 2\n4 161-0199-4 8\n5 061-5375-2 5\n6 061-4525-2 3\n```\n:::\n:::\n\n\n\n\\normalsize\n\n- The `qty` in `sales1` is the quantity sold, but the `qty` in `desc`\n is the number of nails in a package. These should *not* be matched:\n they are different things.\n\n## Matching only on product code\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales1 %>% \n left_join(desc, join_by(product_code))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 6 x 6\n product_code qty.x description size qty.y price\n \n1 061-5344-6 10 finishing nail \"1\\\"\" 1298 6.99\n2 161-0090-0 6 wood nail \"1\\\"\" 25 2.39\n3 061-5388-2 2 framing nail \"4\\\"\" 25 8.19\n4 161-0199-4 8 panel nail \"1-5/8\\\"\" 20 4.69\n5 061-5375-2 5 roofing nail \"1.25\\\"\" 192 6.99\n6 061-4525-2 3 spike nail \"10\\\"\" 1 1.49\n```\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nsales1 %>% \n left_join(desc)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 6 x 5\n product_code qty description size price\n \n1 061-5344-6 10 NA\n2 161-0090-0 6 NA\n3 061-5388-2 2 NA\n4 161-0199-4 8 NA\n5 061-5375-2 5 NA\n6 061-4525-2 3 NA\n```\n:::\n:::\n\n\n\n- Get `qty.x` (from `sales1`) and `qty.y` (from `desc`).\n\n## Matching on different names 1/2\n\n- Suppose the product code in `sales` was just `code`:\n\n\\small\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales %>% rename(\"code\" = \"product_code\") -> sales2\nsales2\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 6 x 2\n code sales\n \n1 061-5344-6 10\n2 161-0090-0 6\n3 061-5388-2 2\n4 161-0199-4 8\n5 061-5375-2 5\n6 061-4525-2 3\n```\n:::\n:::\n\n\n\n\\normalsize\n\n- How to match the two product codes that have different names?\n\n## Matching on different names 2/2\n\n- Use `join_by`, but like this:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales2 %>% \n left_join(desc, join_by(code == product_code))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 6 x 6\n code sales description size qty price\n \n1 061-5344-6 10 finishing nail \"1\\\"\" 1298 6.99\n2 161-0090-0 6 wood nail \"1\\\"\" 25 2.39\n3 061-5388-2 2 framing nail \"4\\\"\" 25 8.19\n4 161-0199-4 8 panel nail \"1-5/8\\\"\" 20 4.69\n5 061-5375-2 5 roofing nail \"1.25\\\"\" 192 6.99\n6 061-4525-2 3 spike nail \"10\\\"\" 1 1.49\n```\n:::\n:::\n\n\n\n## Other types of join\n\n- `right_join`: interchanges roles, looking up keys from second\n dataframe in first.\n- `anti_join`: give me all the rows in the first dataframe that are\n *not* in the second. (Use this eg. to see whether the product\n descriptions are incomplete.)\n- `full_join`: give me all the rows in both dataframes, with missings\n as needed.\n\n## Full join here\n\n\\small\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales %>% full_join(desc)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 7 x 6\n product_code sales description size qty price\n \n1 061-5344-6 10 finishing nail \"1\\\"\" 1298 6.99\n2 161-0090-0 6 wood nail \"1\\\"\" 25 2.39\n3 061-5388-2 2 framing nail \"4\\\"\" 25 8.19\n4 161-0199-4 8 panel nail \"1-5/8\\\"\" 20 4.69\n5 061-5375-2 5 roofing nail \"1.25\\\"\" 192 6.99\n6 061-4525-2 3 spike nail \"10\\\"\" 1 1.49\n7 061-5329-4 NA masonry nail \"1.5\\\"\" 112 8.19\n```\n:::\n:::\n\n\n\n\\normalsize\n\n- The missing `sales` for \"masonry nail\" says that it was in the\n lookup table `desc`, but we didn't sell any.\n\n## The same thing, but with `anti_join`\n\nAnything in first df but not in second?\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndesc %>% anti_join(sales)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 1 x 5\n product_code description size qty price\n \n1 061-5329-4 masonry nail \"1.5\\\"\" 112 8.19\n```\n:::\n\n```{.r .cell-code}\nsales %>% anti_join(desc)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 0 x 2\n# i 2 variables: product_code , sales \n```\n:::\n:::\n\n\n\nMasonry nails are the only thing in our product description file that we\ndid not sell any of.\n", "supporting": [ "choosing_files" ], diff --git a/_freeze/logistic/execute-results/html.json b/_freeze/logistic/execute-results/html.json index 16a8921..c3c68ce 100644 --- a/_freeze/logistic/execute-results/html.json +++ b/_freeze/logistic/execute-results/html.json @@ -1,7 +1,7 @@ { - "hash": "d00d4260f0342837f54c858773a0870d", + "hash": "e6b7540e15c08007b53f23b153419a27", "result": { - "markdown": "---\ntitle: \"Logistic Regression\"\n---\n\n\n\n## Logistic regression\n\n\n* When response variable is measured/counted, regression can work well.\n\n* But what if response is yes/no, lived/died, success/failure?\n\n* Model *probability* of success.\n\n* Probability must be between 0 and 1; need method that ensures this.\n\n* *Logistic regression* does this. In R, is a\n*generalized linear model* with binomial \"family\": \n\n::: {.cell}\n\n```{.r .cell-code}\nglm(y ~ x, family=\"binomial\")\n```\n:::\n\n\n\n* Begin with simplest case.\n\n\n\n## Packages\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(MASS)\nlibrary(tidyverse)\nlibrary(marginaleffects)\nlibrary(broom)\nlibrary(nnet)\nlibrary(conflicted)\nconflict_prefer(\"select\", \"dplyr\")\nconflict_prefer(\"filter\", \"dplyr\")\nconflict_prefer(\"rename\", \"dplyr\")\nconflict_prefer(\"summarize\", \"dplyr\")\n```\n:::\n\n\n \n\n\n## The rats, part 1\n\n\n* Rats given dose of some poison; either live or die:\n\n\\small\n```\ndose status\n0 lived\n1 died\n2 lived\n3 lived\n4 died\n5 died\n```\n\n\\normalsize\n\n## Read in: \n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \"http://ritsokiguess.site/datafiles/rat.txt\"\nrats <- read_delim(my_url, \" \")\nrats\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n \n\n\n## Basic logistic regression\n\n\n* Make response into a factor first:\n\n\\small\n\n::: {.cell}\n\n```{.r .cell-code}\nrats2 <- rats %>% mutate(status = factor(status))\n```\n:::\n\n\\normalsize\n \n\n\n* then fit model:\n\n\\small\n\n::: {.cell}\n\n```{.r .cell-code}\nstatus.1 <- glm(status ~ dose, family = \"binomial\", data = rats2)\n```\n:::\n\n\\normalsize\n \n\n \n\n\n## Output\n\n\\scriptsize\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(status.1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\nCall:\nglm(formula = status ~ dose, family = \"binomial\", data = rats2)\n\nCoefficients:\n Estimate Std. Error z value Pr(>|z|)\n(Intercept) 1.6841 1.7979 0.937 0.349\ndose -0.6736 0.6140 -1.097 0.273\n\n(Dispersion parameter for binomial family taken to be 1)\n\n Null deviance: 8.3178 on 5 degrees of freedom\nResidual deviance: 6.7728 on 4 degrees of freedom\nAIC: 10.773\n\nNumber of Fisher Scoring iterations: 4\n```\n:::\n:::\n\n\\normalsize\n\n\n## Interpreting the output\n\n\n* Like (multiple) regression, get\ntests of significance of individual $x$'s\n\n* Here not significant (only 6 observations).\n\n* \"Slope\" for dose is negative, meaning that as dose increases, probability of event modelled (survival) decreases.\n\n\n\n\n\n## Output part 2: predicted survival probs\n\n\\small\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(status.1)) %>% \n select(dose, estimate)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\\normalsize \n\n## On a graph\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_cap(status.1, condition = \"dose\")\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-revealjs/unnamed-chunk-1-1.png){width=960}\n:::\n:::\n\n\n\n## The rats, more\n\n\n* More realistic: more rats at each dose (say 10).\n\n* Listing each rat on one line makes a big data file.\n\n* Use format below: dose, number of survivals, number of deaths.\n\n```\n\ndose lived died\n0 10 0\n1 7 3 \n2 6 4 \n3 4 6 \n4 2 8 \n5 1 9 \n\n```\n\n\n* 6 lines of data correspond to 60 actual rats.\n\n* Saved in `rat2.txt`.\n\n\n\n## These data\n\n\\footnotesize\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \"http://ritsokiguess.site/datafiles/rat2.txt\"\nrat2 <- read_delim(my_url, \" \")\nrat2\n```\n\n::: {.cell-output-display}\n`````{=html}\n
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\n`````\n:::\n:::\n\n\\normalsize\n\n\n## Create response matrix:\n\n- Each row contains *multiple* observations.\n- Create *two-column* response:\n - \\#survivals in first column, \n - \\#deaths in second.\n\n\n\\footnotesize\n\n::: {.cell}\n\n```{.r .cell-code}\nresponse <- with(rat2, cbind(lived, died))\nresponse\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n lived died\n[1,] 10 0\n[2,] 7 3\n[3,] 6 4\n[4,] 4 6\n[5,] 2 8\n[6,] 1 9\n```\n:::\n:::\n\n\\normalsize\n\n- Response is R `matrix`:\n\n\\scriptsize\n\n::: {.cell}\n\n```{.r .cell-code}\nclass(response)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] \"matrix\" \"array\" \n```\n:::\n:::\n\n\\normalsize\n\n \n## Fit logistic regression\n\n- using response you just made:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrat2.1 <- glm(response ~ dose,\n family = \"binomial\",\n data = rat2\n)\n```\n:::\n\n\n\n\n\n## Output\n\n\\scriptsize\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(rat2.1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\nCall:\nglm(formula = response ~ dose, family = \"binomial\", data = rat2)\n\nCoefficients:\n Estimate Std. Error z value Pr(>|z|) \n(Intercept) 2.3619 0.6719 3.515 0.000439 ***\ndose -0.9448 0.2351 -4.018 5.87e-05 ***\n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\n(Dispersion parameter for binomial family taken to be 1)\n\n Null deviance: 27.530 on 5 degrees of freedom\nResidual deviance: 2.474 on 4 degrees of freedom\nAIC: 18.94\n\nNumber of Fisher Scoring iterations: 4\n```\n:::\n:::\n\n\\normalsize\n\n\n## Predicted survival probs\n\n::: {.cell}\n\n```{.r .cell-code}\nnew <- datagrid(model = rat2.1, dose = 0:5)\ncbind(predictions(rat2.1, newdata = new))\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n \n\n## On a picture\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(rat2.1, newdata = new)) %>% \n select(estimate, conf.low, conf.high, dose) %>% \n ggplot(aes(x = dose, y = estimate, \n ymin = conf.low, ymax = conf.high)) + \n geom_line() + geom_ribbon(alpha = 0.3)\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-revealjs/unnamed-chunk-2-1.png){width=960}\n:::\n:::\n\n\n\n## Comments\n\n\n\n* Significant effect of dose. \n\n* Effect of larger dose is to *decrease* survival probability\n(\"slope\" negative; also see in decreasing predictions.)\n\n* Confidence intervals around prediction narrower (more data).\n\n## Multiple logistic regression\n\n\n* With more than one $x$, works much like multiple regression.\n\n* Example: study of patients with blood poisoning severe enough to warrant surgery. Relate survival to other potential risk factors.\n\n* Variables, 1=present, 0=absent:\n\n\n * survival (death from sepsis=1), response\n * shock\n * malnutrition\n * alcoholism\n * age (as numerical variable)\n * bowel infarction\n\n\n* See what relates to death.\n\n\n\n## Read in data\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \n \"http://ritsokiguess.site/datafiles/sepsis.txt\"\nsepsis <- read_delim(my_url, \" \")\n```\n:::\n\n \n## Make sure categoricals really are\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis %>% \n mutate(across(-age, \\(x) factor(x))) -> sepsis\n```\n:::\n\n\n\n## The data (some)\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis\n```\n\n::: {.cell-output-display}\n`````{=html}\n
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\n`````\n:::\n:::\n\n\n \n\n\n## Fit model\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis.1 <- glm(death ~ shock + malnut + alcohol + age +\n bowelinf,\nfamily = \"binomial\",\ndata = sepsis\n)\n```\n:::\n\n\n \n\n\n## Output part 1\n\n::: {.cell}\n\n```{.r .cell-code}\ntidy(sepsis.1)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n \n\n\n\n* All P-values fairly small\n\n* but `malnut` not significant: remove.\n\n\n\n## Removing `malnut`\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis.2 <- update(sepsis.1, . ~ . - malnut)\ntidy(sepsis.2)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n \n\n\n\n* Everything significant now.\n\n\n\n## Comments\n\n\n* Most of the original $x$'s helped predict death. Only `malnut` seemed not to add anything.\n\n* Removed `malnut` and tried again.\n\n* Everything remaining is significant (though `bowelinf`\nactually became *less* significant).\n\n* All coefficients are *positive*, so having any of the risk\nfactors (or being older)\n*increases* risk of death. \n\n\n## Predictions from model without \"malnut\"\n\n\n* A few (rows of original dataframe) chosen \"at random\":\n\n\\tiny\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis %>% slice(c(4, 1, 2, 11, 32)) -> new\nnew\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n\n```{.r .cell-code}\ncbind(predictions(sepsis.2, newdata = new)) %>% \n select(estimate, conf.low, conf.high, shock:bowelinf)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\\normalsize\n\n\n## Comments \n\n* Survival chances pretty good if no risk factors, though decreasing with age.\n\n* Having more than one risk factor reduces survival chances dramatically.\n\n* Usually good job of predicting survival; sometimes death predicted to survive.\n\n## Another way to assess effects\n\nof `age`:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnew <- datagrid(model = sepsis.2, age = seq(30, 70, 10))\nnew\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n\n## Assessing age effect \n\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(sepsis.2, newdata = new)) %>% \n select(estimate, shock:age)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
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\n`````\n:::\n:::\n\n\n## Assessing shock effect\n\n\\footnotesize\n\n::: {.cell}\n\n```{.r .cell-code}\nnew <- datagrid(shock = c(0, 1), model = sepsis.2)\nnew\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n\n```{.r .cell-code}\ncbind(predictions(sepsis.2, newdata = new)) %>% \n select(estimate, death:shock)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\\normalsize\n\n## Assessing proportionality of odds for age\n\n\n* An assumption we made is that log-odds of survival depends\nlinearly on age.\n\n* Hard to get your head around, but \nbasic idea is that survival chances go continuously up (or down)\nwith age, instead of (for example) going up and then down.\n\n* In this case, seems reasonable, but should check:\n\n\n## Residuals vs.\\ age\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis.2 %>% augment(sepsis) %>% \n ggplot(aes(x = age, y = .resid)) +\n geom_point()\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-revealjs/virtusentella-1.png){width=960}\n:::\n:::\n\n \n## Comments\n\n* No apparent problems overall.\n\n* Confusing \"line\" across: no risk factors, survived. \n\n\n\n## Probability and odds\n\n* For probability $p$, odds is $p/(1-p)$:\n\n\n \\begin{tabular}{rrrl}\n \\hline\n Prob.\\ & Odds & log-odds & in words\\\\\n \\hline\n 0.5 & $0.5/0.5=1/1=1.00$ & $0.00$ & ``even money''\\\\\n 0.1 & $0.1/0.9=1/9=0.11$ & $-2.20$ & ``9 to 1''\\\\\n 0.4 & $0.4/0.6=1/1.5=0.67$ & $-0.41$ & ``1.5 to 1''\\\\\n 0.8 & $0.8/0.2=4/1=4.00$ & $1.39$ & ``4 to 1 on''\\\\\n \\hline\n \\end{tabular}\n\n\n* Gamblers use odds: if you win at 9 to 1 odds, get original\nstake back plus 9 times the stake.\n\n* Probability has to be between 0 and 1\n\n* Odds between 0 and infinity\n\n* *Log*-odds can be anything: any log-odds corresponds to\nvalid probability.\n\n\n\n## Odds ratio\n\n\n* Suppose 90 of 100 men drank wine last week, but only 20 of 100 women.\n\n* Prob of man drinking wine $90/100=0.9$, woman $20/100=0.2$.\n\n* Odds of man drinking wine $0.9/0.1=9$, woman $0.2/0.8=0.25$.\n\n* Ratio of odds is $9/0.25=36$.\n\n* Way of quantifying difference between men and women: ``odds of\ndrinking wine 36 times larger for males than females''. \n\n\n\n## Sepsis data again\n\n\n* Recall prediction of probability of death from risk factors:\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis.2.tidy <- tidy(sepsis.2)\nsepsis.2.tidy\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n \n\n\n* Slopes in column `estimate`.\n\n\n\n## Multiplying the odds\n\n\n* Can interpret slopes by taking \"exp\" of them. We ignore intercept.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis.2.tidy %>% \n mutate(exp_coeff=exp(estimate)) %>% \n select(term, exp_coeff)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n## Interpretation\n\n\\small\n\n::: {.cell}\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\\normalsize\n\n\n* These say \"how much do you *multiply* odds of death by\nfor increase of 1 in corresponding risk factor?\" Or, what is odds\nratio for that factor being 1 (present) vs.\\ 0 (absent)?\n\n* Eg.\\ being alcoholic vs.\\ not increases odds of death by 24 times\n\n* One year older multiplies odds by about 1.1 times. Over 40 years,\nabout $1.09^{40}=31$ times. \n\n\n\n## Odds ratio and relative risk\n\n\n* **Relative risk** is ratio of probabilities.\n\n* Above: 90 of 100 men (0.9) drank wine, 20 of 100 women (0.2).\n\n* Relative risk 0.9/0.2=4.5. (odds ratio was 36).\n\n* When probabilities small, relative risk and odds ratio similar.\n\n* Eg.\\ prob of man having disease 0.02, woman 0.01.\n\n* Relative risk $0.02/0.01=2$.\n\n## Odds ratio vs.\\ relative risk\n\n- Odds for men and for women:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n(od1 <- 0.02 / 0.98) # men\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 0.02040816\n```\n:::\n\n```{.r .cell-code}\n(od2 <- 0.01 / 0.99) # women\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 0.01010101\n```\n:::\n:::\n\n\n- Odds ratio \n\n\n::: {.cell}\n\n```{.r .cell-code}\nod1 / od2\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 2.020408\n```\n:::\n:::\n\n\n\n- Very close to relative risk of 2.\n\n\n## More than 2 response categories\n\n\n* With 2 response categories, model the probability of one, and prob of other is one minus that. So doesn't matter which category you model.\n\n* With more than 2 categories, have to think more carefully about the categories: are they\n\n\n* *ordered*: you can put them in a natural order (like low, medium, high)\n\n* *nominal*: ordering the categories doesn't make sense (like red, green, blue).\n\n\n* R handles both kinds of response; learn how.\n\n\n\n## Ordinal response: the miners\n\n\n* \nModel probability of being in given category *or lower*.\n\n* Example: coal-miners often suffer disease pneumoconiosis. Likelihood of disease believed to be greater \namong miners who have worked longer. \n\n* Severity of disease measured on categorical scale: none,\nmoderate, severe.\n\n## Miners data\n\n* Data are frequencies:\n\n```\nExposure None Moderate Severe\n5.8 98 0 0\n15.0 51 2 1\n21.5 34 6 3\n27.5 35 5 8\n33.5 32 10 9\n39.5 23 7 8\n46.0 12 6 10\n51.5 4 2 5\n```\n\n\n\n\n## Reading the data\n\nData in aligned columns with more than one space between, so: \n\n\\small\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \"http://ritsokiguess.site/datafiles/miners-tab.txt\"\nfreqs <- read_table(my_url)\n```\n:::\n\n\\normalsize\n\n\n## The data\n\n::: {.cell}\n\n```{.r .cell-code}\nfreqs\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n \n\n\n## Tidying \n\n::: {.cell}\n\n```{.r .cell-code}\nfreqs %>%\n pivot_longer(-Exposure, names_to = \"Severity\", values_to = \"Freq\") %>%\n mutate(Severity = fct_inorder(Severity)) -> miners\n```\n:::\n\n\n \n\n\n## Result\n\n\\tiny\n\n::: {.cell}\n\n```{.r .cell-code}\nminers\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\\normalsize\n \n\n\n## Plot proportions against exposure\n\n\\small\n\n::: {.cell}\n\n```{.r .cell-code}\nminers %>% \n group_by(Exposure) %>% \n mutate(proportion = Freq / sum(Freq)) -> prop\nggplot(prop, aes(x = Exposure, y = proportion,\n colour = Severity)) + \n geom_point() + geom_smooth(se = F)\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-revealjs/bLogistic-28-1.png){width=960}\n:::\n:::\n\n\\normalsize\n\n\n## Reminder of data setup\n\\footnotesize\n\n::: {.cell}\n\n```{.r .cell-code}\nminers\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n \n\\normalsize\n\n\n\n## Fitting ordered logistic model\n\nUse function `polr` from package `MASS`. Like `glm`.\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsev.1 <- polr(Severity ~ Exposure,\n weights = Freq,\n data = miners\n)\n```\n:::\n\n \n\n\n## Output: not very illuminating\n\n\n\\scriptsize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsev.1 <- polr(Severity ~ Exposure,\n weights = Freq,\n data = miners,\n Hess = TRUE\n)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(sev.1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nCall:\npolr(formula = Severity ~ Exposure, data = miners, weights = Freq, \n Hess = TRUE)\n\nCoefficients:\n Value Std. Error t value\nExposure 0.0959 0.01194 8.034\n\nIntercepts:\n Value Std. Error t value\nNone|Moderate 3.9558 0.4097 9.6558\nModerate|Severe 4.8690 0.4411 11.0383\n\nResidual Deviance: 416.9188 \nAIC: 422.9188 \n```\n:::\n:::\n\n\\normalsize\n \n\n## Does exposure have an effect?\nFit model without `Exposure`, and compare\nusing `anova`. Note `1` for model with just intercept:\n\n\n::: {.cell}\n\n:::\n\n\n \n\n\\small\n\n::: {.cell}\n\n```{.r .cell-code}\nsev.0 <- polr(Severity ~ 1, weights = Freq, data = miners)\nanova(sev.0, sev.1)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\\normalsize\n\nExposure definitely has effect on severity of disease. \n\n\n## Another way\n\n\n* What (if anything) can we drop from model with `exposure`?\n\n::: {.cell}\n\n```{.r .cell-code}\ndrop1(sev.1, test = \"Chisq\")\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n \n\n\n* Nothing. Exposure definitely has effect.\n\n\n\n## Predicted probabilities 1/2\n\n\n::: {.cell}\n\n```{.r .cell-code}\nfreqs %>% select(Exposure) -> new\nnew\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n\n## Predicted probabilities 2/2\n\n\\small\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(sev.1, newdata = new)) %>%\n select(group, estimate, Exposure) %>% \n pivot_wider(names_from = group, values_from = estimate)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\\normalsize\n\n## Plot of predicted probabilities\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_cap(model = sev.1, condition = c(\"Exposure\", \"group\"),\n type = \"probs\") +\n geom_point(data = prop, aes(x = Exposure, y = proportion, \n colour = Severity)) -> ggg\n```\n:::\n\n\n## The graph\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggg\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-revealjs/unnamed-chunk-11-1.png){width=960}\n:::\n:::\n\n\n\n## Comments\n\n\n* Model appears to match data well enough.\n\n- As exposure goes up, prob of None\ngoes down, Severe goes up (sharply for high exposure).\n\n- So more exposure means worse disease.\n\n\n## Unordered responses\n\n\n* With unordered (nominal) responses, can use *generalized logit*.\n\n* Example: 735 people, record age and sex (male 0, female 1), which of 3 brands of some product preferred.\n\n* Data in `mlogit.csv` separated by commas (so\n`read_csv` will work):\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \"http://ritsokiguess.site/datafiles/mlogit.csv\"\nbrandpref <- read_csv(my_url)\n```\n:::\n\n \n\n\n\n\n## The data (some)\n\n::: {.cell}\n\n```{.r .cell-code}\nbrandpref\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n \n\n\n## Bashing into shape\n\n\n* `sex` and `brand` not meaningful as numbers, so\nturn into factors:\n\n::: {.cell}\n\n```{.r .cell-code}\nbrandpref %>%\n mutate(sex = ifelse(sex == 1, \"female\", \"male\"), \n sex = factor(sex),\n brand = factor(brand)\n ) -> brandpref\n```\n:::\n\n \n \n## Fitting model \n\n* We use `multinom` from package `nnet`. Works\nlike `polr`.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbrands.1 <- multinom(brand ~ age + sex, data = brandpref)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# weights: 12 (6 variable)\ninitial value 807.480032 \niter 10 value 702.990572\nfinal value 702.970704 \nconverged\n```\n:::\n:::\n\n \n\n\n## Can we drop anything?\n\n\n* Unfortunately `drop1` seems not to work:\n\n::: {.cell}\n\n```{.r .cell-code}\ndrop1(brands.1, test = \"Chisq\", trace = 0)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\ntrying - age \n```\n:::\n\n::: {.cell-output .cell-output-error}\n```\nError in if (trace) {: argument is not interpretable as logical\n```\n:::\n:::\n\n\n* So, fall back on fitting model without what you want to test, and\ncomparing using `anova`. \n\n\n\n## Do age/sex help predict brand? 1/3\n\nFit models without each of age and sex:\n\n::: {.cell}\n\n```{.r .cell-code}\nbrands.2 <- multinom(brand ~ age, data = brandpref)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# weights: 9 (4 variable)\ninitial value 807.480032 \niter 10 value 706.796323\niter 10 value 706.796322\nfinal value 706.796322 \nconverged\n```\n:::\n\n```{.r .cell-code}\nbrands.3 <- multinom(brand ~ sex, data = brandpref)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# weights: 9 (4 variable)\ninitial value 807.480032 \nfinal value 791.861266 \nconverged\n```\n:::\n:::\n\n \n\n\n## Do age/sex help predict brand? 2/3\n\n\\scriptsize\n\n::: {.cell}\n\n```{.r .cell-code}\nanova(brands.2, brands.1)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n\n```{.r .cell-code}\nanova(brands.3, brands.1)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\\normalsize\n\n\n## Do age/sex help predict brand? 3/3\n\n\n* `age` definitely significant (second `anova`)\n\n* `sex` significant also (first `anova`), though P-value less dramatic\n\n* Keep both.\n- Expect to see a large effect of `age`, and a smaller one of `sex`.\n\n\n\n\n## Another way to build model\n\n\n* Start from model with everything and run `step`:\n\n\n::: {.cell}\n\n:::\n\n\n\n\\footnotesize\n\n::: {.cell}\n\n```{.r .cell-code}\nstep(brands.1, trace = 0)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\ntrying - age \ntrying - sex \n```\n:::\n\n::: {.cell-output .cell-output-stdout}\n```\nCall:\nmultinom(formula = brand ~ age + sex)\n\nCoefficients:\n (Intercept) age sexmale\n2 -11.25127 0.3682202 -0.5237736\n3 -22.25571 0.6859149 -0.4658215\n\nResidual Deviance: 1405.941 \nAIC: 1417.941 \n```\n:::\n:::\n\n\\normalsize\n \n\n* Final model contains both `age` and `sex` so neither\ncould be removed.\n\n\n## Making predictions\n\nFind age 5-number summary, and the two sexes:\n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(brandpref)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n brand sex age \n 1:207 female:466 Min. :24.0 \n 2:307 male :269 1st Qu.:32.0 \n 3:221 Median :32.0 \n Mean :32.9 \n 3rd Qu.:34.0 \n Max. :38.0 \n```\n:::\n:::\n\n\nSpace the ages out a bit for prediction (see over).\n\n\\normalsize\n\n## Combinations\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnew <- datagrid(age = c(24, 30, 33, 35, 38), \n sex = c(\"female\", \"male\"), model = brands.1)\nnew\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n## The predictions\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(brands.1, newdata = new)) %>%\n select(group, estimate, age, sex) %>% \n pivot_wider(names_from = group, values_from = estimate)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n## Comments\n\n* Young males prefer brand 1, \nbut older males prefer brand 3.\n\n* Females similar, but like brand 1 less and\nbrand 2 more.\n\n- A clear `brand` effect, but the `sex` effect is less clear. \n\n## Making a plot\n\n- `plot_cap` doesn't quite work\n- so don't draw, edit, *then* make graph:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_cap(brands.1, condition = c(\"age\", \"brand\", \"sex\"), \n type = \"probs\", draw = FALSE) %>% \n ggplot(aes(x = age, y = estimate, colour = group, \n linetype = sex)) +\n geom_line() -> g\n```\n:::\n\n\n## The graph\n\n\n::: {.cell}\n\n```{.r .cell-code}\ng\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-revealjs/unnamed-chunk-16-1.png){width=960}\n:::\n:::\n\n \n\n## Digesting the plot\n\n\n* Brand vs.\\ age: younger people (of both genders) prefer brand\n1, but older people (of both genders) prefer brand 3. (Explains\nsignificant age effect.)\n\n* Brand vs.\\ sex: females (solid) like brand 1 less than males\n(dashed), like brand 2 more (for all ages). \n\n* Not much brand difference between genders (solid and dashed\nlines of same colours close), but enough to be significant.\n\n* Model didn't include interaction, so modelled effect of gender\non brand same for each age, modelled effect of age same for each\ngender. (See also later.) \n\n\n## Alternative data format\n\nSummarize all people of same brand preference, same sex, same age on one line of data file with frequency on end:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbrandpref\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n\n```\n1 0 24 1\n1 0 26 2\n1 0 27 4\n1 0 28 4\n1 0 29 7\n1 0 30 3\n...\n```\n\nWhole data set in 65 lines not 735! But how?\n\n\n## Getting alternative data format\n\n::: {.cell}\n\n```{.r .cell-code}\nbrandpref %>%\n group_by(age, sex, brand) %>%\n summarize(Freq = n()) %>%\n ungroup() -> b\nb\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n \n\n## Fitting models, almost the same\n\n\n* Just have to remember `weights` to incorporate\nfrequencies.\n\n* Otherwise `multinom` assumes you have just 1 obs\non each line!\n\n* Again turn (numerical) `sex` and `brand` into factors:\n\n\\footnotesize\n\n::: {.cell}\n\n```{.r .cell-code}\nb %>%\n mutate(sex = factor(sex)) %>%\n mutate(brand = factor(brand)) -> bf\nb.1 <- multinom(brand ~ age + sex, data = bf, weights = Freq)\nb.2 <- multinom(brand ~ age, data = bf, weights = Freq)\n```\n:::\n\n\\normalsize\n\n\n\n## P-value for `sex` identical\n\n\\footnotesize\n\n::: {.cell}\n\n```{.r .cell-code}\nanova(b.2, b.1)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\\normalsize\n\nSame P-value as before, so we haven't changed anything important.\n\n\n\n\n## Trying interaction between age and gender\n\n\n::: {.cell}\n\n:::\n\n\n \n\\scriptsize\n\n::: {.cell}\n\n```{.r .cell-code}\nbrands.4 <- update(brands.1, . ~ . + age:sex)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# weights: 15 (8 variable)\ninitial value 807.480032 \niter 10 value 703.191146\niter 20 value 702.572260\niter 30 value 702.570900\niter 30 value 702.570893\niter 30 value 702.570893\nfinal value 702.570893 \nconverged\n```\n:::\n\n```{.r .cell-code}\nanova(brands.1, brands.4)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n \n\\normalsize\n\n* No evidence that effect of age on brand preference differs for\nthe two genders.\n\n## Make graph again\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_cap(brands.4, condition = c(\"age\", \"brand\", \"sex\"), \n type = \"probs\", draw = FALSE) %>% \n ggplot(aes(x = age, y = estimate, colour = group, \n linetype = sex)) +\n geom_line() -> g4\n```\n:::\n\n\n\n## Not much difference in the graph\n\n\n::: {.cell}\n\n```{.r .cell-code}\ng4\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-revealjs/unnamed-chunk-19-1.png){width=960}\n:::\n:::\n\n\n## Compare model without interaction\n\n\n::: {.cell}\n\n```{.r .cell-code}\ng\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-revealjs/unnamed-chunk-20-1.png){width=960}\n:::\n:::\n", + "markdown": "---\ntitle: \"Logistic Regression\"\n---\n\n\n\n## Logistic regression\n\n\n* When response variable is measured/counted, regression can work well.\n\n* But what if response is yes/no, lived/died, success/failure?\n\n* Model *probability* of success.\n\n* Probability must be between 0 and 1; need method that ensures this.\n\n* *Logistic regression* does this. In R, is a\n*generalized linear model* with binomial \"family\": \n\n::: {.cell}\n\n```{.r .cell-code}\nglm(y ~ x, family=\"binomial\")\n```\n:::\n\n\n\n* Begin with simplest case.\n\n\n\n## Packages\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(MASS)\nlibrary(tidyverse)\nlibrary(marginaleffects)\nlibrary(broom)\nlibrary(nnet)\nlibrary(conflicted)\nconflict_prefer(\"select\", \"dplyr\")\nconflict_prefer(\"filter\", \"dplyr\")\nconflict_prefer(\"rename\", \"dplyr\")\nconflict_prefer(\"summarize\", \"dplyr\")\n```\n:::\n\n\n \n\n\n## The rats, part 1\n\n\n* Rats given dose of some poison; either live or die:\n\n\\small\n```\ndose status\n0 lived\n1 died\n2 lived\n3 lived\n4 died\n5 died\n```\n\n\\normalsize\n\n## Read in: \n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \"http://ritsokiguess.site/datafiles/rat.txt\"\nrats <- read_delim(my_url, \" \")\nrats\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n \n\n\n## Basic logistic regression\n\n\n* Make response into a factor first:\n\n\\small\n\n::: {.cell}\n\n```{.r .cell-code}\nrats2 <- rats %>% mutate(status = factor(status))\n```\n:::\n\n\\normalsize\n \n\n\n* then fit model:\n\n\\small\n\n::: {.cell}\n\n```{.r .cell-code}\nstatus.1 <- glm(status ~ dose, family = \"binomial\", data = rats2)\n```\n:::\n\n\\normalsize\n \n\n \n\n\n## Output\n\n\\scriptsize\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(status.1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\nCall:\nglm(formula = status ~ dose, family = \"binomial\", data = rats2)\n\nCoefficients:\n Estimate Std. Error z value Pr(>|z|)\n(Intercept) 1.6841 1.7979 0.937 0.349\ndose -0.6736 0.6140 -1.097 0.273\n\n(Dispersion parameter for binomial family taken to be 1)\n\n Null deviance: 8.3178 on 5 degrees of freedom\nResidual deviance: 6.7728 on 4 degrees of freedom\nAIC: 10.773\n\nNumber of Fisher Scoring iterations: 4\n```\n:::\n:::\n\n\\normalsize\n\n\n## Interpreting the output\n\n\n* Like (multiple) regression, get\ntests of significance of individual $x$'s\n\n* Here not significant (only 6 observations).\n\n* \"Slope\" for dose is negative, meaning that as dose increases, probability of event modelled (survival) decreases.\n\n\n\n\n\n## Output part 2: predicted survival probs\n\n\\small\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(status.1)) %>% \n select(dose, estimate)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\\normalsize \n\n## On a graph\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_predictions(status.1, condition = \"dose\")\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-revealjs/unnamed-chunk-1-1.png){width=960}\n:::\n:::\n\n\n\n## The rats, more\n\n\n* More realistic: more rats at each dose (say 10).\n\n* Listing each rat on one line makes a big data file.\n\n* Use format below: dose, number of survivals, number of deaths.\n\n```\n\ndose lived died\n0 10 0\n1 7 3 \n2 6 4 \n3 4 6 \n4 2 8 \n5 1 9 \n\n```\n\n\n* 6 lines of data correspond to 60 actual rats.\n\n* Saved in `rat2.txt`.\n\n\n\n## These data\n\n\\footnotesize\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \"http://ritsokiguess.site/datafiles/rat2.txt\"\nrat2 <- read_delim(my_url, \" \")\nrat2\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\\normalsize\n\n\n## Create response matrix:\n\n- Each row contains *multiple* observations.\n- Create *two-column* response:\n - \\#survivals in first column, \n - \\#deaths in second.\n\n\n\\footnotesize\n\n::: {.cell}\n\n```{.r .cell-code}\nresponse <- with(rat2, cbind(lived, died))\nresponse\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n lived died\n[1,] 10 0\n[2,] 7 3\n[3,] 6 4\n[4,] 4 6\n[5,] 2 8\n[6,] 1 9\n```\n:::\n:::\n\n\\normalsize\n\n- Response is R `matrix`:\n\n\\scriptsize\n\n::: {.cell}\n\n```{.r .cell-code}\nclass(response)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] \"matrix\" \"array\" \n```\n:::\n:::\n\n\\normalsize\n\n \n## Fit logistic regression\n\n- using response you just made:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrat2.1 <- glm(response ~ dose,\n family = \"binomial\",\n data = rat2\n)\n```\n:::\n\n\n\n\n\n## Output\n\n\\scriptsize\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(rat2.1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\nCall:\nglm(formula = response ~ dose, family = \"binomial\", data = rat2)\n\nCoefficients:\n Estimate Std. Error z value Pr(>|z|) \n(Intercept) 2.3619 0.6719 3.515 0.000439 ***\ndose -0.9448 0.2351 -4.018 5.87e-05 ***\n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\n(Dispersion parameter for binomial family taken to be 1)\n\n Null deviance: 27.530 on 5 degrees of freedom\nResidual deviance: 2.474 on 4 degrees of freedom\nAIC: 18.94\n\nNumber of Fisher Scoring iterations: 4\n```\n:::\n:::\n\n\\normalsize\n\n\n## Predicted survival probs\n\n::: {.cell}\n\n```{.r .cell-code}\nnew <- datagrid(model = rat2.1, dose = 0:5)\ncbind(predictions(rat2.1, newdata = new))\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n \n\n## On a picture\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(rat2.1, newdata = new)) %>% \n select(estimate, conf.low, conf.high, dose) %>% \n ggplot(aes(x = dose, y = estimate, \n ymin = conf.low, ymax = conf.high)) + \n geom_line() + geom_ribbon(alpha = 0.3)\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-revealjs/unnamed-chunk-2-1.png){width=960}\n:::\n:::\n\n\n\n## Comments\n\n\n\n* Significant effect of dose. \n\n* Effect of larger dose is to *decrease* survival probability\n(\"slope\" negative; also see in decreasing predictions.)\n\n* Confidence intervals around prediction narrower (more data).\n\n## Multiple logistic regression\n\n\n* With more than one $x$, works much like multiple regression.\n\n* Example: study of patients with blood poisoning severe enough to warrant surgery. Relate survival to other potential risk factors.\n\n* Variables, 1=present, 0=absent:\n\n\n * survival (death from sepsis=1), response\n * shock\n * malnutrition\n * alcoholism\n * age (as numerical variable)\n * bowel infarction\n\n\n* See what relates to death.\n\n\n\n## Read in data\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \n \"http://ritsokiguess.site/datafiles/sepsis.txt\"\nsepsis <- read_delim(my_url, \" \")\n```\n:::\n\n \n## Make sure categoricals really are\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis %>% \n mutate(across(-age, \\(x) factor(x))) -> sepsis\n```\n:::\n\n\n\n## The data (some)\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n \n\n\n## Fit model\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis.1 <- glm(death ~ shock + malnut + alcohol + age +\n bowelinf,\nfamily = \"binomial\",\ndata = sepsis\n)\n```\n:::\n\n\n \n\n\n## Output part 1\n\n::: {.cell}\n\n```{.r .cell-code}\ntidy(sepsis.1)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n \n\n\n\n* All P-values fairly small\n\n* but `malnut` not significant: remove.\n\n\n\n## Removing `malnut`\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis.2 <- update(sepsis.1, . ~ . - malnut)\ntidy(sepsis.2)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n \n\n\n\n* Everything significant now.\n\n\n\n## Comments\n\n\n* Most of the original $x$'s helped predict death. Only `malnut` seemed not to add anything.\n\n* Removed `malnut` and tried again.\n\n* Everything remaining is significant (though `bowelinf`\nactually became *less* significant).\n\n* All coefficients are *positive*, so having any of the risk\nfactors (or being older)\n*increases* risk of death. \n\n\n## Predictions from model without \"malnut\"\n\n\n* A few (rows of original dataframe) chosen \"at random\":\n\n\\tiny\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis %>% slice(c(4, 1, 2, 11, 32)) -> new\nnew\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n\n```{.r .cell-code}\ncbind(predictions(sepsis.2, newdata = new)) %>% \n select(estimate, conf.low, conf.high, shock:bowelinf)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\\normalsize\n\n\n## Comments \n\n* Survival chances pretty good if no risk factors, though decreasing with age.\n\n* Having more than one risk factor reduces survival chances dramatically.\n\n* Usually good job of predicting survival; sometimes death predicted to survive.\n\n## Another way to assess effects\n\nof `age`:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnew <- datagrid(model = sepsis.2, age = seq(30, 70, 10))\nnew\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n\n## Assessing age effect \n\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(sepsis.2, newdata = new)) %>% \n select(estimate, shock:age)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n## Assessing shock effect\n\n\\footnotesize\n\n::: {.cell}\n\n```{.r .cell-code}\nnew <- datagrid(shock = c(0, 1), model = sepsis.2)\nnew\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n\n```{.r .cell-code}\ncbind(predictions(sepsis.2, newdata = new)) %>% \n select(estimate, death:shock)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\\normalsize\n\n## Assessing proportionality of odds for age\n\n\n* An assumption we made is that log-odds of survival depends\nlinearly on age.\n\n* Hard to get your head around, but \nbasic idea is that survival chances go continuously up (or down)\nwith age, instead of (for example) going up and then down.\n\n* In this case, seems reasonable, but should check:\n\n\n## Residuals vs.\\ age\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis.2 %>% augment(sepsis) %>% \n ggplot(aes(x = age, y = .resid)) +\n geom_point()\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-revealjs/virtusentella-1.png){width=960}\n:::\n:::\n\n \n## Comments\n\n* No apparent problems overall.\n\n* Confusing \"line\" across: no risk factors, survived. \n\n\n\n## Probability and odds\n\n* For probability $p$, odds is $p/(1-p)$:\n\n\n \\begin{tabular}{rrrl}\n \\hline\n Prob.\\ & Odds & log-odds & in words\\\\\n \\hline\n 0.5 & $0.5/0.5=1/1=1.00$ & $0.00$ & ``even money''\\\\\n 0.1 & $0.1/0.9=1/9=0.11$ & $-2.20$ & ``9 to 1''\\\\\n 0.4 & $0.4/0.6=1/1.5=0.67$ & $-0.41$ & ``1.5 to 1''\\\\\n 0.8 & $0.8/0.2=4/1=4.00$ & $1.39$ & ``4 to 1 on''\\\\\n \\hline\n \\end{tabular}\n\n\n* Gamblers use odds: if you win at 9 to 1 odds, get original\nstake back plus 9 times the stake.\n\n* Probability has to be between 0 and 1\n\n* Odds between 0 and infinity\n\n* *Log*-odds can be anything: any log-odds corresponds to\nvalid probability.\n\n\n\n## Odds ratio\n\n\n* Suppose 90 of 100 men drank wine last week, but only 20 of 100 women.\n\n* Prob of man drinking wine $90/100=0.9$, woman $20/100=0.2$.\n\n* Odds of man drinking wine $0.9/0.1=9$, woman $0.2/0.8=0.25$.\n\n* Ratio of odds is $9/0.25=36$.\n\n* Way of quantifying difference between men and women: ``odds of\ndrinking wine 36 times larger for males than females''. \n\n\n\n## Sepsis data again\n\n\n* Recall prediction of probability of death from risk factors:\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis.2.tidy <- tidy(sepsis.2)\nsepsis.2.tidy\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n \n\n\n* Slopes in column `estimate`.\n\n\n\n## Multiplying the odds\n\n\n* Can interpret slopes by taking \"exp\" of them. We ignore intercept.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis.2.tidy %>% \n mutate(exp_coeff=exp(estimate)) %>% \n select(term, exp_coeff)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n## Interpretation\n\n\\small\n\n::: {.cell}\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\\normalsize\n\n\n* These say \"how much do you *multiply* odds of death by\nfor increase of 1 in corresponding risk factor?\" Or, what is odds\nratio for that factor being 1 (present) vs.\\ 0 (absent)?\n\n* Eg.\\ being alcoholic vs.\\ not increases odds of death by 24 times\n\n* One year older multiplies odds by about 1.1 times. Over 40 years,\nabout $1.09^{40}=31$ times. \n\n\n\n## Odds ratio and relative risk\n\n\n* **Relative risk** is ratio of probabilities.\n\n* Above: 90 of 100 men (0.9) drank wine, 20 of 100 women (0.2).\n\n* Relative risk 0.9/0.2=4.5. (odds ratio was 36).\n\n* When probabilities small, relative risk and odds ratio similar.\n\n* Eg.\\ prob of man having disease 0.02, woman 0.01.\n\n* Relative risk $0.02/0.01=2$.\n\n## Odds ratio vs.\\ relative risk\n\n- Odds for men and for women:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n(od1 <- 0.02 / 0.98) # men\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 0.02040816\n```\n:::\n\n```{.r .cell-code}\n(od2 <- 0.01 / 0.99) # women\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 0.01010101\n```\n:::\n:::\n\n\n- Odds ratio \n\n\n::: {.cell}\n\n```{.r .cell-code}\nod1 / od2\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 2.020408\n```\n:::\n:::\n\n\n\n- Very close to relative risk of 2.\n\n\n## More than 2 response categories\n\n\n* With 2 response categories, model the probability of one, and prob of other is one minus that. So doesn't matter which category you model.\n\n* With more than 2 categories, have to think more carefully about the categories: are they\n\n\n* *ordered*: you can put them in a natural order (like low, medium, high)\n\n* *nominal*: ordering the categories doesn't make sense (like red, green, blue).\n\n\n* R handles both kinds of response; learn how.\n\n\n\n## Ordinal response: the miners\n\n\n* \nModel probability of being in given category *or lower*.\n\n* Example: coal-miners often suffer disease pneumoconiosis. Likelihood of disease believed to be greater \namong miners who have worked longer. \n\n* Severity of disease measured on categorical scale: none,\nmoderate, severe.\n\n## Miners data\n\n* Data are frequencies:\n\n```\nExposure None Moderate Severe\n5.8 98 0 0\n15.0 51 2 1\n21.5 34 6 3\n27.5 35 5 8\n33.5 32 10 9\n39.5 23 7 8\n46.0 12 6 10\n51.5 4 2 5\n```\n\n\n\n\n## Reading the data\n\nData in aligned columns with more than one space between, so: \n\n\\small\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \"http://ritsokiguess.site/datafiles/miners-tab.txt\"\nfreqs <- read_table(my_url)\n```\n:::\n\n\\normalsize\n\n\n## The data\n\n::: {.cell}\n\n```{.r .cell-code}\nfreqs\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n \n\n\n## Tidying \n\n::: {.cell}\n\n```{.r .cell-code}\nfreqs %>%\n pivot_longer(-Exposure, names_to = \"Severity\", values_to = \"Freq\") %>%\n mutate(Severity = fct_inorder(Severity)) -> miners\n```\n:::\n\n\n \n\n\n## Result\n\n\\tiny\n\n::: {.cell}\n\n```{.r .cell-code}\nminers\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\\normalsize\n \n\n\n## Plot proportions against exposure\n\n\\small\n\n::: {.cell}\n\n```{.r .cell-code}\nminers %>% \n group_by(Exposure) %>% \n mutate(proportion = Freq / sum(Freq)) -> prop\nggplot(prop, aes(x = Exposure, y = proportion,\n colour = Severity)) + \n geom_point() + geom_smooth(se = F)\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-revealjs/bLogistic-28-1.png){width=960}\n:::\n:::\n\n\\normalsize\n\n\n## Reminder of data setup\n\\footnotesize\n\n::: {.cell}\n\n```{.r .cell-code}\nminers\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n \n\\normalsize\n\n\n\n## Fitting ordered logistic model\n\nUse function `polr` from package `MASS`. Like `glm`.\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsev.1 <- polr(Severity ~ Exposure,\n weights = Freq,\n data = miners\n)\n```\n:::\n\n \n\n\n## Output: not very illuminating\n\n\n\\scriptsize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsev.1 <- polr(Severity ~ Exposure,\n weights = Freq,\n data = miners,\n Hess = TRUE\n)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(sev.1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nCall:\npolr(formula = Severity ~ Exposure, data = miners, weights = Freq, \n Hess = TRUE)\n\nCoefficients:\n Value Std. Error t value\nExposure 0.0959 0.01194 8.034\n\nIntercepts:\n Value Std. Error t value\nNone|Moderate 3.9558 0.4097 9.6558\nModerate|Severe 4.8690 0.4411 11.0383\n\nResidual Deviance: 416.9188 \nAIC: 422.9188 \n```\n:::\n:::\n\n\\normalsize\n \n\n## Does exposure have an effect?\nFit model without `Exposure`, and compare\nusing `anova`. Note `1` for model with just intercept:\n\n\n::: {.cell}\n\n:::\n\n\n \n\n\\small\n\n::: {.cell}\n\n```{.r .cell-code}\nsev.0 <- polr(Severity ~ 1, weights = Freq, data = miners)\nanova(sev.0, sev.1)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\\normalsize\n\nExposure definitely has effect on severity of disease. \n\n\n## Another way\n\n\n* What (if anything) can we drop from model with `exposure`?\n\n::: {.cell}\n\n```{.r .cell-code}\ndrop1(sev.1, test = \"Chisq\")\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n \n\n\n* Nothing. Exposure definitely has effect.\n\n\n\n## Predicted probabilities 1/2\n\n\n::: {.cell}\n\n```{.r .cell-code}\nfreqs %>% select(Exposure) -> new\nnew\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n\n## Predicted probabilities 2/2\n\n\\small\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(sev.1, newdata = new)) %>%\n select(group, estimate, Exposure) %>% \n pivot_wider(names_from = group, values_from = estimate)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\\normalsize\n\n## Plot of predicted probabilities\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_predictions(model = sev.1, condition = c(\"Exposure\", \"group\"),\n type = \"probs\") +\n geom_point(data = prop, aes(x = Exposure, y = proportion, \n colour = Severity)) -> ggg\n```\n:::\n\n\n## The graph\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggg\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-revealjs/unnamed-chunk-11-1.png){width=960}\n:::\n:::\n\n\n\n## Comments\n\n\n* Model appears to match data well enough.\n\n- As exposure goes up, prob of None\ngoes down, Severe goes up (sharply for high exposure).\n\n- So more exposure means worse disease.\n\n\n## Unordered responses\n\n\n* With unordered (nominal) responses, can use *generalized logit*.\n\n* Example: 735 people, record age and sex (male 0, female 1), which of 3 brands of some product preferred.\n\n* Data in `mlogit.csv` separated by commas (so\n`read_csv` will work):\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \"http://ritsokiguess.site/datafiles/mlogit.csv\"\nbrandpref <- read_csv(my_url)\n```\n:::\n\n \n\n\n\n\n## The data (some)\n\n::: {.cell}\n\n```{.r .cell-code}\nbrandpref\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n \n\n\n## Bashing into shape\n\n\n* `sex` and `brand` not meaningful as numbers, so\nturn into factors:\n\n::: {.cell}\n\n```{.r .cell-code}\nbrandpref %>%\n mutate(sex = ifelse(sex == 1, \"female\", \"male\"), \n sex = factor(sex),\n brand = factor(brand)\n ) -> brandpref\n```\n:::\n\n \n \n## Fitting model \n\n* We use `multinom` from package `nnet`. Works\nlike `polr`.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbrands.1 <- multinom(brand ~ age + sex, data = brandpref)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# weights: 12 (6 variable)\ninitial value 807.480032 \niter 10 value 702.990572\nfinal value 702.970704 \nconverged\n```\n:::\n:::\n\n \n\n\n## Can we drop anything?\n\n\n* Unfortunately `drop1` seems not to work:\n\n::: {.cell}\n\n```{.r .cell-code}\ndrop1(brands.1, test = \"Chisq\", trace = 0)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\ntrying - age \n```\n:::\n\n::: {.cell-output .cell-output-error}\n```\nError in if (trace) {: argument is not interpretable as logical\n```\n:::\n:::\n\n\n* So, fall back on fitting model without what you want to test, and\ncomparing using `anova`. \n\n\n\n## Do age/sex help predict brand? 1/3\n\nFit models without each of age and sex:\n\n::: {.cell}\n\n```{.r .cell-code}\nbrands.2 <- multinom(brand ~ age, data = brandpref)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# weights: 9 (4 variable)\ninitial value 807.480032 \niter 10 value 706.796323\niter 10 value 706.796322\nfinal value 706.796322 \nconverged\n```\n:::\n\n```{.r .cell-code}\nbrands.3 <- multinom(brand ~ sex, data = brandpref)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# weights: 9 (4 variable)\ninitial value 807.480032 \nfinal value 791.861266 \nconverged\n```\n:::\n:::\n\n \n\n\n## Do age/sex help predict brand? 2/3\n\n\\scriptsize\n\n::: {.cell}\n\n```{.r .cell-code}\nanova(brands.2, brands.1)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n\n```{.r .cell-code}\nanova(brands.3, brands.1)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\\normalsize\n\n\n## Do age/sex help predict brand? 3/3\n\n\n* `age` definitely significant (second `anova`)\n\n* `sex` significant also (first `anova`), though P-value less dramatic\n\n* Keep both.\n- Expect to see a large effect of `age`, and a smaller one of `sex`.\n\n\n\n\n## Another way to build model\n\n\n* Start from model with everything and run `step`:\n\n\n::: {.cell}\n\n:::\n\n\n\n\\footnotesize\n\n::: {.cell}\n\n```{.r .cell-code}\nstep(brands.1, trace = 0)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\ntrying - age \ntrying - sex \n```\n:::\n\n::: {.cell-output .cell-output-stdout}\n```\nCall:\nmultinom(formula = brand ~ age + sex)\n\nCoefficients:\n (Intercept) age sexmale\n2 -11.25127 0.3682202 -0.5237736\n3 -22.25571 0.6859149 -0.4658215\n\nResidual Deviance: 1405.941 \nAIC: 1417.941 \n```\n:::\n:::\n\n\\normalsize\n \n\n* Final model contains both `age` and `sex` so neither\ncould be removed.\n\n\n## Making predictions\n\nFind age 5-number summary, and the two sexes:\n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(brandpref)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n brand sex age \n 1:207 female:466 Min. :24.0 \n 2:307 male :269 1st Qu.:32.0 \n 3:221 Median :32.0 \n Mean :32.9 \n 3rd Qu.:34.0 \n Max. :38.0 \n```\n:::\n:::\n\n\nSpace the ages out a bit for prediction (see over).\n\n\\normalsize\n\n## Combinations\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnew <- datagrid(age = c(24, 30, 33, 35, 38), \n sex = c(\"female\", \"male\"), model = brands.1)\nnew\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n## The predictions\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(brands.1, newdata = new)) %>%\n select(group, estimate, age, sex) %>% \n pivot_wider(names_from = group, values_from = estimate)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n## Comments\n\n* Young males prefer brand 1, \nbut older males prefer brand 3.\n\n* Females similar, but like brand 1 less and\nbrand 2 more.\n\n- A clear `brand` effect, but the `sex` effect is less clear. \n\n## Making a plot\n\n- `plot_predictions` doesn't quite work\n- so don't draw, edit, *then* make graph:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_predictions(brands.1, condition = c(\"age\", \"brand\", \"sex\"), \n type = \"probs\", draw = FALSE) %>% \n ggplot(aes(x = age, y = estimate, colour = group, \n linetype = sex)) +\n geom_line() -> g\n```\n:::\n\n\n## The graph\n\n\n::: {.cell}\n\n```{.r .cell-code}\ng\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-revealjs/unnamed-chunk-16-1.png){width=960}\n:::\n:::\n\n \n\n## Digesting the plot\n\n\n* Brand vs.\\ age: younger people (of both genders) prefer brand\n1, but older people (of both genders) prefer brand 3. (Explains\nsignificant age effect.)\n\n* Brand vs.\\ sex: females (solid) like brand 1 less than males\n(dashed), like brand 2 more (for all ages). \n\n* Not much brand difference between genders (solid and dashed\nlines of same colours close), but enough to be significant.\n\n* Model didn't include interaction, so modelled effect of gender\non brand same for each age, modelled effect of age same for each\ngender. (See also later.) \n\n\n## Alternative data format\n\nSummarize all people of same brand preference, same sex, same age on one line of data file with frequency on end:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbrandpref\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n\n```\n1 0 24 1\n1 0 26 2\n1 0 27 4\n1 0 28 4\n1 0 29 7\n1 0 30 3\n...\n```\n\nWhole data set in 65 lines not 735! But how?\n\n\n## Getting alternative data format\n\n::: {.cell}\n\n```{.r .cell-code}\nbrandpref %>%\n group_by(age, sex, brand) %>%\n summarize(Freq = n()) %>%\n ungroup() -> b\nb\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n \n\n## Fitting models, almost the same\n\n\n* Just have to remember `weights` to incorporate\nfrequencies.\n\n* Otherwise `multinom` assumes you have just 1 obs\non each line!\n\n* Again turn (numerical) `sex` and `brand` into factors:\n\n\\footnotesize\n\n::: {.cell}\n\n```{.r .cell-code}\nb %>%\n mutate(sex = factor(sex)) %>%\n mutate(brand = factor(brand)) -> bf\nb.1 <- multinom(brand ~ age + sex, data = bf, weights = Freq)\nb.2 <- multinom(brand ~ age, data = bf, weights = Freq)\n```\n:::\n\n\\normalsize\n\n\n\n## P-value for `sex` identical\n\n\\footnotesize\n\n::: {.cell}\n\n```{.r .cell-code}\nanova(b.2, b.1)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\\normalsize\n\nSame P-value as before, so we haven't changed anything important.\n\n\n\n\n## Trying interaction between age and gender\n\n\n::: {.cell}\n\n:::\n\n\n \n\\scriptsize\n\n::: {.cell}\n\n```{.r .cell-code}\nbrands.4 <- update(brands.1, . ~ . + age:sex)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# weights: 15 (8 variable)\ninitial value 807.480032 \niter 10 value 703.191146\niter 20 value 702.572260\niter 30 value 702.570900\niter 30 value 702.570893\niter 30 value 702.570893\nfinal value 702.570893 \nconverged\n```\n:::\n\n```{.r .cell-code}\nanova(brands.1, brands.4)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
\n \n
\n`````\n:::\n:::\n\n\n \n\\normalsize\n\n* No evidence that effect of age on brand preference differs for\nthe two genders.\n\n## Make graph again\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_predictions(brands.4, condition = c(\"age\", \"brand\", \"sex\"), \n type = \"probs\", draw = FALSE) %>% \n ggplot(aes(x = age, y = estimate, colour = group, \n linetype = sex)) +\n geom_line() -> g4\n```\n:::\n\n\n\n## Not much difference in the graph\n\n\n::: {.cell}\n\n```{.r .cell-code}\ng4\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-revealjs/unnamed-chunk-19-1.png){width=960}\n:::\n:::\n\n\n## Compare model without interaction\n\n\n::: {.cell}\n\n```{.r .cell-code}\ng\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-revealjs/unnamed-chunk-20-1.png){width=960}\n:::\n:::\n", "supporting": [ "logistic_files/figure-revealjs" ], diff --git a/_freeze/logistic/execute-results/tex.json b/_freeze/logistic/execute-results/tex.json index d733b5f..3b008bf 100644 --- a/_freeze/logistic/execute-results/tex.json +++ b/_freeze/logistic/execute-results/tex.json @@ -1,7 +1,7 @@ { - "hash": "d00d4260f0342837f54c858773a0870d", + "hash": "e6b7540e15c08007b53f23b153419a27", "result": { - "markdown": "---\ntitle: \"Logistic Regression\"\n---\n\n\n\n\n## Logistic regression\n\n\n* When response variable is measured/counted, regression can work well.\n\n* But what if response is yes/no, lived/died, success/failure?\n\n* Model *probability* of success.\n\n* Probability must be between 0 and 1; need method that ensures this.\n\n* *Logistic regression* does this. In R, is a\n*generalized linear model* with binomial \"family\": \n\n\n::: {.cell}\n\n```{.r .cell-code}\nglm(y ~ x, family=\"binomial\")\n```\n:::\n\n\n\n\n* Begin with simplest case.\n\n\n\n## Packages\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(MASS)\nlibrary(tidyverse)\nlibrary(marginaleffects)\nlibrary(broom)\nlibrary(nnet)\nlibrary(conflicted)\nconflict_prefer(\"select\", \"dplyr\")\nconflict_prefer(\"filter\", \"dplyr\")\nconflict_prefer(\"rename\", \"dplyr\")\nconflict_prefer(\"summarize\", \"dplyr\")\n```\n:::\n\n\n\n \n\n\n## The rats, part 1\n\n\n* Rats given dose of some poison; either live or die:\n\n\\small\n```\ndose status\n0 lived\n1 died\n2 lived\n3 lived\n4 died\n5 died\n```\n\n\\normalsize\n\n## Read in: \n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \"http://ritsokiguess.site/datafiles/rat.txt\"\nrats <- read_delim(my_url, \" \")\nrats\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 6 x 2\n dose status\n \n1 0 lived \n2 1 died \n3 2 lived \n4 3 lived \n5 4 died \n6 5 died \n```\n:::\n:::\n\n\n \n\n\n## Basic logistic regression\n\n\n* Make response into a factor first:\n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrats2 <- rats %>% mutate(status = factor(status))\n```\n:::\n\n\n\\normalsize\n \n\n\n* then fit model:\n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nstatus.1 <- glm(status ~ dose, family = \"binomial\", data = rats2)\n```\n:::\n\n\n\\normalsize\n \n\n \n\n\n## Output\n\n\\scriptsize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(status.1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\nCall:\nglm(formula = status ~ dose, family = \"binomial\", data = rats2)\n\nCoefficients:\n Estimate Std. Error z value Pr(>|z|)\n(Intercept) 1.6841 1.7979 0.937 0.349\ndose -0.6736 0.6140 -1.097 0.273\n\n(Dispersion parameter for binomial family taken to be 1)\n\n Null deviance: 8.3178 on 5 degrees of freedom\nResidual deviance: 6.7728 on 4 degrees of freedom\nAIC: 10.773\n\nNumber of Fisher Scoring iterations: 4\n```\n:::\n:::\n\n\n\\normalsize\n\n\n## Interpreting the output\n\n\n* Like (multiple) regression, get\ntests of significance of individual $x$'s\n\n* Here not significant (only 6 observations).\n\n* \"Slope\" for dose is negative, meaning that as dose increases, probability of event modelled (survival) decreases.\n\n\n\n\n\n## Output part 2: predicted survival probs\n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(status.1)) %>% \n select(dose, estimate)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n dose estimate\n1 0 0.8434490\n2 1 0.7331122\n3 2 0.5834187\n4 3 0.4165813\n5 4 0.2668878\n6 5 0.1565510\n```\n:::\n:::\n\n\n\\normalsize \n\n## On a graph\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_cap(status.1, condition = \"dose\")\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-beamer/unnamed-chunk-1-1.pdf)\n:::\n:::\n\n\n\n\n## The rats, more\n\n\n* More realistic: more rats at each dose (say 10).\n\n* Listing each rat on one line makes a big data file.\n\n* Use format below: dose, number of survivals, number of deaths.\n\n```\n\ndose lived died\n0 10 0\n1 7 3 \n2 6 4 \n3 4 6 \n4 2 8 \n5 1 9 \n\n```\n\n\n* 6 lines of data correspond to 60 actual rats.\n\n* Saved in `rat2.txt`.\n\n\n\n## These data\n\n\\footnotesize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \"http://ritsokiguess.site/datafiles/rat2.txt\"\nrat2 <- read_delim(my_url, \" \")\nrat2\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 6 x 3\n dose lived died\n \n1 0 10 0\n2 1 7 3\n3 2 6 4\n4 3 4 6\n5 4 2 8\n6 5 1 9\n```\n:::\n:::\n\n\n\\normalsize\n\n\n## Create response matrix:\n\n- Each row contains *multiple* observations.\n- Create *two-column* response:\n - \\#survivals in first column, \n - \\#deaths in second.\n\n\n\\footnotesize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nresponse <- with(rat2, cbind(lived, died))\nresponse\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n lived died\n[1,] 10 0\n[2,] 7 3\n[3,] 6 4\n[4,] 4 6\n[5,] 2 8\n[6,] 1 9\n```\n:::\n:::\n\n\n\\normalsize\n\n- Response is R `matrix`:\n\n\\scriptsize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nclass(response)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] \"matrix\" \"array\" \n```\n:::\n:::\n\n\n\\normalsize\n\n \n## Fit logistic regression\n\n- using response you just made:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrat2.1 <- glm(response ~ dose,\n family = \"binomial\",\n data = rat2\n)\n```\n:::\n\n\n\n\n\n\n## Output\n\n\\scriptsize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(rat2.1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\nCall:\nglm(formula = response ~ dose, family = \"binomial\", data = rat2)\n\nCoefficients:\n Estimate Std. Error z value Pr(>|z|) \n(Intercept) 2.3619 0.6719 3.515 0.000439 ***\ndose -0.9448 0.2351 -4.018 5.87e-05 ***\n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\n(Dispersion parameter for binomial family taken to be 1)\n\n Null deviance: 27.530 on 5 degrees of freedom\nResidual deviance: 2.474 on 4 degrees of freedom\nAIC: 18.94\n\nNumber of Fisher Scoring iterations: 4\n```\n:::\n:::\n\n\n\\normalsize\n\n\n## Predicted survival probs\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnew <- datagrid(model = rat2.1, dose = 0:5)\ncbind(predictions(rat2.1, newdata = new))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n rowid estimate p.value s.value conf.low conf.high dose\n1 1 0.9138762 0.0004389651 11.153606 0.73983042 0.9753671 0\n2 2 0.8048905 0.0031438277 8.313262 0.61695841 0.9135390 1\n3 3 0.6159474 0.1721141940 2.538562 0.44876099 0.7595916 2\n4 4 0.3840526 0.1721142921 2.538561 0.24040837 0.5512390 3\n5 5 0.1951095 0.0031438384 8.313257 0.08646093 0.3830417 4\n6 6 0.0861238 0.0004389668 11.153600 0.02463288 0.2601697 5\n```\n:::\n:::\n\n\n \n\n## On a picture\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(rat2.1, newdata = new)) %>% \n select(estimate, conf.low, conf.high, dose) %>% \n ggplot(aes(x = dose, y = estimate, \n ymin = conf.low, ymax = conf.high)) + \n geom_line() + geom_ribbon(alpha = 0.3)\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-beamer/unnamed-chunk-2-1.pdf)\n:::\n:::\n\n\n\n\n## Comments\n\n\n\n* Significant effect of dose. \n\n* Effect of larger dose is to *decrease* survival probability\n(\"slope\" negative; also see in decreasing predictions.)\n\n* Confidence intervals around prediction narrower (more data).\n\n## Multiple logistic regression\n\n\n* With more than one $x$, works much like multiple regression.\n\n* Example: study of patients with blood poisoning severe enough to warrant surgery. Relate survival to other potential risk factors.\n\n* Variables, 1=present, 0=absent:\n\n\n * survival (death from sepsis=1), response\n * shock\n * malnutrition\n * alcoholism\n * age (as numerical variable)\n * bowel infarction\n\n\n* See what relates to death.\n\n\n\n## Read in data\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \n \"http://ritsokiguess.site/datafiles/sepsis.txt\"\nsepsis <- read_delim(my_url, \" \")\n```\n:::\n\n\n \n## Make sure categoricals really are\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis %>% \n mutate(across(-age, \\(x) factor(x))) -> sepsis\n```\n:::\n\n\n\n\n## The data (some)\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 106 x 6\n death shock malnut alcohol age bowelinf\n \n 1 0 0 0 0 56 0 \n 2 0 0 0 0 80 0 \n 3 0 0 0 0 61 0 \n 4 0 0 0 0 26 0 \n 5 0 0 0 0 53 0 \n 6 1 0 1 0 87 0 \n 7 0 0 0 0 21 0 \n 8 1 0 0 1 69 0 \n 9 0 0 0 0 57 0 \n10 0 0 1 0 76 0 \n# i 96 more rows\n```\n:::\n:::\n\n\n\n \n\n\n## Fit model\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis.1 <- glm(death ~ shock + malnut + alcohol + age +\n bowelinf,\nfamily = \"binomial\",\ndata = sepsis\n)\n```\n:::\n\n\n\n \n\n\n## Output part 1\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntidy(sepsis.1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 6 x 5\n term estimate std.error statistic p.value\n \n1 (Intercept) -9.75 2.54 -3.84 0.000124\n2 shock1 3.67 1.16 3.15 0.00161 \n3 malnut1 1.22 0.728 1.67 0.0948 \n4 alcohol1 3.35 0.982 3.42 0.000635\n5 age 0.0922 0.0303 3.04 0.00237 \n6 bowelinf1 2.80 1.16 2.40 0.0162 \n```\n:::\n:::\n\n\n \n\n\n\n* All P-values fairly small\n\n* but `malnut` not significant: remove.\n\n\n\n## Removing `malnut`\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis.2 <- update(sepsis.1, . ~ . - malnut)\ntidy(sepsis.2)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 5 x 5\n term estimate std.error statistic p.value\n \n1 (Intercept) -8.89 2.32 -3.84 0.000124\n2 shock1 3.70 1.10 3.35 0.000797\n3 alcohol1 3.19 0.917 3.47 0.000514\n4 age 0.0898 0.0292 3.07 0.00211 \n5 bowelinf1 2.39 1.07 2.23 0.0260 \n```\n:::\n:::\n\n\n \n\n\n\n* Everything significant now.\n\n\n\n## Comments\n\n\n* Most of the original $x$'s helped predict death. Only `malnut` seemed not to add anything.\n\n* Removed `malnut` and tried again.\n\n* Everything remaining is significant (though `bowelinf`\nactually became *less* significant).\n\n* All coefficients are *positive*, so having any of the risk\nfactors (or being older)\n*increases* risk of death. \n\n\n## Predictions from model without \"malnut\"\n\n\n* A few (rows of original dataframe) chosen \"at random\":\n\n\\tiny\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis %>% slice(c(4, 1, 2, 11, 32)) -> new\nnew\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 5 x 6\n death shock malnut alcohol age bowelinf\n \n1 0 0 0 0 26 0 \n2 0 0 0 0 56 0 \n3 0 0 0 0 80 0 \n4 1 0 0 1 66 1 \n5 1 0 0 1 49 0 \n```\n:::\n\n```{.r .cell-code}\ncbind(predictions(sepsis.2, newdata = new)) %>% \n select(estimate, conf.low, conf.high, shock:bowelinf)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n estimate conf.low conf.high shock malnut alcohol age bowelinf\n1 0.001415347 6.272642e-05 0.03103047 0 0 0 26 0\n2 0.020552383 4.102504e-03 0.09656596 0 0 0 56 0\n3 0.153416834 5.606838e-02 0.35603441 0 0 0 80 0\n4 0.931290137 5.490986e-01 0.99341482 0 0 1 66 1\n5 0.213000997 7.639063e-02 0.46967947 0 0 1 49 0\n```\n:::\n:::\n\n\n\\normalsize\n\n\n## Comments \n\n* Survival chances pretty good if no risk factors, though decreasing with age.\n\n* Having more than one risk factor reduces survival chances dramatically.\n\n* Usually good job of predicting survival; sometimes death predicted to survive.\n\n## Another way to assess effects\n\nof `age`:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnew <- datagrid(model = sepsis.2, age = seq(30, 70, 10))\nnew\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n death shock alcohol bowelinf age\n1 0 0 0 0 30\n2 0 0 0 0 40\n3 0 0 0 0 50\n4 0 0 0 0 60\n5 0 0 0 0 70\n```\n:::\n:::\n\n\n\n\n## Assessing age effect \n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(sepsis.2, newdata = new)) %>% \n select(estimate, shock:age)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n estimate shock alcohol bowelinf age\n1 0.002026053 0 0 0 30\n2 0.004960283 0 0 0 40\n3 0.012092515 0 0 0 50\n4 0.029179226 0 0 0 60\n5 0.068729752 0 0 0 70\n```\n:::\n:::\n\n\n\n## Assessing shock effect\n\n\\footnotesize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnew <- datagrid(shock = c(0, 1), model = sepsis.2)\nnew\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n death alcohol age bowelinf shock\n1 0 0 51.28302 0 0\n2 0 0 51.28302 0 1\n```\n:::\n\n```{.r .cell-code}\ncbind(predictions(sepsis.2, newdata = new)) %>% \n select(estimate, death:shock)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n estimate death alcohol age bowelinf shock\n1 0.01354973 0 0 51.28302 0 0\n2 0.35742607 0 0 51.28302 0 1\n```\n:::\n:::\n\n\n\\normalsize\n\n## Assessing proportionality of odds for age\n\n\n* An assumption we made is that log-odds of survival depends\nlinearly on age.\n\n* Hard to get your head around, but \nbasic idea is that survival chances go continuously up (or down)\nwith age, instead of (for example) going up and then down.\n\n* In this case, seems reasonable, but should check:\n\n\n## Residuals vs.\\ age\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis.2 %>% augment(sepsis) %>% \n ggplot(aes(x = age, y = .resid)) +\n geom_point()\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-beamer/virtusentella-1.pdf)\n:::\n:::\n\n\n \n## Comments\n\n* No apparent problems overall.\n\n* Confusing \"line\" across: no risk factors, survived. \n\n\n\n## Probability and odds\n\n* For probability $p$, odds is $p/(1-p)$:\n\n\n \\begin{tabular}{rrrl}\n \\hline\n Prob.\\ & Odds & log-odds & in words\\\\\n \\hline\n 0.5 & $0.5/0.5=1/1=1.00$ & $0.00$ & ``even money''\\\\\n 0.1 & $0.1/0.9=1/9=0.11$ & $-2.20$ & ``9 to 1''\\\\\n 0.4 & $0.4/0.6=1/1.5=0.67$ & $-0.41$ & ``1.5 to 1''\\\\\n 0.8 & $0.8/0.2=4/1=4.00$ & $1.39$ & ``4 to 1 on''\\\\\n \\hline\n \\end{tabular}\n\n\n* Gamblers use odds: if you win at 9 to 1 odds, get original\nstake back plus 9 times the stake.\n\n* Probability has to be between 0 and 1\n\n* Odds between 0 and infinity\n\n* *Log*-odds can be anything: any log-odds corresponds to\nvalid probability.\n\n\n\n## Odds ratio\n\n\n* Suppose 90 of 100 men drank wine last week, but only 20 of 100 women.\n\n* Prob of man drinking wine $90/100=0.9$, woman $20/100=0.2$.\n\n* Odds of man drinking wine $0.9/0.1=9$, woman $0.2/0.8=0.25$.\n\n* Ratio of odds is $9/0.25=36$.\n\n* Way of quantifying difference between men and women: ``odds of\ndrinking wine 36 times larger for males than females''. \n\n\n\n## Sepsis data again\n\n\n* Recall prediction of probability of death from risk factors:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis.2.tidy <- tidy(sepsis.2)\nsepsis.2.tidy\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 5 x 5\n term estimate std.error statistic p.value\n \n1 (Intercept) -8.89 2.32 -3.84 0.000124\n2 shock1 3.70 1.10 3.35 0.000797\n3 alcohol1 3.19 0.917 3.47 0.000514\n4 age 0.0898 0.0292 3.07 0.00211 \n5 bowelinf1 2.39 1.07 2.23 0.0260 \n```\n:::\n:::\n\n\n\n \n\n\n* Slopes in column `estimate`.\n\n\n\n## Multiplying the odds\n\n\n* Can interpret slopes by taking \"exp\" of them. We ignore intercept.\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis.2.tidy %>% \n mutate(exp_coeff=exp(estimate)) %>% \n select(term, exp_coeff)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 5 x 2\n term exp_coeff\n \n1 (Intercept) 0.000137\n2 shock1 40.5 \n3 alcohol1 24.2 \n4 age 1.09 \n5 bowelinf1 10.9 \n```\n:::\n:::\n\n\n\n## Interpretation\n\n\\small\n\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 5 x 2\n term exp_coeff\n \n1 (Intercept) 0.000137\n2 shock1 40.5 \n3 alcohol1 24.2 \n4 age 1.09 \n5 bowelinf1 10.9 \n```\n:::\n:::\n\n\n\\normalsize\n\n\n* These say \"how much do you *multiply* odds of death by\nfor increase of 1 in corresponding risk factor?\" Or, what is odds\nratio for that factor being 1 (present) vs.\\ 0 (absent)?\n\n* Eg.\\ being alcoholic vs.\\ not increases odds of death by 24 times\n\n* One year older multiplies odds by about 1.1 times. Over 40 years,\nabout $1.09^{40}=31$ times. \n\n\n\n## Odds ratio and relative risk\n\n\n* **Relative risk** is ratio of probabilities.\n\n* Above: 90 of 100 men (0.9) drank wine, 20 of 100 women (0.2).\n\n* Relative risk 0.9/0.2=4.5. (odds ratio was 36).\n\n* When probabilities small, relative risk and odds ratio similar.\n\n* Eg.\\ prob of man having disease 0.02, woman 0.01.\n\n* Relative risk $0.02/0.01=2$.\n\n## Odds ratio vs.\\ relative risk\n\n- Odds for men and for women:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n(od1 <- 0.02 / 0.98) # men\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 0.02040816\n```\n:::\n\n```{.r .cell-code}\n(od2 <- 0.01 / 0.99) # women\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 0.01010101\n```\n:::\n:::\n\n\n\n- Odds ratio \n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nod1 / od2\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 2.020408\n```\n:::\n:::\n\n\n\n\n- Very close to relative risk of 2.\n\n\n## More than 2 response categories\n\n\n* With 2 response categories, model the probability of one, and prob of other is one minus that. So doesn't matter which category you model.\n\n* With more than 2 categories, have to think more carefully about the categories: are they\n\n\n* *ordered*: you can put them in a natural order (like low, medium, high)\n\n* *nominal*: ordering the categories doesn't make sense (like red, green, blue).\n\n\n* R handles both kinds of response; learn how.\n\n\n\n## Ordinal response: the miners\n\n\n* \nModel probability of being in given category *or lower*.\n\n* Example: coal-miners often suffer disease pneumoconiosis. Likelihood of disease believed to be greater \namong miners who have worked longer. \n\n* Severity of disease measured on categorical scale: none,\nmoderate, severe.\n\n## Miners data\n\n* Data are frequencies:\n\n```\nExposure None Moderate Severe\n5.8 98 0 0\n15.0 51 2 1\n21.5 34 6 3\n27.5 35 5 8\n33.5 32 10 9\n39.5 23 7 8\n46.0 12 6 10\n51.5 4 2 5\n```\n\n\n\n\n## Reading the data\n\nData in aligned columns with more than one space between, so: \n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \"http://ritsokiguess.site/datafiles/miners-tab.txt\"\nfreqs <- read_table(my_url)\n```\n:::\n\n\n\\normalsize\n\n\n## The data\n\n\n::: {.cell}\n\n```{.r .cell-code}\nfreqs\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 8 x 4\n Exposure None Moderate Severe\n \n1 5.8 98 0 0\n2 15 51 2 1\n3 21.5 34 6 3\n4 27.5 35 5 8\n5 33.5 32 10 9\n6 39.5 23 7 8\n7 46 12 6 10\n8 51.5 4 2 5\n```\n:::\n:::\n\n\n\n \n\n\n## Tidying \n\n\n::: {.cell}\n\n```{.r .cell-code}\nfreqs %>%\n pivot_longer(-Exposure, names_to = \"Severity\", values_to = \"Freq\") %>%\n mutate(Severity = fct_inorder(Severity)) -> miners\n```\n:::\n\n\n\n \n\n\n## Result\n\n\\tiny\n\n\n::: {.cell}\n\n```{.r .cell-code}\nminers\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 24 x 3\n Exposure Severity Freq\n \n 1 5.8 None 98\n 2 5.8 Moderate 0\n 3 5.8 Severe 0\n 4 15 None 51\n 5 15 Moderate 2\n 6 15 Severe 1\n 7 21.5 None 34\n 8 21.5 Moderate 6\n 9 21.5 Severe 3\n10 27.5 None 35\n# i 14 more rows\n```\n:::\n:::\n\n\n\\normalsize\n \n\n\n## Plot proportions against exposure\n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nminers %>% \n group_by(Exposure) %>% \n mutate(proportion = Freq / sum(Freq)) -> prop\nggplot(prop, aes(x = Exposure, y = proportion,\n colour = Severity)) + \n geom_point() + geom_smooth(se = F)\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-beamer/bLogistic-28-1.pdf)\n:::\n:::\n\n\n\\normalsize\n\n\n## Reminder of data setup\n\\footnotesize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nminers\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 24 x 3\n Exposure Severity Freq\n \n 1 5.8 None 98\n 2 5.8 Moderate 0\n 3 5.8 Severe 0\n 4 15 None 51\n 5 15 Moderate 2\n 6 15 Severe 1\n 7 21.5 None 34\n 8 21.5 Moderate 6\n 9 21.5 Severe 3\n10 27.5 None 35\n# i 14 more rows\n```\n:::\n:::\n\n\n\n \n\\normalsize\n\n\n\n## Fitting ordered logistic model\n\nUse function `polr` from package `MASS`. Like `glm`.\n\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsev.1 <- polr(Severity ~ Exposure,\n weights = Freq,\n data = miners\n)\n```\n:::\n\n\n \n\n\n## Output: not very illuminating\n\n\n\\scriptsize\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsev.1 <- polr(Severity ~ Exposure,\n weights = Freq,\n data = miners,\n Hess = TRUE\n)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(sev.1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nCall:\npolr(formula = Severity ~ Exposure, data = miners, weights = Freq, \n Hess = TRUE)\n\nCoefficients:\n Value Std. Error t value\nExposure 0.0959 0.01194 8.034\n\nIntercepts:\n Value Std. Error t value\nNone|Moderate 3.9558 0.4097 9.6558\nModerate|Severe 4.8690 0.4411 11.0383\n\nResidual Deviance: 416.9188 \nAIC: 422.9188 \n```\n:::\n:::\n\n\n\\normalsize\n \n\n## Does exposure have an effect?\nFit model without `Exposure`, and compare\nusing `anova`. Note `1` for model with just intercept:\n\n\n\n::: {.cell}\n\n:::\n\n\n\n \n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsev.0 <- polr(Severity ~ 1, weights = Freq, data = miners)\nanova(sev.0, sev.1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nLikelihood ratio tests of ordinal regression models\n\nResponse: Severity\n Model Resid. df Resid. Dev Test Df LR stat.\n1 1 369 505.1621 \n2 Exposure 368 416.9188 1 vs 2 1 88.24324\n Pr(Chi)\n1 \n2 0\n```\n:::\n:::\n\n\n\\normalsize\n\nExposure definitely has effect on severity of disease. \n\n\n## Another way\n\n\n* What (if anything) can we drop from model with `exposure`?\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndrop1(sev.1, test = \"Chisq\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nSingle term deletions\n\nModel:\nSeverity ~ Exposure\n Df AIC LRT Pr(>Chi) \n 422.92 \nExposure 1 509.16 88.243 < 2.2e-16 ***\n---\nSignif. codes: \n0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n```\n:::\n:::\n\n\n\n \n\n\n* Nothing. Exposure definitely has effect.\n\n\n\n## Predicted probabilities 1/2\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nfreqs %>% select(Exposure) -> new\nnew\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 8 x 1\n Exposure\n \n1 5.8\n2 15 \n3 21.5\n4 27.5\n5 33.5\n6 39.5\n7 46 \n8 51.5\n```\n:::\n:::\n\n\n\n\n## Predicted probabilities 2/2\n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(sev.1, newdata = new)) %>%\n select(group, estimate, Exposure) %>% \n pivot_wider(names_from = group, values_from = estimate)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 8 x 4\n Exposure None Moderate Severe\n \n1 5.8 0.968 0.0191 0.0132\n2 15 0.925 0.0433 0.0314\n3 21.5 0.869 0.0739 0.0569\n4 27.5 0.789 0.114 0.0969\n5 33.5 0.678 0.162 0.160 \n6 39.5 0.542 0.205 0.253 \n7 46 0.388 0.224 0.388 \n8 51.5 0.272 0.210 0.517 \n```\n:::\n:::\n\n\n\\normalsize\n\n## Plot of predicted probabilities\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_cap(model = sev.1, condition = c(\"Exposure\", \"group\"),\n type = \"probs\") +\n geom_point(data = prop, aes(x = Exposure, y = proportion, \n colour = Severity)) -> ggg\n```\n:::\n\n\n\n## The graph\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggg\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-beamer/unnamed-chunk-11-1.pdf)\n:::\n:::\n\n\n\n\n## Comments\n\n\n* Model appears to match data well enough.\n\n- As exposure goes up, prob of None\ngoes down, Severe goes up (sharply for high exposure).\n\n- So more exposure means worse disease.\n\n\n## Unordered responses\n\n\n* With unordered (nominal) responses, can use *generalized logit*.\n\n* Example: 735 people, record age and sex (male 0, female 1), which of 3 brands of some product preferred.\n\n* Data in `mlogit.csv` separated by commas (so\n`read_csv` will work):\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \"http://ritsokiguess.site/datafiles/mlogit.csv\"\nbrandpref <- read_csv(my_url)\n```\n:::\n\n\n \n\n\n\n\n## The data (some)\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbrandpref\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 735 x 3\n brand sex age\n \n 1 1 0 24\n 2 1 0 26\n 3 1 0 26\n 4 1 1 27\n 5 1 1 27\n 6 3 1 27\n 7 1 0 27\n 8 1 0 27\n 9 1 1 27\n10 1 0 27\n# i 725 more rows\n```\n:::\n:::\n\n\n\n \n\n\n## Bashing into shape\n\n\n* `sex` and `brand` not meaningful as numbers, so\nturn into factors:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbrandpref %>%\n mutate(sex = ifelse(sex == 1, \"female\", \"male\"), \n sex = factor(sex),\n brand = factor(brand)\n ) -> brandpref\n```\n:::\n\n\n \n \n## Fitting model \n\n* We use `multinom` from package `nnet`. Works\nlike `polr`.\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbrands.1 <- multinom(brand ~ age + sex, data = brandpref)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# weights: 12 (6 variable)\ninitial value 807.480032 \niter 10 value 702.990572\nfinal value 702.970704 \nconverged\n```\n:::\n:::\n\n\n \n\n\n## Can we drop anything?\n\n\n* Unfortunately `drop1` seems not to work:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndrop1(brands.1, test = \"Chisq\", trace = 0)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\ntrying - age \n```\n:::\n\n::: {.cell-output .cell-output-error}\n```\nError in if (trace) {: argument is not interpretable as logical\n```\n:::\n:::\n\n\n\n* So, fall back on fitting model without what you want to test, and\ncomparing using `anova`. \n\n\n\n## Do age/sex help predict brand? 1/3\n\nFit models without each of age and sex:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbrands.2 <- multinom(brand ~ age, data = brandpref)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# weights: 9 (4 variable)\ninitial value 807.480032 \niter 10 value 706.796323\niter 10 value 706.796322\nfinal value 706.796322 \nconverged\n```\n:::\n\n```{.r .cell-code}\nbrands.3 <- multinom(brand ~ sex, data = brandpref)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# weights: 9 (4 variable)\ninitial value 807.480032 \nfinal value 791.861266 \nconverged\n```\n:::\n:::\n\n\n \n\n\n## Do age/sex help predict brand? 2/3\n\n\\scriptsize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nanova(brands.2, brands.1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nLikelihood ratio tests of Multinomial Models\n\nResponse: brand\n Model Resid. df Resid. Dev Test Df LR stat.\n1 age 1466 1413.593 \n2 age + sex 1464 1405.941 1 vs 2 2 7.651236\n Pr(Chi)\n1 \n2 0.02180496\n```\n:::\n\n```{.r .cell-code}\nanova(brands.3, brands.1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nLikelihood ratio tests of Multinomial Models\n\nResponse: brand\n Model Resid. df Resid. Dev Test Df LR stat.\n1 sex 1466 1583.723 \n2 age + sex 1464 1405.941 1 vs 2 2 177.7811\n Pr(Chi)\n1 \n2 0\n```\n:::\n:::\n\n\n\\normalsize\n\n\n## Do age/sex help predict brand? 3/3\n\n\n* `age` definitely significant (second `anova`)\n\n* `sex` significant also (first `anova`), though P-value less dramatic\n\n* Keep both.\n- Expect to see a large effect of `age`, and a smaller one of `sex`.\n\n\n\n\n## Another way to build model\n\n\n* Start from model with everything and run `step`:\n\n\n\n::: {.cell}\n\n:::\n\n\n\n\n\\footnotesize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nstep(brands.1, trace = 0)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\ntrying - age \ntrying - sex \n```\n:::\n\n::: {.cell-output .cell-output-stdout}\n```\nCall:\nmultinom(formula = brand ~ age + sex)\n\nCoefficients:\n (Intercept) age sexmale\n2 -11.25127 0.3682202 -0.5237736\n3 -22.25571 0.6859149 -0.4658215\n\nResidual Deviance: 1405.941 \nAIC: 1417.941 \n```\n:::\n:::\n\n\n\\normalsize\n \n\n* Final model contains both `age` and `sex` so neither\ncould be removed.\n\n\n## Making predictions\n\nFind age 5-number summary, and the two sexes:\n\n\\small\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(brandpref)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n brand sex age \n 1:207 female:466 Min. :24.0 \n 2:307 male :269 1st Qu.:32.0 \n 3:221 Median :32.0 \n Mean :32.9 \n 3rd Qu.:34.0 \n Max. :38.0 \n```\n:::\n:::\n\n\n\nSpace the ages out a bit for prediction (see over).\n\n\\normalsize\n\n## Combinations\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnew <- datagrid(age = c(24, 30, 33, 35, 38), \n sex = c(\"female\", \"male\"), model = brands.1)\nnew\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n brand age sex\n1 2 24 female\n2 2 24 male\n3 2 30 female\n4 2 30 male\n5 2 33 female\n6 2 33 male\n7 2 35 female\n8 2 35 male\n9 2 38 female\n10 2 38 male\n```\n:::\n:::\n\n\n\n## The predictions\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(brands.1, newdata = new)) %>%\n select(group, estimate, age, sex) %>% \n pivot_wider(names_from = group, values_from = estimate)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 10 x 5\n age sex `1` `2` `3`\n \n 1 24 female 0.915 0.0819 0.00279\n 2 24 male 0.948 0.0502 0.00181\n 3 30 female 0.500 0.407 0.0933 \n 4 30 male 0.625 0.302 0.0732 \n 5 33 female 0.203 0.500 0.297 \n 6 33 male 0.296 0.432 0.272 \n 7 35 female 0.0840 0.432 0.484 \n 8 35 male 0.131 0.397 0.472 \n 9 38 female 0.0162 0.252 0.732 \n10 38 male 0.0260 0.239 0.735 \n```\n:::\n:::\n\n\n\n## Comments\n\n* Young males prefer brand 1, \nbut older males prefer brand 3.\n\n* Females similar, but like brand 1 less and\nbrand 2 more.\n\n- A clear `brand` effect, but the `sex` effect is less clear. \n\n## Making a plot\n\n- `plot_cap` doesn't quite work\n- so don't draw, edit, *then* make graph:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_cap(brands.1, condition = c(\"age\", \"brand\", \"sex\"), \n type = \"probs\", draw = FALSE) %>% \n ggplot(aes(x = age, y = estimate, colour = group, \n linetype = sex)) +\n geom_line() -> g\n```\n:::\n\n\n\n## The graph\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ng\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-beamer/unnamed-chunk-16-1.pdf)\n:::\n:::\n\n\n \n\n## Digesting the plot\n\n\n* Brand vs.\\ age: younger people (of both genders) prefer brand\n1, but older people (of both genders) prefer brand 3. (Explains\nsignificant age effect.)\n\n* Brand vs.\\ sex: females (solid) like brand 1 less than males\n(dashed), like brand 2 more (for all ages). \n\n* Not much brand difference between genders (solid and dashed\nlines of same colours close), but enough to be significant.\n\n* Model didn't include interaction, so modelled effect of gender\non brand same for each age, modelled effect of age same for each\ngender. (See also later.) \n\n\n## Alternative data format\n\nSummarize all people of same brand preference, same sex, same age on one line of data file with frequency on end:\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbrandpref\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 735 x 3\n brand sex age\n \n 1 1 male 24\n 2 1 male 26\n 3 1 male 26\n 4 1 female 27\n 5 1 female 27\n 6 3 female 27\n 7 1 male 27\n 8 1 male 27\n 9 1 female 27\n10 1 male 27\n# i 725 more rows\n```\n:::\n:::\n\n\n\n\n```\n1 0 24 1\n1 0 26 2\n1 0 27 4\n1 0 28 4\n1 0 29 7\n1 0 30 3\n...\n```\n\nWhole data set in 65 lines not 735! But how?\n\n\n## Getting alternative data format\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbrandpref %>%\n group_by(age, sex, brand) %>%\n summarize(Freq = n()) %>%\n ungroup() -> b\nb\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 65 x 4\n age sex brand Freq\n \n 1 24 male 1 1\n 2 26 male 1 2\n 3 27 female 1 4\n 4 27 female 3 1\n 5 27 male 1 4\n 6 28 female 1 6\n 7 28 female 2 2\n 8 28 female 3 1\n 9 28 male 1 4\n10 28 male 3 2\n# i 55 more rows\n```\n:::\n:::\n\n\n\n \n\n## Fitting models, almost the same\n\n\n* Just have to remember `weights` to incorporate\nfrequencies.\n\n* Otherwise `multinom` assumes you have just 1 obs\non each line!\n\n* Again turn (numerical) `sex` and `brand` into factors:\n\n\\footnotesize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nb %>%\n mutate(sex = factor(sex)) %>%\n mutate(brand = factor(brand)) -> bf\nb.1 <- multinom(brand ~ age + sex, data = bf, weights = Freq)\nb.2 <- multinom(brand ~ age, data = bf, weights = Freq)\n```\n:::\n\n\n\\normalsize\n\n\n\n## P-value for `sex` identical\n\n\\footnotesize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nanova(b.2, b.1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nLikelihood ratio tests of Multinomial Models\n\nResponse: brand\n Model Resid. df Resid. Dev Test Df LR stat.\n1 age 126 1413.593 \n2 age + sex 124 1405.941 1 vs 2 2 7.651236\n Pr(Chi)\n1 \n2 0.02180496\n```\n:::\n:::\n\n\n\\normalsize\n\nSame P-value as before, so we haven't changed anything important.\n\n\n\n\n## Trying interaction between age and gender\n\n\n\n::: {.cell}\n\n:::\n\n\n\n \n\\scriptsize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbrands.4 <- update(brands.1, . ~ . + age:sex)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# weights: 15 (8 variable)\ninitial value 807.480032 \niter 10 value 703.191146\niter 20 value 702.572260\niter 30 value 702.570900\niter 30 value 702.570893\niter 30 value 702.570893\nfinal value 702.570893 \nconverged\n```\n:::\n\n```{.r .cell-code}\nanova(brands.1, brands.4)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nLikelihood ratio tests of Multinomial Models\n\nResponse: brand\n Model Resid. df Resid. Dev Test Df\n1 age + sex 1464 1405.941 \n2 age + sex + age:sex 1462 1405.142 1 vs 2 2\n LR stat. Pr(Chi)\n1 \n2 0.7996223 0.6704466\n```\n:::\n:::\n\n\n\n \n\\normalsize\n\n* No evidence that effect of age on brand preference differs for\nthe two genders.\n\n## Make graph again\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_cap(brands.4, condition = c(\"age\", \"brand\", \"sex\"), \n type = \"probs\", draw = FALSE) %>% \n ggplot(aes(x = age, y = estimate, colour = group, \n linetype = sex)) +\n geom_line() -> g4\n```\n:::\n\n\n\n\n## Not much difference in the graph\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ng4\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-beamer/unnamed-chunk-19-1.pdf)\n:::\n:::\n\n\n\n## Compare model without interaction\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ng\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-beamer/unnamed-chunk-20-1.pdf)\n:::\n:::\n", + "markdown": "---\ntitle: \"Logistic Regression\"\n---\n\n\n\n\n## Logistic regression\n\n\n* When response variable is measured/counted, regression can work well.\n\n* But what if response is yes/no, lived/died, success/failure?\n\n* Model *probability* of success.\n\n* Probability must be between 0 and 1; need method that ensures this.\n\n* *Logistic regression* does this. In R, is a\n*generalized linear model* with binomial \"family\": \n\n\n::: {.cell}\n\n```{.r .cell-code}\nglm(y ~ x, family=\"binomial\")\n```\n:::\n\n\n\n\n* Begin with simplest case.\n\n\n\n## Packages\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(MASS)\nlibrary(tidyverse)\nlibrary(marginaleffects)\nlibrary(broom)\nlibrary(nnet)\nlibrary(conflicted)\nconflict_prefer(\"select\", \"dplyr\")\nconflict_prefer(\"filter\", \"dplyr\")\nconflict_prefer(\"rename\", \"dplyr\")\nconflict_prefer(\"summarize\", \"dplyr\")\n```\n:::\n\n\n\n \n\n\n## The rats, part 1\n\n\n* Rats given dose of some poison; either live or die:\n\n\\small\n```\ndose status\n0 lived\n1 died\n2 lived\n3 lived\n4 died\n5 died\n```\n\n\\normalsize\n\n## Read in: \n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \"http://ritsokiguess.site/datafiles/rat.txt\"\nrats <- read_delim(my_url, \" \")\nrats\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 6 x 2\n dose status\n \n1 0 lived \n2 1 died \n3 2 lived \n4 3 lived \n5 4 died \n6 5 died \n```\n:::\n:::\n\n\n \n\n\n## Basic logistic regression\n\n\n* Make response into a factor first:\n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrats2 <- rats %>% mutate(status = factor(status))\n```\n:::\n\n\n\\normalsize\n \n\n\n* then fit model:\n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nstatus.1 <- glm(status ~ dose, family = \"binomial\", data = rats2)\n```\n:::\n\n\n\\normalsize\n \n\n \n\n\n## Output\n\n\\scriptsize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(status.1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\nCall:\nglm(formula = status ~ dose, family = \"binomial\", data = rats2)\n\nCoefficients:\n Estimate Std. Error z value Pr(>|z|)\n(Intercept) 1.6841 1.7979 0.937 0.349\ndose -0.6736 0.6140 -1.097 0.273\n\n(Dispersion parameter for binomial family taken to be 1)\n\n Null deviance: 8.3178 on 5 degrees of freedom\nResidual deviance: 6.7728 on 4 degrees of freedom\nAIC: 10.773\n\nNumber of Fisher Scoring iterations: 4\n```\n:::\n:::\n\n\n\\normalsize\n\n\n## Interpreting the output\n\n\n* Like (multiple) regression, get\ntests of significance of individual $x$'s\n\n* Here not significant (only 6 observations).\n\n* \"Slope\" for dose is negative, meaning that as dose increases, probability of event modelled (survival) decreases.\n\n\n\n\n\n## Output part 2: predicted survival probs\n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(status.1)) %>% \n select(dose, estimate)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n dose estimate\n1 0 0.8434490\n2 1 0.7331122\n3 2 0.5834187\n4 3 0.4165813\n5 4 0.2668878\n6 5 0.1565510\n```\n:::\n:::\n\n\n\\normalsize \n\n## On a graph\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_predictions(status.1, condition = \"dose\")\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-beamer/unnamed-chunk-1-1.pdf)\n:::\n:::\n\n\n\n\n## The rats, more\n\n\n* More realistic: more rats at each dose (say 10).\n\n* Listing each rat on one line makes a big data file.\n\n* Use format below: dose, number of survivals, number of deaths.\n\n```\n\ndose lived died\n0 10 0\n1 7 3 \n2 6 4 \n3 4 6 \n4 2 8 \n5 1 9 \n\n```\n\n\n* 6 lines of data correspond to 60 actual rats.\n\n* Saved in `rat2.txt`.\n\n\n\n## These data\n\n\\footnotesize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \"http://ritsokiguess.site/datafiles/rat2.txt\"\nrat2 <- read_delim(my_url, \" \")\nrat2\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 6 x 3\n dose lived died\n \n1 0 10 0\n2 1 7 3\n3 2 6 4\n4 3 4 6\n5 4 2 8\n6 5 1 9\n```\n:::\n:::\n\n\n\\normalsize\n\n\n## Create response matrix:\n\n- Each row contains *multiple* observations.\n- Create *two-column* response:\n - \\#survivals in first column, \n - \\#deaths in second.\n\n\n\\footnotesize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nresponse <- with(rat2, cbind(lived, died))\nresponse\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n lived died\n[1,] 10 0\n[2,] 7 3\n[3,] 6 4\n[4,] 4 6\n[5,] 2 8\n[6,] 1 9\n```\n:::\n:::\n\n\n\\normalsize\n\n- Response is R `matrix`:\n\n\\scriptsize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nclass(response)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] \"matrix\" \"array\" \n```\n:::\n:::\n\n\n\\normalsize\n\n \n## Fit logistic regression\n\n- using response you just made:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrat2.1 <- glm(response ~ dose,\n family = \"binomial\",\n data = rat2\n)\n```\n:::\n\n\n\n\n\n\n## Output\n\n\\scriptsize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(rat2.1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\nCall:\nglm(formula = response ~ dose, family = \"binomial\", data = rat2)\n\nCoefficients:\n Estimate Std. Error z value Pr(>|z|) \n(Intercept) 2.3619 0.6719 3.515 0.000439 ***\ndose -0.9448 0.2351 -4.018 5.87e-05 ***\n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\n(Dispersion parameter for binomial family taken to be 1)\n\n Null deviance: 27.530 on 5 degrees of freedom\nResidual deviance: 2.474 on 4 degrees of freedom\nAIC: 18.94\n\nNumber of Fisher Scoring iterations: 4\n```\n:::\n:::\n\n\n\\normalsize\n\n\n## Predicted survival probs\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnew <- datagrid(model = rat2.1, dose = 0:5)\ncbind(predictions(rat2.1, newdata = new))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n rowid estimate p.value s.value conf.low conf.high dose\n1 1 0.9138762 0.0004389651 11.153606 0.73983042 0.9753671 0\n2 2 0.8048905 0.0031438277 8.313262 0.61695841 0.9135390 1\n3 3 0.6159474 0.1721141934 2.538562 0.44876099 0.7595916 2\n4 4 0.3840526 0.1721142946 2.538561 0.24040837 0.5512390 3\n5 5 0.1951095 0.0031438386 8.313257 0.08646093 0.3830417 4\n6 6 0.0861238 0.0004389668 11.153600 0.02463288 0.2601697 5\n```\n:::\n:::\n\n\n \n\n## On a picture\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(rat2.1, newdata = new)) %>% \n select(estimate, conf.low, conf.high, dose) %>% \n ggplot(aes(x = dose, y = estimate, \n ymin = conf.low, ymax = conf.high)) + \n geom_line() + geom_ribbon(alpha = 0.3)\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-beamer/unnamed-chunk-2-1.pdf)\n:::\n:::\n\n\n\n\n## Comments\n\n\n\n* Significant effect of dose. \n\n* Effect of larger dose is to *decrease* survival probability\n(\"slope\" negative; also see in decreasing predictions.)\n\n* Confidence intervals around prediction narrower (more data).\n\n## Multiple logistic regression\n\n\n* With more than one $x$, works much like multiple regression.\n\n* Example: study of patients with blood poisoning severe enough to warrant surgery. Relate survival to other potential risk factors.\n\n* Variables, 1=present, 0=absent:\n\n\n * survival (death from sepsis=1), response\n * shock\n * malnutrition\n * alcoholism\n * age (as numerical variable)\n * bowel infarction\n\n\n* See what relates to death.\n\n\n\n## Read in data\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \n \"http://ritsokiguess.site/datafiles/sepsis.txt\"\nsepsis <- read_delim(my_url, \" \")\n```\n:::\n\n\n \n## Make sure categoricals really are\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis %>% \n mutate(across(-age, \\(x) factor(x))) -> sepsis\n```\n:::\n\n\n\n\n## The data (some)\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 106 x 6\n death shock malnut alcohol age bowelinf\n \n 1 0 0 0 0 56 0 \n 2 0 0 0 0 80 0 \n 3 0 0 0 0 61 0 \n 4 0 0 0 0 26 0 \n 5 0 0 0 0 53 0 \n 6 1 0 1 0 87 0 \n 7 0 0 0 0 21 0 \n 8 1 0 0 1 69 0 \n 9 0 0 0 0 57 0 \n10 0 0 1 0 76 0 \n# i 96 more rows\n```\n:::\n:::\n\n\n\n \n\n\n## Fit model\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis.1 <- glm(death ~ shock + malnut + alcohol + age +\n bowelinf,\nfamily = \"binomial\",\ndata = sepsis\n)\n```\n:::\n\n\n\n \n\n\n## Output part 1\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntidy(sepsis.1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 6 x 5\n term estimate std.error statistic p.value\n \n1 (Intercept) -9.75 2.54 -3.84 0.000124\n2 shock1 3.67 1.16 3.15 0.00161 \n3 malnut1 1.22 0.728 1.67 0.0948 \n4 alcohol1 3.35 0.982 3.42 0.000635\n5 age 0.0922 0.0303 3.04 0.00237 \n6 bowelinf1 2.80 1.16 2.40 0.0162 \n```\n:::\n:::\n\n\n \n\n\n\n* All P-values fairly small\n\n* but `malnut` not significant: remove.\n\n\n\n## Removing `malnut`\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis.2 <- update(sepsis.1, . ~ . - malnut)\ntidy(sepsis.2)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 5 x 5\n term estimate std.error statistic p.value\n \n1 (Intercept) -8.89 2.32 -3.84 0.000124\n2 shock1 3.70 1.10 3.35 0.000797\n3 alcohol1 3.19 0.917 3.47 0.000514\n4 age 0.0898 0.0292 3.07 0.00211 \n5 bowelinf1 2.39 1.07 2.23 0.0260 \n```\n:::\n:::\n\n\n \n\n\n\n* Everything significant now.\n\n\n\n## Comments\n\n\n* Most of the original $x$'s helped predict death. Only `malnut` seemed not to add anything.\n\n* Removed `malnut` and tried again.\n\n* Everything remaining is significant (though `bowelinf`\nactually became *less* significant).\n\n* All coefficients are *positive*, so having any of the risk\nfactors (or being older)\n*increases* risk of death. \n\n\n## Predictions from model without \"malnut\"\n\n\n* A few (rows of original dataframe) chosen \"at random\":\n\n\\tiny\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis %>% slice(c(4, 1, 2, 11, 32)) -> new\nnew\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 5 x 6\n death shock malnut alcohol age bowelinf\n \n1 0 0 0 0 26 0 \n2 0 0 0 0 56 0 \n3 0 0 0 0 80 0 \n4 1 0 0 1 66 1 \n5 1 0 0 1 49 0 \n```\n:::\n\n```{.r .cell-code}\ncbind(predictions(sepsis.2, newdata = new)) %>% \n select(estimate, conf.low, conf.high, shock:bowelinf)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n estimate conf.low conf.high shock malnut alcohol age bowelinf\n1 0.001415347 6.272642e-05 0.03103047 0 0 0 26 0\n2 0.020552383 4.102504e-03 0.09656596 0 0 0 56 0\n3 0.153416834 5.606838e-02 0.35603441 0 0 0 80 0\n4 0.931290137 5.490986e-01 0.99341482 0 0 1 66 1\n5 0.213000997 7.639063e-02 0.46967947 0 0 1 49 0\n```\n:::\n:::\n\n\n\\normalsize\n\n\n## Comments \n\n* Survival chances pretty good if no risk factors, though decreasing with age.\n\n* Having more than one risk factor reduces survival chances dramatically.\n\n* Usually good job of predicting survival; sometimes death predicted to survive.\n\n## Another way to assess effects\n\nof `age`:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnew <- datagrid(model = sepsis.2, age = seq(30, 70, 10))\nnew\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n death shock alcohol bowelinf age\n1 0 0 0 0 30\n2 0 0 0 0 40\n3 0 0 0 0 50\n4 0 0 0 0 60\n5 0 0 0 0 70\n```\n:::\n:::\n\n\n\n\n## Assessing age effect \n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(sepsis.2, newdata = new)) %>% \n select(estimate, shock:age)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n estimate shock alcohol bowelinf age\n1 0.002026053 0 0 0 30\n2 0.004960283 0 0 0 40\n3 0.012092515 0 0 0 50\n4 0.029179226 0 0 0 60\n5 0.068729752 0 0 0 70\n```\n:::\n:::\n\n\n\n## Assessing shock effect\n\n\\footnotesize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnew <- datagrid(shock = c(0, 1), model = sepsis.2)\nnew\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n death alcohol age bowelinf shock\n1 0 0 51.28302 0 0\n2 0 0 51.28302 0 1\n```\n:::\n\n```{.r .cell-code}\ncbind(predictions(sepsis.2, newdata = new)) %>% \n select(estimate, death:shock)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n estimate death alcohol age bowelinf shock\n1 0.01354973 0 0 51.28302 0 0\n2 0.35742607 0 0 51.28302 0 1\n```\n:::\n:::\n\n\n\\normalsize\n\n## Assessing proportionality of odds for age\n\n\n* An assumption we made is that log-odds of survival depends\nlinearly on age.\n\n* Hard to get your head around, but \nbasic idea is that survival chances go continuously up (or down)\nwith age, instead of (for example) going up and then down.\n\n* In this case, seems reasonable, but should check:\n\n\n## Residuals vs.\\ age\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis.2 %>% augment(sepsis) %>% \n ggplot(aes(x = age, y = .resid)) +\n geom_point()\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-beamer/virtusentella-1.pdf)\n:::\n:::\n\n\n \n## Comments\n\n* No apparent problems overall.\n\n* Confusing \"line\" across: no risk factors, survived. \n\n\n\n## Probability and odds\n\n* For probability $p$, odds is $p/(1-p)$:\n\n\n \\begin{tabular}{rrrl}\n \\hline\n Prob.\\ & Odds & log-odds & in words\\\\\n \\hline\n 0.5 & $0.5/0.5=1/1=1.00$ & $0.00$ & ``even money''\\\\\n 0.1 & $0.1/0.9=1/9=0.11$ & $-2.20$ & ``9 to 1''\\\\\n 0.4 & $0.4/0.6=1/1.5=0.67$ & $-0.41$ & ``1.5 to 1''\\\\\n 0.8 & $0.8/0.2=4/1=4.00$ & $1.39$ & ``4 to 1 on''\\\\\n \\hline\n \\end{tabular}\n\n\n* Gamblers use odds: if you win at 9 to 1 odds, get original\nstake back plus 9 times the stake.\n\n* Probability has to be between 0 and 1\n\n* Odds between 0 and infinity\n\n* *Log*-odds can be anything: any log-odds corresponds to\nvalid probability.\n\n\n\n## Odds ratio\n\n\n* Suppose 90 of 100 men drank wine last week, but only 20 of 100 women.\n\n* Prob of man drinking wine $90/100=0.9$, woman $20/100=0.2$.\n\n* Odds of man drinking wine $0.9/0.1=9$, woman $0.2/0.8=0.25$.\n\n* Ratio of odds is $9/0.25=36$.\n\n* Way of quantifying difference between men and women: ``odds of\ndrinking wine 36 times larger for males than females''. \n\n\n\n## Sepsis data again\n\n\n* Recall prediction of probability of death from risk factors:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis.2.tidy <- tidy(sepsis.2)\nsepsis.2.tidy\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 5 x 5\n term estimate std.error statistic p.value\n \n1 (Intercept) -8.89 2.32 -3.84 0.000124\n2 shock1 3.70 1.10 3.35 0.000797\n3 alcohol1 3.19 0.917 3.47 0.000514\n4 age 0.0898 0.0292 3.07 0.00211 \n5 bowelinf1 2.39 1.07 2.23 0.0260 \n```\n:::\n:::\n\n\n\n \n\n\n* Slopes in column `estimate`.\n\n\n\n## Multiplying the odds\n\n\n* Can interpret slopes by taking \"exp\" of them. We ignore intercept.\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsepsis.2.tidy %>% \n mutate(exp_coeff=exp(estimate)) %>% \n select(term, exp_coeff)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 5 x 2\n term exp_coeff\n \n1 (Intercept) 0.000137\n2 shock1 40.5 \n3 alcohol1 24.2 \n4 age 1.09 \n5 bowelinf1 10.9 \n```\n:::\n:::\n\n\n\n## Interpretation\n\n\\small\n\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 5 x 2\n term exp_coeff\n \n1 (Intercept) 0.000137\n2 shock1 40.5 \n3 alcohol1 24.2 \n4 age 1.09 \n5 bowelinf1 10.9 \n```\n:::\n:::\n\n\n\\normalsize\n\n\n* These say \"how much do you *multiply* odds of death by\nfor increase of 1 in corresponding risk factor?\" Or, what is odds\nratio for that factor being 1 (present) vs.\\ 0 (absent)?\n\n* Eg.\\ being alcoholic vs.\\ not increases odds of death by 24 times\n\n* One year older multiplies odds by about 1.1 times. Over 40 years,\nabout $1.09^{40}=31$ times. \n\n\n\n## Odds ratio and relative risk\n\n\n* **Relative risk** is ratio of probabilities.\n\n* Above: 90 of 100 men (0.9) drank wine, 20 of 100 women (0.2).\n\n* Relative risk 0.9/0.2=4.5. (odds ratio was 36).\n\n* When probabilities small, relative risk and odds ratio similar.\n\n* Eg.\\ prob of man having disease 0.02, woman 0.01.\n\n* Relative risk $0.02/0.01=2$.\n\n## Odds ratio vs.\\ relative risk\n\n- Odds for men and for women:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n(od1 <- 0.02 / 0.98) # men\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 0.02040816\n```\n:::\n\n```{.r .cell-code}\n(od2 <- 0.01 / 0.99) # women\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 0.01010101\n```\n:::\n:::\n\n\n\n- Odds ratio \n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nod1 / od2\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 2.020408\n```\n:::\n:::\n\n\n\n\n- Very close to relative risk of 2.\n\n\n## More than 2 response categories\n\n\n* With 2 response categories, model the probability of one, and prob of other is one minus that. So doesn't matter which category you model.\n\n* With more than 2 categories, have to think more carefully about the categories: are they\n\n\n* *ordered*: you can put them in a natural order (like low, medium, high)\n\n* *nominal*: ordering the categories doesn't make sense (like red, green, blue).\n\n\n* R handles both kinds of response; learn how.\n\n\n\n## Ordinal response: the miners\n\n\n* \nModel probability of being in given category *or lower*.\n\n* Example: coal-miners often suffer disease pneumoconiosis. Likelihood of disease believed to be greater \namong miners who have worked longer. \n\n* Severity of disease measured on categorical scale: none,\nmoderate, severe.\n\n## Miners data\n\n* Data are frequencies:\n\n```\nExposure None Moderate Severe\n5.8 98 0 0\n15.0 51 2 1\n21.5 34 6 3\n27.5 35 5 8\n33.5 32 10 9\n39.5 23 7 8\n46.0 12 6 10\n51.5 4 2 5\n```\n\n\n\n\n## Reading the data\n\nData in aligned columns with more than one space between, so: \n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \"http://ritsokiguess.site/datafiles/miners-tab.txt\"\nfreqs <- read_table(my_url)\n```\n:::\n\n\n\\normalsize\n\n\n## The data\n\n\n::: {.cell}\n\n```{.r .cell-code}\nfreqs\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 8 x 4\n Exposure None Moderate Severe\n \n1 5.8 98 0 0\n2 15 51 2 1\n3 21.5 34 6 3\n4 27.5 35 5 8\n5 33.5 32 10 9\n6 39.5 23 7 8\n7 46 12 6 10\n8 51.5 4 2 5\n```\n:::\n:::\n\n\n\n \n\n\n## Tidying \n\n\n::: {.cell}\n\n```{.r .cell-code}\nfreqs %>%\n pivot_longer(-Exposure, names_to = \"Severity\", values_to = \"Freq\") %>%\n mutate(Severity = fct_inorder(Severity)) -> miners\n```\n:::\n\n\n\n \n\n\n## Result\n\n\\tiny\n\n\n::: {.cell}\n\n```{.r .cell-code}\nminers\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 24 x 3\n Exposure Severity Freq\n \n 1 5.8 None 98\n 2 5.8 Moderate 0\n 3 5.8 Severe 0\n 4 15 None 51\n 5 15 Moderate 2\n 6 15 Severe 1\n 7 21.5 None 34\n 8 21.5 Moderate 6\n 9 21.5 Severe 3\n10 27.5 None 35\n# i 14 more rows\n```\n:::\n:::\n\n\n\\normalsize\n \n\n\n## Plot proportions against exposure\n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nminers %>% \n group_by(Exposure) %>% \n mutate(proportion = Freq / sum(Freq)) -> prop\nggplot(prop, aes(x = Exposure, y = proportion,\n colour = Severity)) + \n geom_point() + geom_smooth(se = F)\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-beamer/bLogistic-28-1.pdf)\n:::\n:::\n\n\n\\normalsize\n\n\n## Reminder of data setup\n\\footnotesize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nminers\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 24 x 3\n Exposure Severity Freq\n \n 1 5.8 None 98\n 2 5.8 Moderate 0\n 3 5.8 Severe 0\n 4 15 None 51\n 5 15 Moderate 2\n 6 15 Severe 1\n 7 21.5 None 34\n 8 21.5 Moderate 6\n 9 21.5 Severe 3\n10 27.5 None 35\n# i 14 more rows\n```\n:::\n:::\n\n\n\n \n\\normalsize\n\n\n\n## Fitting ordered logistic model\n\nUse function `polr` from package `MASS`. Like `glm`.\n\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsev.1 <- polr(Severity ~ Exposure,\n weights = Freq,\n data = miners\n)\n```\n:::\n\n\n \n\n\n## Output: not very illuminating\n\n\n\\scriptsize\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsev.1 <- polr(Severity ~ Exposure,\n weights = Freq,\n data = miners,\n Hess = TRUE\n)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(sev.1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nCall:\npolr(formula = Severity ~ Exposure, data = miners, weights = Freq, \n Hess = TRUE)\n\nCoefficients:\n Value Std. Error t value\nExposure 0.0959 0.01194 8.034\n\nIntercepts:\n Value Std. Error t value\nNone|Moderate 3.9558 0.4097 9.6558\nModerate|Severe 4.8690 0.4411 11.0383\n\nResidual Deviance: 416.9188 \nAIC: 422.9188 \n```\n:::\n:::\n\n\n\\normalsize\n \n\n## Does exposure have an effect?\nFit model without `Exposure`, and compare\nusing `anova`. Note `1` for model with just intercept:\n\n\n\n::: {.cell}\n\n:::\n\n\n\n \n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsev.0 <- polr(Severity ~ 1, weights = Freq, data = miners)\nanova(sev.0, sev.1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nLikelihood ratio tests of ordinal regression models\n\nResponse: Severity\n Model Resid. df Resid. Dev Test Df LR stat.\n1 1 369 505.1621 \n2 Exposure 368 416.9188 1 vs 2 1 88.24324\n Pr(Chi)\n1 \n2 0\n```\n:::\n:::\n\n\n\\normalsize\n\nExposure definitely has effect on severity of disease. \n\n\n## Another way\n\n\n* What (if anything) can we drop from model with `exposure`?\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndrop1(sev.1, test = \"Chisq\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nSingle term deletions\n\nModel:\nSeverity ~ Exposure\n Df AIC LRT Pr(>Chi) \n 422.92 \nExposure 1 509.16 88.243 < 2.2e-16 ***\n---\nSignif. codes: \n0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n```\n:::\n:::\n\n\n\n \n\n\n* Nothing. Exposure definitely has effect.\n\n\n\n## Predicted probabilities 1/2\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nfreqs %>% select(Exposure) -> new\nnew\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 8 x 1\n Exposure\n \n1 5.8\n2 15 \n3 21.5\n4 27.5\n5 33.5\n6 39.5\n7 46 \n8 51.5\n```\n:::\n:::\n\n\n\n\n## Predicted probabilities 2/2\n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(sev.1, newdata = new)) %>%\n select(group, estimate, Exposure) %>% \n pivot_wider(names_from = group, values_from = estimate)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 8 x 4\n Exposure None Moderate Severe\n \n1 5.8 0.968 0.0191 0.0132\n2 15 0.925 0.0433 0.0314\n3 21.5 0.869 0.0739 0.0569\n4 27.5 0.789 0.114 0.0969\n5 33.5 0.678 0.162 0.160 \n6 39.5 0.542 0.205 0.253 \n7 46 0.388 0.224 0.388 \n8 51.5 0.272 0.210 0.517 \n```\n:::\n:::\n\n\n\\normalsize\n\n## Plot of predicted probabilities\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_predictions(model = sev.1, condition = c(\"Exposure\", \"group\"),\n type = \"probs\") +\n geom_point(data = prop, aes(x = Exposure, y = proportion, \n colour = Severity)) -> ggg\n```\n:::\n\n\n\n## The graph\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggg\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-beamer/unnamed-chunk-11-1.pdf)\n:::\n:::\n\n\n\n\n## Comments\n\n\n* Model appears to match data well enough.\n\n- As exposure goes up, prob of None\ngoes down, Severe goes up (sharply for high exposure).\n\n- So more exposure means worse disease.\n\n\n## Unordered responses\n\n\n* With unordered (nominal) responses, can use *generalized logit*.\n\n* Example: 735 people, record age and sex (male 0, female 1), which of 3 brands of some product preferred.\n\n* Data in `mlogit.csv` separated by commas (so\n`read_csv` will work):\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \"http://ritsokiguess.site/datafiles/mlogit.csv\"\nbrandpref <- read_csv(my_url)\n```\n:::\n\n\n \n\n\n\n\n## The data (some)\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbrandpref\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 735 x 3\n brand sex age\n \n 1 1 0 24\n 2 1 0 26\n 3 1 0 26\n 4 1 1 27\n 5 1 1 27\n 6 3 1 27\n 7 1 0 27\n 8 1 0 27\n 9 1 1 27\n10 1 0 27\n# i 725 more rows\n```\n:::\n:::\n\n\n\n \n\n\n## Bashing into shape\n\n\n* `sex` and `brand` not meaningful as numbers, so\nturn into factors:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbrandpref %>%\n mutate(sex = ifelse(sex == 1, \"female\", \"male\"), \n sex = factor(sex),\n brand = factor(brand)\n ) -> brandpref\n```\n:::\n\n\n \n \n## Fitting model \n\n* We use `multinom` from package `nnet`. Works\nlike `polr`.\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbrands.1 <- multinom(brand ~ age + sex, data = brandpref)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# weights: 12 (6 variable)\ninitial value 807.480032 \niter 10 value 702.990572\nfinal value 702.970704 \nconverged\n```\n:::\n:::\n\n\n \n\n\n## Can we drop anything?\n\n\n* Unfortunately `drop1` seems not to work:\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndrop1(brands.1, test = \"Chisq\", trace = 0)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\ntrying - age \n```\n:::\n\n::: {.cell-output .cell-output-error}\n```\nError in if (trace) {: argument is not interpretable as logical\n```\n:::\n:::\n\n\n\n* So, fall back on fitting model without what you want to test, and\ncomparing using `anova`. \n\n\n\n## Do age/sex help predict brand? 1/3\n\nFit models without each of age and sex:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbrands.2 <- multinom(brand ~ age, data = brandpref)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# weights: 9 (4 variable)\ninitial value 807.480032 \niter 10 value 706.796323\niter 10 value 706.796322\nfinal value 706.796322 \nconverged\n```\n:::\n\n```{.r .cell-code}\nbrands.3 <- multinom(brand ~ sex, data = brandpref)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# weights: 9 (4 variable)\ninitial value 807.480032 \nfinal value 791.861266 \nconverged\n```\n:::\n:::\n\n\n \n\n\n## Do age/sex help predict brand? 2/3\n\n\\scriptsize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nanova(brands.2, brands.1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nLikelihood ratio tests of Multinomial Models\n\nResponse: brand\n Model Resid. df Resid. Dev Test Df LR stat.\n1 age 1466 1413.593 \n2 age + sex 1464 1405.941 1 vs 2 2 7.651236\n Pr(Chi)\n1 \n2 0.02180496\n```\n:::\n\n```{.r .cell-code}\nanova(brands.3, brands.1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nLikelihood ratio tests of Multinomial Models\n\nResponse: brand\n Model Resid. df Resid. Dev Test Df LR stat.\n1 sex 1466 1583.723 \n2 age + sex 1464 1405.941 1 vs 2 2 177.7811\n Pr(Chi)\n1 \n2 0\n```\n:::\n:::\n\n\n\\normalsize\n\n\n## Do age/sex help predict brand? 3/3\n\n\n* `age` definitely significant (second `anova`)\n\n* `sex` significant also (first `anova`), though P-value less dramatic\n\n* Keep both.\n- Expect to see a large effect of `age`, and a smaller one of `sex`.\n\n\n\n\n## Another way to build model\n\n\n* Start from model with everything and run `step`:\n\n\n\n::: {.cell}\n\n:::\n\n\n\n\n\\footnotesize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nstep(brands.1, trace = 0)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\ntrying - age \ntrying - sex \n```\n:::\n\n::: {.cell-output .cell-output-stdout}\n```\nCall:\nmultinom(formula = brand ~ age + sex)\n\nCoefficients:\n (Intercept) age sexmale\n2 -11.25127 0.3682202 -0.5237736\n3 -22.25571 0.6859149 -0.4658215\n\nResidual Deviance: 1405.941 \nAIC: 1417.941 \n```\n:::\n:::\n\n\n\\normalsize\n \n\n* Final model contains both `age` and `sex` so neither\ncould be removed.\n\n\n## Making predictions\n\nFind age 5-number summary, and the two sexes:\n\n\\small\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(brandpref)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n brand sex age \n 1:207 female:466 Min. :24.0 \n 2:307 male :269 1st Qu.:32.0 \n 3:221 Median :32.0 \n Mean :32.9 \n 3rd Qu.:34.0 \n Max. :38.0 \n```\n:::\n:::\n\n\n\nSpace the ages out a bit for prediction (see over).\n\n\\normalsize\n\n## Combinations\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnew <- datagrid(age = c(24, 30, 33, 35, 38), \n sex = c(\"female\", \"male\"), model = brands.1)\nnew\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n brand age sex\n1 2 24 female\n2 2 24 male\n3 2 30 female\n4 2 30 male\n5 2 33 female\n6 2 33 male\n7 2 35 female\n8 2 35 male\n9 2 38 female\n10 2 38 male\n```\n:::\n:::\n\n\n\n## The predictions\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncbind(predictions(brands.1, newdata = new)) %>%\n select(group, estimate, age, sex) %>% \n pivot_wider(names_from = group, values_from = estimate)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 10 x 5\n age sex `1` `2` `3`\n \n 1 24 female 0.915 0.0819 0.00279\n 2 24 male 0.948 0.0502 0.00181\n 3 30 female 0.500 0.407 0.0933 \n 4 30 male 0.625 0.302 0.0732 \n 5 33 female 0.203 0.500 0.297 \n 6 33 male 0.296 0.432 0.272 \n 7 35 female 0.0840 0.432 0.484 \n 8 35 male 0.131 0.397 0.472 \n 9 38 female 0.0162 0.252 0.732 \n10 38 male 0.0260 0.239 0.735 \n```\n:::\n:::\n\n\n\n## Comments\n\n* Young males prefer brand 1, \nbut older males prefer brand 3.\n\n* Females similar, but like brand 1 less and\nbrand 2 more.\n\n- A clear `brand` effect, but the `sex` effect is less clear. \n\n## Making a plot\n\n- `plot_predictions` doesn't quite work\n- so don't draw, edit, *then* make graph:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_predictions(brands.1, condition = c(\"age\", \"brand\", \"sex\"), \n type = \"probs\", draw = FALSE) %>% \n ggplot(aes(x = age, y = estimate, colour = group, \n linetype = sex)) +\n geom_line() -> g\n```\n:::\n\n\n\n## The graph\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ng\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-beamer/unnamed-chunk-16-1.pdf)\n:::\n:::\n\n\n \n\n## Digesting the plot\n\n\n* Brand vs.\\ age: younger people (of both genders) prefer brand\n1, but older people (of both genders) prefer brand 3. (Explains\nsignificant age effect.)\n\n* Brand vs.\\ sex: females (solid) like brand 1 less than males\n(dashed), like brand 2 more (for all ages). \n\n* Not much brand difference between genders (solid and dashed\nlines of same colours close), but enough to be significant.\n\n* Model didn't include interaction, so modelled effect of gender\non brand same for each age, modelled effect of age same for each\ngender. (See also later.) \n\n\n## Alternative data format\n\nSummarize all people of same brand preference, same sex, same age on one line of data file with frequency on end:\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbrandpref\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 735 x 3\n brand sex age\n \n 1 1 male 24\n 2 1 male 26\n 3 1 male 26\n 4 1 female 27\n 5 1 female 27\n 6 3 female 27\n 7 1 male 27\n 8 1 male 27\n 9 1 female 27\n10 1 male 27\n# i 725 more rows\n```\n:::\n:::\n\n\n\n\n```\n1 0 24 1\n1 0 26 2\n1 0 27 4\n1 0 28 4\n1 0 29 7\n1 0 30 3\n...\n```\n\nWhole data set in 65 lines not 735! But how?\n\n\n## Getting alternative data format\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbrandpref %>%\n group_by(age, sex, brand) %>%\n summarize(Freq = n()) %>%\n ungroup() -> b\nb\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 65 x 4\n age sex brand Freq\n \n 1 24 male 1 1\n 2 26 male 1 2\n 3 27 female 1 4\n 4 27 female 3 1\n 5 27 male 1 4\n 6 28 female 1 6\n 7 28 female 2 2\n 8 28 female 3 1\n 9 28 male 1 4\n10 28 male 3 2\n# i 55 more rows\n```\n:::\n:::\n\n\n\n \n\n## Fitting models, almost the same\n\n\n* Just have to remember `weights` to incorporate\nfrequencies.\n\n* Otherwise `multinom` assumes you have just 1 obs\non each line!\n\n* Again turn (numerical) `sex` and `brand` into factors:\n\n\\footnotesize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nb %>%\n mutate(sex = factor(sex)) %>%\n mutate(brand = factor(brand)) -> bf\nb.1 <- multinom(brand ~ age + sex, data = bf, weights = Freq)\nb.2 <- multinom(brand ~ age, data = bf, weights = Freq)\n```\n:::\n\n\n\\normalsize\n\n\n\n## P-value for `sex` identical\n\n\\footnotesize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nanova(b.2, b.1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nLikelihood ratio tests of Multinomial Models\n\nResponse: brand\n Model Resid. df Resid. Dev Test Df LR stat.\n1 age 126 1413.593 \n2 age + sex 124 1405.941 1 vs 2 2 7.651236\n Pr(Chi)\n1 \n2 0.02180496\n```\n:::\n:::\n\n\n\\normalsize\n\nSame P-value as before, so we haven't changed anything important.\n\n\n\n\n## Trying interaction between age and gender\n\n\n\n::: {.cell}\n\n:::\n\n\n\n \n\\scriptsize\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbrands.4 <- update(brands.1, . ~ . + age:sex)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# weights: 15 (8 variable)\ninitial value 807.480032 \niter 10 value 703.191146\niter 20 value 702.572260\niter 30 value 702.570900\niter 30 value 702.570893\niter 30 value 702.570893\nfinal value 702.570893 \nconverged\n```\n:::\n\n```{.r .cell-code}\nanova(brands.1, brands.4)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nLikelihood ratio tests of Multinomial Models\n\nResponse: brand\n Model Resid. df Resid. Dev Test Df\n1 age + sex 1464 1405.941 \n2 age + sex + age:sex 1462 1405.142 1 vs 2 2\n LR stat. Pr(Chi)\n1 \n2 0.7996223 0.6704466\n```\n:::\n:::\n\n\n\n \n\\normalsize\n\n* No evidence that effect of age on brand preference differs for\nthe two genders.\n\n## Make graph again\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplot_predictions(brands.4, condition = c(\"age\", \"brand\", \"sex\"), \n type = \"probs\", draw = FALSE) %>% \n ggplot(aes(x = age, y = estimate, colour = group, \n linetype = sex)) +\n geom_line() -> g4\n```\n:::\n\n\n\n\n## Not much difference in the graph\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ng4\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-beamer/unnamed-chunk-19-1.pdf)\n:::\n:::\n\n\n\n## Compare model without interaction\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ng\n```\n\n::: {.cell-output-display}\n![](logistic_files/figure-beamer/unnamed-chunk-20-1.pdf)\n:::\n:::\n", "supporting": [ "logistic_files/figure-beamer" ], diff --git a/_freeze/logistic/figure-beamer/bLogistic-28-1.pdf b/_freeze/logistic/figure-beamer/bLogistic-28-1.pdf index 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On a graph

-
plot_cap(status.1, condition = "dose")
+
plot_predictions(status.1, condition = "dose")
-
+

The rats, more

    @@ -2731,7 +2731,7 @@

    Predicted survival probs

    @@ -3304,7 +3304,7 @@

    Predicted probabilities 2/2

    Plot of predicted probabilities

    -
    plot_cap(model = sev.1, condition = c("Exposure", "group"),
+
    plot_predictions(model = sev.1, condition = c("Exposure", "group"),
          type = "probs") +
   geom_point(data = prop, aes(x = Exposure, y = proportion, 
                               colour = Severity)) -> ggg
    
@@ -3316,7 +3316,7 @@ 
    The graph
    
 
    ggg
    -
    +

Comments

ggplot(asphalt, aes(y = rut.depth, x = log(viscosity))) +
-  geom_point() + geom_smooth(se = F)
+ geom_point() + geom_smooth(se = F) -> g

(plot overleaf)

Rut depth against log-viscosity

- -
+

Comments and next steps

    @@ -2595,13 +2594,40 @@

    Comments and next steps

rut.1 <- lm(rut.depth ~ pct.a.surf + pct.a.base + fines +
-  voids + log(viscosity) + run, data = asphalt)
+ voids + log(viscosity) + run, data = asphalt) +summary(rut.1)
+
+

+Call:
+lm(formula = rut.depth ~ pct.a.surf + pct.a.base + fines + voids + 
+    log(viscosity) + run, data = asphalt)
+
+Residuals:
+    Min      1Q  Median      3Q     Max 
+-4.1211 -1.9075 -0.7175  1.6382  9.5947 
+
+Coefficients:
+               Estimate Std. Error t value Pr(>|t|)   
+(Intercept)    -12.9937    26.2188  -0.496   0.6247   
+pct.a.surf       3.9706     2.4966   1.590   0.1248   
+pct.a.base       1.2631     3.9703   0.318   0.7531   
+fines            0.1164     1.0124   0.115   0.9094   
+voids            0.5893     1.3244   0.445   0.6604   
+log(viscosity)  -3.1515     0.9194  -3.428   0.0022 **
+run             -1.9655     3.6472  -0.539   0.5949   
+---
+Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+
+Residual standard error: 3.324 on 24 degrees of freedom
+Multiple R-squared:  0.806, Adjusted R-squared:  0.7575 
+F-statistic: 16.62 on 6 and 24 DF,  p-value: 1.743e-07
+

Regression output: summary(rut.1) or:

-
glance(rut.1)
+
glance(rut.1)
@@ -2610,7 +2636,7 @@

Regression output: summary(rut.1) or:

-
tidy(rut.1)
+
tidy(rut.1)
@@ -2624,20 +2650,18 @@

Regression output: summary(rut.1) or:

Comments

    -
  • R-squared 81%, not so bad.
    • -
  • P-value in glance asserts that something helping to predict rut.depth.
    • -
  • Table of coefficients says log(viscosity).
    • -
  • But confused by clearly non-significant variables: remove those to get clearer picture of what is helpful.
    • -
  • Before we do anything, look at residual plots: +

  • R-squared 81%, not so bad.

    • +

  • P-value in glance asserts that something helping to predict rut.depth.

    • +

  • Table of coefficients says log(viscosity).

    • +

  • But confused by clearly non-significant variables: remove those to get clearer picture of what is helpful.

    • +

  • Before we do anything, look at residual plots:

    +
    (a) of residuals against fitted values (as usual)
      -
      • -
      1. of residuals against fitted values (as usual)
        2. -
      1. of residuals against each explanatory.
    • -
  • Problem fixes: +

  • Problem fixes:

    • with (a): fix response variable;
    • with some plots in (b): fix those explanatory variables.
      • @@ -2647,10 +2671,17 @@

      Comments

      Plot fitted values against residuals

      -
      ggplot(rut.1, aes(x = .fitted, y = .resid)) + geom_point()
      +
      ggplot(rut.1, aes(x = .fitted, y = .resid)) + geom_point()
      +
      +

      Normal quantile plot of residuals

      +
      +
      ggplot(rut.1, aes(sample = .resid)) + stat_qq() + stat_qq_line()
      + +
      +

      Plotting residuals against \(x\) variables

        @@ -2658,13 +2689,13 @@

        Plotting residuals against \(x\) variables<
      • Package broom contains a function augment that combines these two together so that they can later be plotted: start with a model first, and then augment with a data frame:

      -
      rut.1 %>% augment(asphalt) -> rut.1a
      +
      rut.1 %>% augment(asphalt) -> rut.1a

      What does rut.1a contain?

      -
      names(rut.1a)
      +
      names(rut.1a)
       [1] "pct.a.surf" "pct.a.base" "fines"      "voids"      "rut.depth" 
  [6] "viscosity"  "run"        ".fitted"    ".resid"     ".hat"      
@@ -2679,20 +2710,20 @@ 
      What does rut.1a contain?
      
 
      
 
      Plotting residuals against \(x\)-variables
      
 
      
-
      rut.1a %>%
-  mutate(log_vis=log(viscosity)) %>% 
-  pivot_longer(
-    c(pct.a.surf:voids, run, log_vis),
-    names_to="xname", values_to="x"
-  ) %>%
-  ggplot(aes(x = x, y = .resid)) +
-  geom_point() + facet_wrap(~xname, scales = "free") -> g
      
+
      rut.1a %>%
+  mutate(log_vis=log(viscosity)) %>% 
+  pivot_longer(
+    c(pct.a.surf:voids, run, log_vis),
+    names_to="xname", values_to="x"
+  ) %>%
+  ggplot(aes(x = x, y = .resid)) +
+  geom_point() + facet_wrap(~xname, scales = "free") -> g
      
 
      
 
      
 
      
 
      The plot
      
 
      
-
      g
      
+
      g
      
 
 
      
 
      
@@ -2719,8 +2750,8 @@ 
      Which transformation?
      
 
      Running Box-Cox
      
 
      From package MASS:
      
 
      
-
      boxcox(rut.depth ~ pct.a.surf + pct.a.base + fines + voids +
-  log(viscosity) + run, data = asphalt)
      
+
      boxcox(rut.depth ~ pct.a.surf + pct.a.base + fines + voids +
+  log(viscosity) + run, data = asphalt)
      @@ -2745,20 +2776,20 @@

      Relationships with explanatories

    • As before: plot response (now log(rut.depth)) against other explanatory variables, all in one shot:
    -
    asphalt %>%
-  mutate(log_vis=log(viscosity)) %>% 
-  pivot_longer(
-    c(pct.a.surf:voids, run, log_vis),
-    names_to="xname", values_to="x"
-  ) %>%
-  ggplot(aes(y = log(rut.depth), x = x)) + geom_point() +
-  facet_wrap(~xname, scales = "free") -> g3
    +
    asphalt %>%
+  mutate(log_vis=log(viscosity)) %>% 
+  pivot_longer(
+    c(pct.a.surf:voids, run, log_vis),
+    names_to="xname", values_to="x"
+  ) %>%
+  ggplot(aes(y = log(rut.depth), x = x)) + geom_point() +
+  facet_wrap(~xname, scales = "free") -> g3

The new plots

-
g3
+
g3
@@ -2772,8 +2803,8 @@

Modelling with transformed response

  • Model log.rut.depth in terms of everything else, see what can be removed:
    • -
    rut.2 <- lm(log(rut.depth) ~ pct.a.surf + pct.a.base +
-  fines + voids + log(viscosity) + run, data = asphalt)
    +
    rut.2 <- lm(log(rut.depth) ~ pct.a.surf + pct.a.base +
+  fines + voids + log(viscosity) + run, data = asphalt)
    • use tidy from broom to display just the coefficients.
      • @@ -2782,7 +2813,7 @@

      Modelling with transformed response

      Output

      -
      tidy(rut.2)
      +
      tidy(rut.2)
      @@ -2791,6 +2822,33 @@

      Output

      +
      summary(rut.2)
      +
      +
      
+Call:
+lm(formula = log(rut.depth) ~ pct.a.surf + pct.a.base + fines + 
+    voids + log(viscosity) + run, data = asphalt)
+
+Residuals:
+     Min       1Q   Median       3Q      Max 
+-0.53072 -0.18563 -0.00003  0.20017  0.55079 
+
+Coefficients:
+               Estimate Std. Error t value Pr(>|t|)    
+(Intercept)    -1.57299    2.43617  -0.646    0.525    
+pct.a.surf      0.58358    0.23198   2.516    0.019 *  
+pct.a.base     -0.10337    0.36891  -0.280    0.782    
+fines           0.09775    0.09407   1.039    0.309    
+voids           0.19885    0.12306   1.616    0.119    
+log(viscosity) -0.55769    0.08543  -6.528 9.45e-07 ***
+run             0.34005    0.33889   1.003    0.326    
+---
+Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+
+Residual standard error: 0.3088 on 24 degrees of freedom
+Multiple R-squared:  0.961, Adjusted R-squared:  0.9512 
+F-statistic: 98.47 on 6 and 24 DF,  p-value: 1.059e-15
      +
      @@ -2799,13 +2857,35 @@

      Taking out everything non-significant

    • Try: remove everything but pct.a.surf and log.viscosity:
    -
    rut.3 <- lm(log(rut.depth) ~ pct.a.surf + log(viscosity), data = asphalt)
    +
    rut.3 <- lm(log(rut.depth) ~ pct.a.surf + log(viscosity), data = asphalt)
+summary(rut.3)
    +
    +
    
+Call:
+lm(formula = log(rut.depth) ~ pct.a.surf + log(viscosity), data = asphalt)
+
+Residuals:
+     Min       1Q   Median       3Q      Max 
+-0.61938 -0.21361  0.06635  0.14932  0.63012 
+
+Coefficients:
+               Estimate Std. Error t value Pr(>|t|)    
+(Intercept)     0.90014    1.08059   0.833   0.4119    
+pct.a.surf      0.39115    0.21879   1.788   0.0846 .  
+log(viscosity) -0.61856    0.02713 -22.797   <2e-16 ***
+---
+Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+
+Residual standard error: 0.3208 on 28 degrees of freedom
+Multiple R-squared:  0.9509,    Adjusted R-squared:  0.9474 
+F-statistic: 270.9 on 2 and 28 DF,  p-value: < 2.2e-16
    +
    • Check that removing all those variables wasn’t too much:
    -
    anova(rut.3, rut.2)
    +
    anova(rut.3, rut.2)
    @@ -2823,7 +2903,7 @@

    Taking out everything non-significant

    Find the largest P-value by eye:

    -
    tidy(rut.2)
    +
    tidy(rut.2)
    @@ -2845,7 +2925,7 @@

    Get the computer to find the largest P-value for you

  • Output from tidy is itself a data frame, thus:
    • -
    tidy(rut.2) %>% arrange(p.value)
    +
    tidy(rut.2) %>% arrange(p.value)
    @@ -2865,14 +2945,14 @@

    Take out pct.a.base

  • Copy and paste the lm code and remove what you’re removing:
    • -
    rut.4 <- lm(log(rut.depth) ~ pct.a.surf + fines + voids + 
-              log(viscosity) + run, data = asphalt)
-tidy(rut.4) %>% arrange(p.value)
    +
    rut.4 <- lm(log(rut.depth) ~ pct.a.surf + fines + voids + 
+              log(viscosity) + run, data = asphalt)
+tidy(rut.4) %>% arrange(p.value) %>% dplyr::select(term, p.value)
    @@ -2885,8 +2965,8 @@

    Take out pct.a.base

    “Update”

    Another way to do the same thing:

    -
    rut.4 <- update(rut.2, . ~ . - pct.a.base)
-tidy(rut.4) %>% arrange(p.value)
    +
    rut.4 <- update(rut.2, . ~ . - pct.a.base)
+tidy(rut.4) %>% arrange(p.value)
    @@ -2903,13 +2983,13 @@

    “Update”

    Take out fines:

    -
    rut.5 <- update(rut.4, . ~ . - fines)
-tidy(rut.5) %>% arrange(p.value)
    +
    rut.5 <- update(rut.4, . ~ . - fines)
+tidy(rut.5) %>% arrange(p.value) %>% dplyr::select(term, p.value)
    @@ -2919,13 +2999,13 @@

    Take out fines:

    Take out run:

    -
    rut.6 <- update(rut.5, . ~ . - run)
-tidy(rut.6) %>% arrange(p.value)
    +
    rut.6 <- update(rut.5, . ~ . - run)
+tidy(rut.6) %>% arrange(p.value) %>% dplyr::select(term, p.value)
    @@ -2939,12 +3019,36 @@

    Comments

  • Different final result from taking things out one at a time (top), than by taking out 4 at once (bottom):
    • -
    coef(rut.6)
    +
    summary(rut.6)
    +
    +
    
+Call:
+lm(formula = log(rut.depth) ~ pct.a.surf + voids + log(viscosity), 
+    data = asphalt)
+
+Residuals:
+     Min       1Q   Median       3Q      Max 
+-0.53548 -0.20181 -0.01702  0.16748  0.54707 
+
+Coefficients:
+               Estimate Std. Error t value Pr(>|t|)    
+(Intercept)    -1.02079    1.36430  -0.748   0.4608    
+pct.a.surf      0.55547    0.22044   2.520   0.0180 *  
+voids           0.24479    0.11560   2.118   0.0436 *  
+log(viscosity) -0.64649    0.02879 -22.458   <2e-16 ***
+---
+Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+
+Residual standard error: 0.3025 on 27 degrees of freedom
+Multiple R-squared:  0.9579,    Adjusted R-squared:  0.9532 
+F-statistic: 204.6 on 3 and 27 DF,  p-value: < 2.2e-16
    +
    +
    coef(rut.6)
       (Intercept)     pct.a.surf          voids log(viscosity) 
     -1.0207945      0.5554686      0.2447934     -0.6464911
    -
    coef(rut.3)
    +
    coef(rut.3)
       (Intercept)     pct.a.surf log(viscosity) 
      0.9001389      0.3911481     -0.6185628
    @@ -2963,7 +3067,7 @@

    Comments on variable selection

  • R has function step that does backward selection, like this:
    • -
    step(rut.2, direction = "backward", test = "F")
    +
    step(rut.2, direction = "backward", test = "F")

    Gets same answer as we did (by removing least significant x).

      @@ -2975,18 +3079,18 @@

      Comments on variable selection

      All possible regressions (output over)

      Uses package leaps:

      -
      leaps <- regsubsets(log(rut.depth) ~ pct.a.surf + 
-                      pct.a.base + fines + voids + 
-                      log(viscosity) + run, 
-                    data = asphalt, nbest = 2)
-s <- summary(leaps)
-with(s, data.frame(rsq, outmat)) -> d
      +
      leaps <- regsubsets(log(rut.depth) ~ pct.a.surf + 
+                      pct.a.base + fines + voids + 
+                      log(viscosity) + run, 
+                    data = asphalt, nbest = 2)
+s <- summary(leaps)
+with(s, data.frame(rsq, outmat)) -> d

    The output

    -
    d %>% rownames_to_column("model") %>% arrange(desc(rsq))
    +
    d %>% rownames_to_column("model") %>% arrange(desc(rsq))
    @@ -3008,9 +3112,9 @@

    Comments

    All possible regressions, adjusted R-squared

    -
    with(s, data.frame(adjr2, outmat)) %>% 
-  rownames_to_column("model") %>% 
-  arrange(desc(adjr2))
    +
    with(s, data.frame(adjr2, outmat)) %>% 
+  rownames_to_column("model") %>% 
+  arrange(desc(adjr2))
    @@ -3027,7 +3131,7 @@

    Revisiting the best model

  • Best model was our rut.6:
    • -
    tidy(rut.6)
    +
    tidy(rut.6)
    @@ -3045,37 +3149,45 @@

    Revisiting (2)

  • We should check residual plots again, though previous scatterplots say it’s unlikely that there will be a problem:
    • -
    g <- ggplot(rut.6, aes(y = .resid, x = .fitted)) + 
-geom_point()
    +
    g <- ggplot(rut.6, aes(y = .resid, x = .fitted)) + 
+geom_point()

    Residuals against fitted values

    -
    g
    - +
    g
    +
    +

    +
    +
    +
    +
    ggplot(rut.6, aes(sample = .resid)) + stat_qq() + stat_qq_line()
    +
    +

    -
    +
    +

    Plotting residuals against x’s

    • Do our trick again to put them all on one plot:
    -
    augment(rut.6, asphalt) %>%
-  mutate(log_vis=log(viscosity)) %>% 
-  pivot_longer(
-    c(pct.a.surf:voids, run, log_vis),
-    names_to="xname", values_to="x",
-  ) %>%
-  ggplot(aes(y = .resid, x = x)) + geom_point() +
-  facet_wrap(~xname, scales = "free") -> g2
    +
    augment(rut.6, asphalt) %>%
+  mutate(log_vis=log(viscosity)) %>% 
+  pivot_longer(
+    c(pct.a.surf:voids, run, log_vis),
+    names_to="xname", values_to="x",
+  ) %>%
+  ggplot(aes(y = .resid, x = x)) + geom_point() +
+  facet_wrap(~xname, scales = "free") -> g2

    Residuals against the x’s

    -
    g2
    +
    g2
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    Functions

    Packages for this section

    library(tidyverse)
-library(broom)
    +library(broom) +# install.packages("vctrs")

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100644 --- a/functions.html +++ b/functions.html @@ -2488,7 +2488,8 @@

    @@ -2568,6 +2569,17 @@

    About the input; testing

    [1] 1.414214
    +
    +
    q <- 17
+sqrt_minus_1(q)
    +
    +
    [1] 4
    +
    +
    sqrt_minus_1("text")
    +
    +
    Error in x - 1: non-numeric argument to binary operator
    +
    +
    • It works!
    @@ -2578,7 +2590,7 @@

    Vectorization 1/2

  • We conceived our function to work on numbers:
    • -
    sqrt_minus_1(3.25)
    +
    sqrt_minus_1(3.25)
    [1] 1.5
    @@ -2587,7 +2599,7 @@

    Vectorization 1/2

  • but it actually works on vectors too, as a free bonus of R:
    • -
    sqrt_minus_1(c(50, 11, 3))
    +
    sqrt_minus_1(c(50, 11, 3))
    [1] 7.000000 3.162278 1.414214
    @@ -2602,8 +2614,17 @@

    Vectorization 2/2

  • or even data frames:
    • -
    d <- tibble(x = 1:2, y = 3:4)
-sqrt_minus_1(d)
    +
    d <- data.frame(x = 1:2, y = 3:4)
+d
    +
    + +
    + +
    +
    +
    sqrt_minus_1(d)
    @@ -2620,15 +2641,15 @@

    More than one input

  • Allow the value to be subtracted, before taking square root, to be input to function as well, thus:
    • -
    sqrt_minus_value <- function(x, d) {
-  sqrt(x - d)
-}
    +
    sqrt_minus_value <- function(x, d) {
+  sqrt(x - d)
+}
    • Call the function with the x and d inputs in the right order:
    -
    sqrt_minus_value(51, 2)
    +
    sqrt_minus_value(51, 2)
    [1] 7
    @@ -2637,11 +2658,23 @@

    More than one input

  • or give the inputs names, in which case they can be in any order:
    • -
    sqrt_minus_value(d = 2, x = 51)
    +
    sqrt_minus_value(d = 2, x = 51)
    [1] 7
    +
    +
    lm(y ~ x, data = d)
    +
    +
    
+Call:
+lm(formula = y ~ x, data = d)
+
+Coefficients:
+(Intercept)            x  
+          2            1
    +
    +

    Defaults 1/2

    @@ -2649,12 +2682,12 @@

    Defaults 1/2

  • Many R functions have values that you can change if you want to, but usually you don’t want to, for example:
    • -
    x <- c(3, 4, 5, NA, 6, 7)
-mean(x)
    +
    x <- c(3, 4, 5, NA, 6, 7)
+mean(x)
    [1] NA
    -
    mean(x, na.rm = TRUE)
    +
    mean(x, na.rm = TRUE)
    [1] 5
    @@ -2670,19 +2703,19 @@

    Defaults 2/2

  • In our function, set a default value for d like this:
    • -
    sqrt_minus_value <- function(x, d = 1) {
-  sqrt(x - d)
-}
    +
    sqrt_minus_value <- function(x, d = 1) {
+  sqrt(x - d)
+}
    • If you specify a value for d, it will be used. If you don’t, 1 will be used instead:
    -
    sqrt_minus_value(51, 2)
    +
    sqrt_minus_value(51, 2)
    [1] 7
    -
    sqrt_minus_value(51)
    +
    sqrt_minus_value(51)
    [1] 7.071068
    @@ -2694,7 +2727,7 @@

    Catching errors before they happen

  • What happened here?
    • -
    sqrt_minus_value(6, 8)
    +
    sqrt_minus_value(6, 8)
    Warning in sqrt(x - d): NaNs produced
    @@ -2708,10 +2741,10 @@

    Catching errors before they happen

  • Check that the square root will be OK first. Here’s how:
    • -
    sqrt_minus_value <- function(x, d = 1) {
-  stopifnot(x - d >= 0)
-  sqrt(x - d)
-}
    +
    sqrt_minus_value <- function(x, d = 1) {
+  stopifnot(x - d >= 0)
+  sqrt(x - d)
+}
    @@ -2720,7 +2753,7 @@

    What happens with stopifnot

  • This should be good, and is:
    • -
    sqrt_minus_value(8, 6)
    +
    sqrt_minus_value(8, 6)
    [1] 1.414214
    @@ -2729,7 +2762,7 @@

    What happens with stopifnot

  • This should fail, and see how it does:
    • -
    sqrt_minus_value(6, 8)
    +
    sqrt_minus_value(6, 8)
    Error in sqrt_minus_value(6, 8): x - d >= 0 is not TRUE
    @@ -2746,11 +2779,39 @@

    Using R’s built-ins

  • For example, if you will be calculating a lot of regression-line slopes, you don’t have to do this from scratch: you can use R’s regression calculations, like this:
    • -
    my_df <- tibble(x = 1:4, y = c(10, 11, 10, 14))
-# my_df
-my_df.1 <- lm(y ~ x, data = my_df)
-# summary(my_df.1)
-tidy(my_df.1)
    +
    my_df <- data.frame(x = 1:4, y = c(10, 11, 10, 14))
+my_df
    +
    + +
    + +
    +
    +
    my_df.1 <- lm(y ~ x, data = my_df)
+summary(my_df.1)
    +
    +
    
+Call:
+lm(formula = y ~ x, data = my_df)
+
+Residuals:
+   1    2    3    4 
+ 0.4  0.3 -1.8  1.1 
+
+Coefficients:
+            Estimate Std. Error t value Pr(>|t|)  
+(Intercept)   8.5000     1.8775   4.527   0.0455 *
+x             1.1000     0.6856   1.605   0.2498  
+---
+Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+
+Residual standard error: 1.533 on 2 degrees of freedom
+Multiple R-squared:  0.5628,    Adjusted R-squared:  0.3442 
+F-statistic: 2.574 on 1 and 2 DF,  p-value: 0.2498
    +
    +
    tidy(my_df.1)
    @@ -2765,7 +2826,7 @@

    Using R’s built-ins

    Pulling out just the slope

    Use pluck:

    -
    tidy(my_df.1) %>% pluck("estimate", 2)
    +
    tidy(my_df.1) %>% pluck("estimate", 2)
    [1] 1.1
    @@ -2779,16 +2840,16 @@

    Making this into a function

  • Output: just the slope, a number. Thus:
    • -
    slope <- function(xx, yy) {
-  y.1 <- lm(yy ~ xx)
-  tidy(y.1) %>% pluck("estimate", 2)
-}
    +
    slope <- function(xx, yy) {
+  y.1 <- lm(yy ~ xx)
+  tidy(y.1) %>% pluck("estimate", 2)
+}
    • Check using our data from before: correct:
    -
    with(my_df, slope(x, y))
    +
    with(my_df, slope(x, y))
    [1] 1.1
    @@ -2800,10 +2861,10 @@

    Passing things on

  • lm has a lot of options, with defaults, that we might want to change. Instead of intercepting all the possibilities and passing them on, we can do this:
    • -
    slope <- function(xx, yy, ...) {
-  y.1 <- lm(yy ~ xx, ...)
-  tidy(y.1) %>% pluck("estimate", 2)
-}
    +
    slope <- function(xx, yy, ...) {
+  y.1 <- lm(yy ~ xx, ...)
+  tidy(y.1) %>% pluck("estimate", 2)
+}
    • The ... in the header line means “accept any other input”, and the ... in the lm line means “pass anything other than x and y straight on to lm”.
      • @@ -2816,7 +2877,7 @@

      Using ...

    • So we should be able to do this:
    -
    with(my_df, slope(x, y, subset = 3:4))
    +
    with(my_df, slope(x, y, subset = 3:4))
    [1] 4
    @@ -2825,7 +2886,7 @@

    Using ...

  • Just uses the last two observations in x and y:
    • -
    my_df %>% slice(3:4)
    +
    my_df %>% slice(3:4)
    @@ -2845,7 +2906,7 @@

    Running a function for each of several inputs

  • Suppose we have a data frame containing several different x’s to use in regressions, along with the y we had before:
    • -
    (d <- tibble(x1 = 1:4, x2 = c(8, 7, 6, 5), x3 = c(2, 4, 6, 9)))
    +
    (d <- tibble(x1 = 1:4, x2 = c(8, 7, 6, 5), x3 = c(2, 4, 6, 9)))
    @@ -2869,12 +2930,12 @@

    The loop way

  • Looping variable i goes from 1 to 3 (3 columns, thus 3 slopes):
    • -
    slopes <- numeric(3)
-for (i in 1:3) {
-  d %>% pull(i) -> xx
-  slopes[i] <- slope(xx, my_df$y)
-}
-slopes
    +
    slopes <- numeric(3)
+for (i in 1:3) {
+  d %>% pull(i) -> xx
+  slopes[i] <- slope(xx, my_df$y)
+}
+slopes
    [1]  1.1000000 -1.1000000  0.5140187
    @@ -2890,7 +2951,7 @@

    The map_dbl way

  • Since slope returns a decimal number (a dbl), appropriate function-running function is map_dbl:
    • -
    map_dbl(d, \(d) slope(d, my_df$y))
    +
    map_dbl(d, \(d) slope(d, my_df$y))
            x1         x2         x3 
  1.1000000 -1.1000000  0.5140187
    @@ -2906,8 +2967,8 @@

    Square roots

  • “Find the square roots of each of the numbers 1 through 10”:
    • -
    x <- 1:10
-map_dbl(x, \(x) sqrt(x))
    +
    x <- 1:10
+map_dbl(x, \(x) sqrt(x))
     [1] 1.000000 1.414214 1.732051 2.000000 2.236068 2.449490 2.645751 2.828427
  [9] 3.000000 3.162278
    @@ -2920,12 +2981,12 @@

    Summarizing all columns of a data frame, two ways

  • use my d from above:
    • -
    map_dbl(d, \(d) mean(d))
    +
    map_dbl(d, \(d) mean(d))
      x1   x2   x3 
 2.50 6.50 5.25
    -
    d %>% summarize(across(everything(), \(x) mean(x)))
    +
    d %>% summarize(across(everything(), \(x) mean(x)))
    @@ -2943,10 +3004,10 @@

    What if summary returns more than one thing?

  • For example, finding quartiles:
    • -
    quartiles <- function(x) {
-  quantile(x, c(0.25, 0.75))
-}
-quartiles(1:5)
    +
    quartiles <- function(x) {
+  quantile(x, c(0.25, 0.75))
+}
+quartiles(1:5)
    25% 75% 
   2   4
    @@ -2962,7 +3023,7 @@

    map results

  • Try:
    • -
    map(d, \(d) quartiles(d))
    +
    map(d, \(d) quartiles(d))
    $x1
  25%  75% 
@@ -2987,7 +3048,7 @@ 
    Or
    
 
  • Better: pretend output from quartiles is one-column data frame:
    • 
 
 
    
-
    map_df(d, \(d) quartiles(d))
    
+
    map_df(d, \(d) quartiles(d))
    
 
    
 
 
    
@@ -3001,7 +3062,7 @@ 
    Or
    
 
    
 
    Or even
    
 
    
-
    d %>% map_df(\(d) quartiles(d))
    
+
    d %>% map_df(\(d) quartiles(d))
    
 
    
 
 
    
@@ -3025,8 +3086,8 @@ 
    Map in data frames with mutate
    
 
  • map can also be used within data frames to calculate new columns. Let’s do the square roots of 1 through 10 again:
    • 
 
 
    
-
    d <- tibble(x = 1:10)
-d %>% mutate(root = map_dbl(x, \(x) sqrt(x)))
    
+
    d <- tibble(x = 1:10)
+d %>% mutate(root = map_dbl(x, \(x) sqrt(x)))
    
 
    
 
 
    
@@ -3052,11 +3113,11 @@ 
    Odd or even?
    
 
  • Odd or even? Work out the remainder when dividing by 2:
    • 
 
 
    
-
    6 %% 2
    
+
    6 %% 2
    
 
    
 
    [1] 0
    
 
    
-
    5 %% 2
    
+
    5 %% 2
    
 
    
 
    [1] 1
    
 
    
@@ -3071,31 +3132,31 @@ 
    Write the function
    
 
  • First test for integerness, then test for odd or even, and then do the appropriate calculation:
    • 
 
 
    
-
    hotpo <- function(x) {
-  stopifnot(round(x) == x) # passes if input an integer
-  remainder <- x %% 2
-  if (remainder == 1) {
-    ans <- 3 * x + 1
-  }
-  else {
-    ans <- x %/% 2 # integer division
-  }
-  ans
-}
    
+
    hotpo <- function(x) {
+  stopifnot(round(x) == x) # passes if input an integer
+  remainder <- x %% 2
+  if (remainder == 1) {
+    ans <- 3 * x + 1
+  }
+  else {
+    ans <- x %/% 2 # integer division
+  }
+  ans
+}
    
 
    
 
    
 
    
 
    Test it
    
 
    
-
    hotpo(3)
    
+
    hotpo(3)
    
 
    
 
    [1] 10
    
 
    
-
    hotpo(12)
    
+
    hotpo(12)
    
 
    
 
    [1] 6
    
 
    
-
    hotpo(4.5)
    
+
    hotpo(4.5)
    
 
    
 
    Error in hotpo(4.5): round(x) == x is not TRUE
    
 
    
@@ -3107,7 +3168,7 @@ 
    One through ten
    
 
  • Use a data frame of numbers 1 through 10 again:
    • 
 
 
    
-
    tibble(x = 1:10) %>% mutate(y = map_int(x, \(x) hotpo(x)))
    
+
    tibble(x = 1:10) %>% mutate(y = map_int(x, \(x) hotpo(x)))
    
 
    
 
 
    
@@ -3125,14 +3186,14 @@ 
    Until I get to 1 (if I ever do)
    
 
  • If I get to 4, 2, 1, 4, 2, 1 I’ll repeat for ever, so let’s stop when we get to 1:
    • 
 
 
    
-
    hotpo_seq <- function(x) {
-  ans <- x
-  while (x != 1) {
-    x <- hotpo(x)
-    ans <- c(ans, x)
-  }
-  ans
-}
    
+
    hotpo_seq <- function(x) {
+  ans <- x
+  while (x != 1) {
+    x <- hotpo(x)
+    ans <- c(ans, x)
+  }
+  ans
+}
    
 
    
 
      
 
    • Strategy: keep looping “while x is not 1”.
      • 
@@ -3145,7 +3206,7 @@ 
      Trying it 1/2
      
 
    • Start at 6:
      • 
 
    
 
    
-
    hotpo_seq(6)
    
+
    hotpo_seq(6)
    
 
    
 
    [1]  6  3 10  5 16  8  4  2  1
    
 
    
@@ -3157,7 +3218,7 @@ 
    Trying it 2/2
    
 
  • Start at 27:
    • 
 
 
    
-
    hotpo_seq(27)
    
+
    hotpo_seq(27)
    
 
    
 
      [1]   27   82   41  124   62   31   94   47  142   71  214
  [12]  107  322  161  484  242  121  364  182   91  274  137
@@ -3183,10 +3244,10 @@ 
    Which starting points have the longest sequences?
    
 
    
 
    Top 10 longest sequences
    
 
    
-
    tibble(start = 1:100) %>%
-  mutate(seq_length = map_int(
-    start, \(start) length(hotpo_seq(start)))) %>%
-  slice_max(seq_length, n = 10)
    
+
    tibble(start = 1:100) %>%
+  mutate(seq_length = map_int(
+    start, \(start) length(hotpo_seq(start)))) %>%
+  slice_max(seq_length, n = 10)
    
 
    
 
 
    
@@ -3203,8 +3264,8 @@ 
    Top 10 longest sequences
    
 
    
 
    What happens if we save the entire sequence?
    
 
    
-
    tibble(start = 1:7) %>%
-  mutate(sequence = map(start, \(start) hotpo_seq(start)))
    
+
    tibble(start = 1:7) %>%
+  mutate(sequence = map(start, \(start) hotpo_seq(start)))
    
 
    
 
 
    
@@ -3221,12 +3282,12 @@ 
    What happens if we save the entire sequence?
    
 
    
 
    Using the whole sequence to find its length and its max
    
 
    
-
    tibble(start = 1:7) %>%
-  mutate(sequence = map(start, \(start) hotpo_seq(start))) %>%
-  mutate(
-    seq_length = map_int(sequence, \(sequence) length(sequence)),
-    seq_max = map_int(sequence, \(sequence) max(sequence))
-  )
    
+
    tibble(start = 1:7) %>%
+  mutate(sequence = map(start, \(start) hotpo_seq(start))) %>%
+  mutate(
+    seq_length = map_int(sequence, \(sequence) length(sequence)),
+    seq_max = map_int(sequence, \(sequence) max(sequence))
+  )
    
 
    
 
 
    
@@ -3240,11 +3301,11 @@ 
    Using the whole sequence to find its length and its max
    
 
    
 
    Does it work with rowwise?
    
 
    
-
    tibble(start=1:7) %>% 
-  rowwise() %>% 
-  mutate(sequence = list(hotpo_seq(start))) %>% 
-  mutate(seq_length = length(sequence)) %>% 
-  mutate(seq_max = max(sequence))
    
+
    tibble(start=1:7) %>% 
+  rowwise() %>% 
+  mutate(sequence = list(hotpo_seq(start))) %>% 
+  mutate(seq_length = length(sequence)) %>% 
+  mutate(seq_max = max(sequence))
    
 
    
 
 
    
diff --git a/functions.pdf b/functions.pdf
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diff --git a/inference_1.pdf b/inference_1.pdf
index 5798fee88e2f54bacd1e6a50e7fbdff7111128b2..6058995c9d36d710c783056c2ee8c76ef56d2746 100644
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diff --git a/inference_2.pdf b/inference_2.pdf
index 644ab8a7414e1a5dd27d83edebda13da4dcdafbc..925ef7f04c3dcc10ce4509cb941fdc58b81d01a6 100644
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diff --git a/inference_3.html b/inference_3.html
index 54c0737..5f7ecc5 100644
--- a/inference_3.html
+++ b/inference_3.html
@@ -2592,9 +2592,9 @@ 
    Test and CI
    
 
    Not just coincidence. Let \(C = 100(1 - \alpha)\), so C% gives corresponding CI to level-\(\alpha\) test. Then following always true. (Symbol \(\iff\) means “if and only if”.)
    
 ---+++
diff --git a/inference_3.pdf b/inference_3.pdf
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diff --git a/inference_4a.pdf b/inference_4a.pdf
index 6a862747508a6e3264d55cc937c59b665e5be7ac..8d323dd7d9f84d8c45beab5747a9be2c70a77892 100644
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diff --git a/inference_4b.pdf b/inference_4b.pdf
index 8b30de3cbcd6eeb098129be0d870209811a64d0d..ffabe89354a7c62885d11078592b47bba9001f4d 100644
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diff --git a/inference_5a.html b/inference_5a.html
index bc113b8..a2d862b 100644
--- a/inference_5a.html
+++ b/inference_5a.html
@@ -2512,7 +2512,7 @@ 
    The chi-squared test for independence
    
 
 
    
   
 
    
 
@@ -2606,7 +2606,7 @@ 
    The test
    
 
  • Do chi-squared test:
    • 
 
 
    
-
    chisq.test(tab,correct=F)
    
+
    chisq.test(tab, correct=F)
    
 
    
 
    
     Pearson's Chi-squared test
diff --git a/inference_5a.pdf b/inference_5a.pdf
index c8836c27fbfe94ef9ed2b3aba5f110d30d65a8e9..cac4231477dd2f03b6bc0012210ad87503de70fd 100644
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diff --git a/inference_5b.html b/inference_5b.html
index 521999f..e9d2e88 100644
--- a/inference_5b.html
+++ b/inference_5b.html
@@ -2528,12 +2528,21 @@ 
    Reading the data
    
 
    
 
    The data (some random rows)
    
 
    
-
    rats %>% slice_sample(n=12)
    
+
    rats %>% slice_sample(n=10)
    
 
    
 
 
    
   
+
    
+
    
+
    rats
    
+
    
+
+
    
+  
 
    
 
    
@@ -2542,15 +2551,15 @@ 
    The data (some random rows)
    
 
    
 
    Boxplots
    
 
    
-
    ggplot(rats, aes(y=density, x=group)) + geom_boxplot()
    
+
    ggplot(rats, aes(y=density, x=group)) + geom_boxplot()
    
 
 
    
 
    
 
    
 
    Or, arranging groups in data (logical) order
    
 
    
-
    ggplot(rats, aes(y=density, x=fct_inorder(group))) +
-  geom_boxplot()
    
+
    ggplot(rats, aes(y=density, x=fct_inorder(group))) +
+  geom_boxplot()
    
 
 
    
 
    
@@ -2566,8 +2575,8 @@ 
    Analysis of Variance
    
 
    
 
    Testing: ANOVA in R
    
 
    
-
    rats.aov <- aov(density~group,data=rats)
-summary(rats.aov)
    
+
    rats.aov <- aov(density~group,data=rats)
+summary(rats.aov)
    
 
    
 
                Df Sum Sq Mean Sq F value Pr(>F)   
 group        2   7434    3717   7.978 0.0019 **
@@ -2590,15 +2599,15 @@ 
    Which groups are different from which?
    
 
  • First pick out each group:
    • 
 
 
    
-
    rats %>% filter(group=="Control") -> controls
-rats %>% filter(group=="Lowjump") -> lows
-rats %>% filter(group=="Highjump") -> highs
    
+
    rats %>% filter(group=="Control") -> controls
+rats %>% filter(group=="Lowjump") -> lows
+rats %>% filter(group=="Highjump") -> highs
    
 
    
 
    
 
    
 
    Control vs. low
    
 
    
-
    t.test(controls$density, lows$density)
    
+
    t.test(controls$density, lows$density)
    
 
    
 
    
     Welch Two Sample t-test
@@ -2618,7 +2627,7 @@ 
    Control vs. low
    
 
    
 
    Control vs. high
    
 
    
-
    t.test(controls$density, highs$density)
    
+
    t.test(controls$density, highs$density)
    
 
    
 
    
     Welch Two Sample t-test
@@ -2638,7 +2647,7 @@ 
    Control vs. high
    
 
    
 
    Low vs. high
    
 
    
-
    t.test(lows$density, highs$density)
    
+
    t.test(lows$density, highs$density)
    
 
    
 
    
     Welch Two Sample t-test
@@ -2692,8 +2701,8 @@ 
    Honestly Significant Differences
    
 
    
 
    Tukey on rat data
    
 
    
-
    rats.aov <- aov(density~group, data = rats)
-TukeyHSD(rats.aov)
    
+
    rats.aov <- aov(density~group, data = rats)
+TukeyHSD(rats.aov)
    
 
    
 
      Tukey multiple comparisons of means
     95% family-wise confidence level
@@ -2727,8 +2736,8 @@ 
    Why Tukey’s procedure better than all t-tests
    
 
    
 
    Checking assumptions
    
 
    
-
    ggplot(rats,aes(y = density, x = fct_inorder(group)))+
-  geom_boxplot()
    
+
    ggplot(rats,aes(y = density, x = fct_inorder(group)))+
+  geom_boxplot()
    
 
 
    
 
    Assumptions:
    
@@ -2740,8 +2749,8 @@ 
    Checking assumptions
    
 
    
 
    Normal quantile plots by group
    
 
    
-
    ggplot(rats, aes(sample = density)) + stat_qq() + 
-  stat_qq_line() + facet_wrap( ~ group)
    
+
    ggplot(rats, aes(sample = density)) + stat_qq() + 
+  stat_qq_line() + facet_wrap( ~ group)
    
 
 
    
 
    
@@ -2772,7 +2781,7 @@ 
    Mood’s median test here
    
 
  • Actually do this using median_test:
    • 
 
 
    
-
    median_test(rats, density, group)
    
+
    median_test(rats, density, group)
    
 
    
 
    $grand_median
 [1] 621.5
@@ -2800,7 +2809,7 @@ 
    Comments
    
 
  • To determine which groups differ from which, can compare all possible pairs of groups via (2-sample) Mood’s median tests, then adjust P-values by multiplying by number of 2-sample Mood tests done (Bonferroni):
    • 
 
 
    
-
    pairwise_median_test(rats, density, group)
    
+
    pairwise_median_test(rats, density, group)
    
 
    
 
 
    
@@ -2822,7 +2831,7 @@ 
    Welch ANOVA
    
 
  • Welch ANOVA done by oneway.test as shown (for illustration):
    • 
 
 
    
-
    oneway.test(density~group, data=rats)
    
+
    oneway.test(density~group, data=rats)
    
 
    
 
    
     One-way analysis of means (not assuming equal variances)
@@ -2842,7 +2851,7 @@ 
    Games-Howell
    
 
  • Lives in package PMCMRplus. Install first.
    • 
 
 
    
-
    gamesHowellTest(density~factor(group),data=rats)
    
+
    gamesHowellTest(density~factor(group),data=rats)
    
 
    
 
             Control Highjump
 Highjump 0.0056  -       
diff --git a/inference_5b.pdf b/inference_5b.pdf
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diff --git a/manova.pdf b/manova.pdf
index 88d0068708e875721c56dc7d4a3c830379b64ab4..1cc932bd51cf4a2818a845870305b446a8a1540b 100644
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diff --git a/multiway.pdf b/multiway.pdf
index f0e50d67e204615bc0f5102cc64e10419e20cd24..fb0d82440c94eb0dcb122f2ada1372c351f7ad13 100644
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diff --git a/outline_c32.pdf b/outline_c32.pdf
index 39b719dcdf0bebca91fde1db13d5b682d6edade2..fe64ce2261472754a7ccc542f0f5461600f1e292 100644
GIT binary patch
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diff --git a/outline_c33.pdf b/outline_c33.pdf
index 7dff42bbf76eba870b3b23b5c631935ab180bfba..78f435d9da79bbf67ba17a50edddafe798251c03 100644
GIT binary patch
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diff --git a/outline_d29.pdf b/outline_d29.pdf
index 4b6d98c9f20e525ebb29c919a57c522ce094205f..3ebceedf8d7f85c89bd9bd8b938388a75f2c9566 100644
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diff --git a/princomp.pdf b/princomp.pdf
index 7389dffd6027983e30d418511b7dfb63b061462d..e8f784f72137bfdd061c9ea04270973b66f527f0 100644
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zy}EDh93OReading from the Web; the soap data
 
  • Save the URL in a variable first:
    • 
 
 
    
-
    url <- url("http://ritsokiguess.site/datafiles/soap.txt")
+
    url <- "http://ritsokiguess.site/datafiles/soap.txt"
 soap <- read_delim(url, " ")
    
 
    
 
    
@@ -2825,9 +2825,10 @@ 
    Reading it in
    
 
  • Excel spreadsheets must be “local”: cannot read one in from a URL.
    • 
 
 
    
-
    library(readxl)
-mydata2 <- read_excel("test2.xlsx", sheet = "data")
-mydata2
    
+
    # install.packages("readxl")
+library(readxl)
+mydata2 <- read_excel("test2.xlsx", sheet = "data")
+mydata2
    
 
    
 
 
    
diff --git a/readfile.pdf b/readfile.pdf
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z1l&c?Prq&ex2ak^o5umPiCheck data
  Max.   :586.0   Max.   :14.000  
    
 
    
 
    
+
    
+
    sleep
    
+
    
+
+
    
+  
+
    
+
    
+
    
 
    Make scatter plot of ATST (response) vs. age (explanatory) using code overleaf:
    
 
    
 
    
 
    The scatterplot
    
 
    
-
    ggplot(sleep, aes(x = age, y = atst)) + geom_point()
    
+
    ggplot(sleep, aes(x = age, y = atst)) + geom_point()
    
 
 
    
-
    
+
    
 
    
 
    Correlation
    
 
      
 
    • Measures how well a straight line fits the data:
      • 
 
    
 
    
-
    with(sleep, cor(atst, age))
    
+
    with(sleep, cor(atst, age))
    
 
    
 
    [1] -0.9515469
    
 
    
@@ -2558,7 +2569,7 @@ 
    Correlation
    
 
  • Can do correlations of all pairs of variables:
    • 
 
 
    
-
    cor(sleep)
    
+
    cor(sleep)
    
 
    
 
               atst        age
 atst  1.0000000 -0.9515469
@@ -2578,22 +2589,22 @@ 
    Lowess curve
    
 
    
 
    Plot with lowess curve
    
 
    
-
    ggplot(sleep, aes(x = age, y = atst)) + geom_point() +
-  geom_smooth()
    
+
    ggplot(sleep, aes(x = age, y = atst)) + geom_point() +
+  geom_smooth()
    
 
 
    
-
    
+
    
 
    
 
    The regression
    
 
    Scatterplot shows no obvious curve, and a pretty clear downward trend. So we can run the regression:
    
 
    
-
    sleep.1 <- lm(atst ~ age, data = sleep)
    
+
    sleep.1 <- lm(atst ~ age, data = sleep)
    
 
    
 
    
 
    
 
    The output
    
 
    
-
    summary(sleep.1)
    
+
    summary(sleep.1)
    
 
    
 
    
 Call:
@@ -2634,7 +2645,7 @@ 
    Doing things with the regression output
    
 
  • Package broom extracts info from model output in way that can be used in pipe (later):
    • 
 
 
    
-
    tidy(sleep.1)
    
+
    tidy(sleep.1)
    
 
    
 
 
    
@@ -2648,7 +2659,7 @@ 
    Doing things with the regression output
    
 
    
 
    also one-line summary of model:
    
 
    
-
    glance(sleep.1)
    
+
    glance(sleep.1)
    
 
    
 
 
    
@@ -2662,7 +2673,7 @@ 
    also one-line summary of model:
    
 
    
 
    Broom part 2
    
 
    
-
    sleep.1 %>% augment(sleep)
    
+
    sleep.1 %>% augment(sleep)
    
 
    
 
 
    
@@ -2689,8 +2700,8 @@ 
    CI for mean response and prediction intervals
    
 
    The marginaleffects package 1/2
    
 
    To get predictions for specific values, set up a dataframe with those values first:
    
 
    
-
    new <- datagrid(model = sleep.1, age = c(10, 5))
-new
    
+
    new <- datagrid(model = sleep.1, age = c(10, 5))
+new
    
 
    
 
 
    
@@ -2706,13 +2717,13 @@ 
    The marginaleffects package 1/2
    
 
    The marginaleffects package 2/2
    
 
    Then feed into newdata in predictions. This contains a lot of columns, so you probably want only to display the ones you care about:
    
 
    
-
    cbind(predictions(sleep.1, newdata = new)) %>% 
-  select(estimate, conf.low, conf.high, age)
    
+
    cbind(predictions(sleep.1, newdata = new)) %>% 
+  select(estimate, conf.low, conf.high, age)
    
 
    
 
 
    
   
 
    
 
    
@@ -2723,8 +2734,14 @@ 
    The marginaleffects package 2/2
    
 
    Prediction intervals
    
 
    These are obtained (instead) with predict as below. Use the same dataframe new as before:
    
 
    
-
    pp <- predict(sleep.1, new, interval = "p")
-cbind(new, pp) %>% select(-atst)
    
+
    pp <- predict(sleep.1, new, interval = "p")
+pp
    
+
    
+
           fit      lwr      upr
+1 506.0729 475.8982 536.2475
+2 576.2781 543.8474 608.7088
    
+
    
+
    cbind(new, pp) %>% select(-atst)
    
 
    
 
 
    
@@ -2747,12 +2764,12 @@ 
    Comments
    
 
    That grey envelope
    
 
    Marks confidence interval for mean for all \(x\):
    
 
    
-
    ggplot(sleep, aes(x = age, y = atst)) + geom_point() +
-  geom_smooth(method = "lm") +
-  scale_y_continuous(breaks = seq(420, 600, 20))
    
+
    ggplot(sleep, aes(x = age, y = atst)) + geom_point() +
+  geom_smooth(method = "lm") +
+  scale_y_continuous(breaks = seq(420, 600, 20))
    
 
 
    
-
    
+
    
 
    
 
    Diagnostics
    
 
    How to tell whether a straight-line regression is appropriate?
    
@@ -2765,7 +2782,7 @@ 
    Diagnostics
    
 
    Residual plot
    
 
    Not much pattern here — regression appropriate.
    
 
    
-
    ggplot(sleep.1, aes(x = .fitted, y = .resid)) + geom_point()
    
+
    ggplot(sleep.1, aes(x = .fitted, y = .resid)) + geom_point()
    
 
 
    
 
    
@@ -2773,22 +2790,22 @@ 
    Residual plot
    
 
    An inappropriate regression
    
 
    Different data:
    
 
    
-
    my_url <- "http://ritsokiguess.site/datafiles/curvy.txt"
-curvy <- read_delim(my_url, " ")
    
+
    my_url <- "http://ritsokiguess.site/datafiles/curvy.txt"
+curvy <- read_delim(my_url, " ")
    
 
    
 
    
 
    
 
    Scatterplot
    
 
    
-
    ggplot(curvy, aes(x = xx, y = yy)) + geom_point()
    
+
    ggplot(curvy, aes(x = xx, y = yy)) + geom_point()
    
 
 
    
 
    
 
    
 
    Regression line, anyway
    
 
    
-
    curvy.1 <- lm(yy ~ xx, data = curvy)
-summary(curvy.1)
    
+
    curvy.1 <- lm(yy ~ xx, data = curvy)
+summary(curvy.1)
    
 
    
 
    
 Call:
@@ -2815,7 +2832,7 @@ 
    Regression line, anyway
    
 
    
 
    Residual plot
    
 
    
-
    ggplot(curvy.1, aes(x = .fitted, y = .resid)) + geom_point()
    
+
    ggplot(curvy.1, aes(x = .fitted, y = .resid)) + geom_point()
    
 
 
    
 
    
@@ -2826,20 +2843,20 @@ 
    No good: fixing it up
    
 
  • Fitting a curve would be better. Try this:
    • 
 
 
    
-
    curvy.2 <- lm(yy ~ xx + I(xx^2), data = curvy)
    
+
    curvy.2 <- lm(yy ~ xx + I(xx^2), data = curvy)
    
 
    
 
      
 
    • Adding xx-squared term, to allow for curve.
      • 
 
    • Another way to do same thing: specify how model changes:
      • 
 
    
 
    
-
    curvy.2a <- update(curvy.1, . ~ . + I(xx^2))
    
+
    curvy.2a <- update(curvy.1, . ~ . + I(xx^2))
    
 
    
 
    
 
    
 
    Regression 2
    
 
    
-
    tidy(curvy.2)
    
+
    tidy(curvy.2)
    
 
    
 
 
    
@@ -2848,7 +2865,7 @@ 
    Regression 2
    
   
 
    
 
    
-
    glance(curvy.2) #
    
+
    glance(curvy.2) #
    
 
    
 
 
    
@@ -2871,7 +2888,7 @@ 
    Comments
    
 
    The residual plot now
    
 
    No problems any more:
    
 
    
-
    ggplot(curvy.2, aes(x = .fitted, y = .resid)) + geom_point()
    
+
    ggplot(curvy.2, aes(x = .fitted, y = .resid)) + geom_point()
    
 
 
    
 
    
@@ -2897,9 +2914,9 @@ 
    Box-Cox
    
 
    
 
    Some made-up data
    
 
    
-
    my_url <- "http://ritsokiguess.site/datafiles/madeup2.csv"
-madeup <- read_csv(my_url)
-madeup
    
+
    my_url <- "http://ritsokiguess.site/datafiles/madeup2.csv"
+madeup <- read_csv(my_url)
+madeup
    
 
    
 
 
    
@@ -2914,8 +2931,8 @@ 
    Some made-up data
    
 
    
 
    Scatterplot: faster than linear growth
    
 
    
-
    ggplot(madeup, aes(x = x, y = y)) + geom_point() +
-  geom_smooth()
    
+
    ggplot(madeup, aes(x = x, y = y)) + geom_point() +
+  geom_smooth()
    
 
 
    
 
    
@@ -2927,7 +2944,7 @@ 
    Running Box-Cox
    
 
  • Output: a graph (next page):
    • 
 
 
    
-
    boxcox(y ~ x, data = madeup)
    
+
    boxcox(y ~ x, data = madeup)
    
 
    
 
    
 
    
@@ -2950,8 +2967,8 @@ 
    Did transformation straighten things?
    
 
  • Plot transformed \(y\) against \(x\). Here, log:
    • 
 
 
    
-
    ggplot(madeup, aes(x = x, y = log(y))) + geom_point() +
-  geom_smooth()
    
+
    ggplot(madeup, aes(x = x, y = log(y))) + geom_point() +
+  geom_smooth()
    
 
 
    
 
    Looks much straighter.
    
@@ -2959,8 +2976,8 @@ 
    Did transformation straighten things?
    
 
    
 
    Regression with transformed \(y\)
    
 
    
-
    madeup.1 <- lm(log(y) ~ x, data = madeup)
-glance(madeup.1)
    
+
    madeup.1 <- lm(log(y) ~ x, data = madeup)
+glance(madeup.1)
    
 
    
 
 
    
@@ -2969,7 +2986,7 @@ 
    Regression with transformed \(y\)
    
   
 
    
 
    
-
    tidy(madeup.1)
    
+
    tidy(madeup.1)
    
 
    
 
 
    
@@ -3003,15 +3020,15 @@ 
    Multiple regression example
    
 
    
 
    The data
    
 
    
-
    my_url <- 
-  "http://ritsokiguess.site/datafiles/regressx.txt"
-visits <- read_delim(my_url, " ")
    
+
    my_url <- 
+  "http://ritsokiguess.site/datafiles/regressx.txt"
+visits <- read_delim(my_url, " ")
    
 
    
 
    
 
    
 
    Check data
    
 
    
-
    visits
    
+
    visits
    
 
    
 
 
    
@@ -3025,9 +3042,9 @@ 
    Check data
    
 
    
 
    Fit multiple regression
    
 
    
-
    visits.1 <- lm(timedrs ~ phyheal + menheal + stress,
-  data = visits)
-summary(visits.1)
    
+
    visits.1 <- lm(timedrs ~ phyheal + menheal + stress,
+  data = visits)
+summary(visits.1)
    
 
    
 
    
 Call:
@@ -3063,8 +3080,8 @@ 
    The slopes
    
 
    
 
    Just menheal
    
 
    
-
    visits.2 <- lm(timedrs ~ menheal, data = visits)
-summary(visits.2)
    
+
    visits.2 <- lm(timedrs ~ menheal, data = visits)
+summary(visits.2)
    
 
    
 
    
 Call:
@@ -3101,7 +3118,7 @@ 
    menheal by itself
    
 
    Investigating via correlation
    
 
    Leave out first column (subjno):
    
 
    
-
    visits %>% select(-subjno) %>% cor()
    
+
    visits %>% select(-subjno) %>% cor()
    
 
    
 
              timedrs   phyheal   menheal    stress
 timedrs 1.0000000 0.4395293 0.2555703 0.2865951
@@ -3120,7 +3137,7 @@ 
    Investigating via correlation
    
 
    
 
    Residual plot (from timedrs on all)
    
 
    
-
    ggplot(visits.1, aes(x = .fitted, y = .resid)) + geom_point()
    
+
    ggplot(visits.1, aes(x = .fitted, y = .resid)) + geom_point()
    
 
 
    
 
    Apparently random. But…
    
@@ -3128,7 +3145,7 @@ 
    Residual plot (from timedrs on all)
    
 
    
 
    Normal quantile plot of residuals
    
 
    
-
    ggplot(visits.1, aes(sample = .resid)) + stat_qq() + stat_qq_line()
    
+
    ggplot(visits.1, aes(sample = .resid)) + stat_qq() + stat_qq_line()
    
 
 
    
 
    Not normal at all; upper tail is way too long.
    
@@ -3137,8 +3154,8 @@ 
    Normal quantile plot of residuals
    
 
    Absolute residuals
    
 
    Is there trend in size of residuals (fan-out)? Plot absolute value of residual against fitted value:
    
 
    
-
    ggplot(visits.1, aes(x = .fitted, y = abs(.resid))) +
-  geom_point() + geom_smooth()
    
+
    ggplot(visits.1, aes(x = .fitted, y = abs(.resid))) +
+  geom_point() + geom_smooth()
    
 
 
    
 
    
@@ -3167,7 +3184,7 @@ 
    Box-Cox transformations
    
 
  • Extra problem: some of timedrs values are 0, but Box-Cox expects all +. Note response for boxcox:
    • 
 
 
    
-
    boxcox(timedrs + 1 ~ phyheal + menheal + stress, data = visits)
    
+
    boxcox(timedrs + 1 ~ phyheal + menheal + stress, data = visits)
    
 
    
 
    
 
    
@@ -3182,8 +3199,8 @@ 
    Comments on try 1
    
 
  • Focus on \(\lambda\) in \((-0.3,0.1)\):
    • 
 
 
    
-
    my.lambda <- seq(-0.3, 0.1, 0.01)
-my.lambda
    
+
    my.lambda <- seq(-0.3, 0.1, 0.01)
+my.lambda
    
 
    
 
     [1] -0.30 -0.29 -0.28 -0.27 -0.26 -0.25 -0.24 -0.23 -0.22
 [10] -0.21 -0.20 -0.19 -0.18 -0.17 -0.16 -0.15 -0.14 -0.13
@@ -3196,10 +3213,10 @@ 
    Comments on try 1
    
 
    
 
    Try 2
    
 
    
-
    boxcox(timedrs + 1 ~ phyheal + menheal + stress,
-  lambda = my.lambda,
-  data = visits
-)
    
+
    boxcox(timedrs + 1 ~ phyheal + menheal + stress,
+  lambda = my.lambda,
+  data = visits
+)
    
 
 
    
 
    
@@ -3218,9 +3235,9 @@ 
    Fixing the problems
    
 
  • Then check residual plot to see that it is OK now.
    • 
 
 
    
-
    visits.3 <- lm(log(timedrs + 1) ~ phyheal + menheal + stress,
-  data = visits
-)
    
+
    visits.3 <- lm(log(timedrs + 1) ~ phyheal + menheal + stress,
+  data = visits
+)
    
 
    
 
      
 
    • timedrs+1 because some timedrs values 0, can’t take log of 0.
      • 
@@ -3230,7 +3247,7 @@ 
      Fixing the problems
      
 
      
 
      Output
      
 
      
-
      summary(visits.3)
      
+
      summary(visits.3)
      
 
      
 
      
 Call:
@@ -3268,23 +3285,23 @@ 
      Comments
      
 
      
 
      Residuals against fitted values
      
 
      
-
      ggplot(visits.3, aes(x = .fitted, y = .resid)) +
-  geom_point()
      
+
      ggplot(visits.3, aes(x = .fitted, y = .resid)) +
+  geom_point()
      
 
 
      
 
      
 
      
 
      Normal quantile plot of residuals
      
 
      
-
      ggplot(visits.3, aes(sample = .resid)) + stat_qq() + stat_qq_line()
      
+
      ggplot(visits.3, aes(sample = .resid)) + stat_qq() + stat_qq_line()
      
 
 
      
 
      
 
      
 
      Absolute residuals against fitted
      
 
      
-
      ggplot(visits.3, aes(x = .fitted, y = abs(.resid))) +
-  geom_point() + geom_smooth()
      
+
      ggplot(visits.3, aes(x = .fitted, y = abs(.resid))) +
+  geom_point() + geom_smooth()
      
 
 
      
 
      
@@ -3307,15 +3324,15 @@ 
      Testing more than one \(x\) at once
      
 
    
 
 
    
-
    visits.5 <- lm(log(timedrs + 1) ~ phyheal + menheal + stress, 
-               data = visits)
-visits.6 <- lm(log(timedrs + 1) ~ stress, data = visits)
    
+
    visits.5 <- lm(log(timedrs + 1) ~ phyheal + menheal + stress, 
+               data = visits)
+visits.6 <- lm(log(timedrs + 1) ~ stress, data = visits)
    
 
    
 
    
 
    
 
    Results of tests
    
 
    
-
    anova(visits.6, visits.5)
    
+
    anova(visits.6, visits.5)
    
 
    
 
 
    
@@ -3347,8 +3364,7 @@ 
    The punting data
    
 120               130                117.58       126 
 140               120                140.25       129  
 130               140                150.17       136 
-150               160                165.17       154 
-
    
+150               160                165.17       154 
    
 
    
 
    
 
    Reading in
    
@@ -3356,14 +3372,14 @@ 
    Reading in
    
 
  • Separated by multiple spaces with columns lined up:
    • 
 
 
    
-
    my_url <- "http://ritsokiguess.site/datafiles/punting.txt"
-punting <- read_table(my_url)
    
+
    my_url <- "http://ritsokiguess.site/datafiles/punting.txt"
+punting <- read_table(my_url)
    
 
    
 
    
 
    
 
    The data
    
 
    
-
    punting
    
+
    punting
    
 
    
 
 
    
@@ -3377,8 +3393,8 @@ 
    The data
    
 
    
 
    Regression and output
    
 
    
-
    punting.1 <- lm(punt ~ left + right + fred, data = punting)
-glance(punting.1)
    
+
    punting.1 <- lm(punt ~ left + right + fred, data = punting)
+glance(punting.1)
    
 
    
 
 
    
@@ -3387,7 +3403,7 @@ 
    Regression and output
    
   
 
    
 
    
-
    tidy(punting.1)
    
+
    tidy(punting.1)
    
 
    
 
 
    
@@ -3396,6 +3412,27 @@ 
    Regression and output
    
   
 
    
 
    
+
    summary(punting.1)
    
+
    
+
    
+Call:
+lm(formula = punt ~ left + right + fred, data = punting)
+
+Residuals:
+     Min       1Q   Median       3Q      Max 
+-14.9325 -11.5618  -0.0315   9.0415  20.0886 
+
+Coefficients:
+            Estimate Std. Error t value Pr(>|t|)
+(Intercept)  -4.6855    29.1172  -0.161    0.876
+left          0.2679     2.1111   0.127    0.902
+right         1.0524     2.1477   0.490    0.636
+fred         -0.2672     4.2266  -0.063    0.951
+
+Residual standard error: 14.68 on 9 degrees of freedom
+Multiple R-squared:  0.7781,    Adjusted R-squared:  0.7042 
+F-statistic: 10.52 on 3 and 9 DF,  p-value: 0.00267
    
+
    
 
    
 
    
 
    
@@ -3410,7 +3447,7 @@ 
    Comments
    
 
    
 
    The correlations
    
 
    
-
    cor(punting)
    
+
    cor(punting)
    
 
    
 
               left     right      punt      fred
 left  1.0000000 0.8957224 0.8117368 0.9722632
@@ -3427,8 +3464,30 @@ 
    The correlations
    
 
    
 
    Just right
    
 
    
-
    punting.2 <- lm(punt ~ right, data = punting)
-anova(punting.2, punting.1)
    
+
    punting.2 <- lm(punt ~ right, data = punting)
+summary(punting.2)
    
+
    
+
    
+Call:
+lm(formula = punt ~ right, data = punting)
+
+Residuals:
+     Min       1Q   Median       3Q      Max 
+-15.7576 -11.0611   0.3656   7.8890  19.0423 
+
+Coefficients:
+            Estimate Std. Error t value Pr(>|t|)    
+(Intercept)  -3.6930    25.2649  -0.146    0.886    
+right         1.0427     0.1692   6.162 7.09e-05 ***
+---
+Signif. codes:  
+0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+
+Residual standard error: 13.36 on 11 degrees of freedom
+Multiple R-squared:  0.7754,    Adjusted R-squared:  0.7549 
+F-statistic: 37.97 on 1 and 11 DF,  p-value: 7.088e-05
    
+
    
+
    anova(punting.2, punting.1)
    
 
    
 
 
    
@@ -3437,17 +3496,40 @@ 
    Just right
    
   
 
    
 
    
+
    punting.3 <- lm(punt ~ left, data = punting)
+summary(punting.3)
    
+
    
+
    
+Call:
+lm(formula = punt ~ left, data = punting)
+
+Residuals:
+    Min      1Q  Median      3Q     Max 
+-22.840 -12.298  -2.234   8.990  35.820 
+
+Coefficients:
+            Estimate Std. Error t value Pr(>|t|)    
+(Intercept)  12.8834    30.1575   0.427 0.677474    
+left          0.9553     0.2072   4.610 0.000753 ***
+---
+Signif. codes:  
+0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+
+Residual standard error: 16.46 on 11 degrees of freedom
+Multiple R-squared:  0.6589,    Adjusted R-squared:  0.6279 
+F-statistic: 21.25 on 1 and 11 DF,  p-value: 0.0007528
    
+
    
 
    
 
    No significant loss by dropping other two variables.
    
 
    
 
    
 
    Comparing R-squareds
    
 
    
-
    summary(punting.1)$r.squared
    
+
    summary(punting.1)$r.squared
    
 
    
 
    [1] 0.7781401
    
 
    
-
    summary(punting.2)$r.squared
    
+
    summary(punting.2)$r.squared
    
 
    
 
    [1] 0.7753629
    
 
    
@@ -3457,7 +3539,7 @@ 
    Comparing R-squareds
    
 
    
 
    Regression results
    
 
    
-
    tidy(punting.2)
    
+
    tidy(punting.2)
    
 
    
 
 
    
@@ -3481,8 +3563,8 @@ 
    But
    
 
    
 
    Augmenting punting.2
    
 
    
-
    punting.2 %>% augment(punting) -> punting.2.aug
-punting.2.aug 
    
+
    punting.2 %>% augment(punting) -> punting.2.aug
+punting.2.aug 
    
 
    
 
 
    
@@ -3496,8 +3578,8 @@ 
    Augmenting punting.2
    
 
    
 
    Residuals against left
    
 
    
-
    ggplot(punting.2.aug, aes(x = left, y = .resid)) +
-  geom_point()
    
+
    ggplot(punting.2.aug, aes(x = left, y = .resid)) +
+  geom_point()
    
 
 
    
 
    
@@ -3508,15 +3590,15 @@ 
    Comments
    
 
  • We should add left-squared to the regression (and therefore put left back in when we do that):
    • 
 
 
    
-
    punting.3 <- lm(punt ~ left + I(left^2) + right,
-  data = punting
-)
    
+
    punting.3 <- lm(punt ~ left + I(left^2) + right,
+  data = punting
+)
    
 
    
 
    
 
    
 
    Regression with left-squared
    
 
    
-
    summary(punting.3)
    
+
    summary(punting.3)
    
 
    
 
    
 Call:
diff --git a/regression.pdf b/regression.pdf
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diff --git a/running.pdf b/running.pdf
index 46a2fb8dbb8aa7620bd824916bd3956d45c9495e..7e92e34531bfa2d057fc389d5027fab5bafbc5f3 100644
GIT binary patch
delta 7612
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diff --git a/tidy_extra.html b/tidy_extra.html
index 177cd10..8c473d5 100644
--- a/tidy_extra.html
+++ b/tidy_extra.html
@@ -2494,7 +2494,7 @@ 
    Packages
    
 
    
 
    The pig feed data again
    
 
    
-
    my_url <- "http://ritsokiguess.site/STAC32/pigs1.txt"
+
    my_url <- "http://ritsokiguess.site/datafiles/pigs1.txt"
 pigs <- read_table(my_url)
 pigs
    
 
    
@@ -2600,16 +2600,16 @@ 
    Lengthen and separate
    
 
    
 
    Making longer, the better way
    
 
    
-
    prevalence %>% 
+
    prevalence  %>% 
   pivot_longer(-Species, names_to=c("disease", "location"),
-               names_sep="_", values_to="frequency") %>% 
-  arrange(Species, location, disease) -> prevalence_longer
+               names_sep="_", 
+               values_to="frequency") -> prevalence_longer 
 prevalence_longer
    
 
    
 
 
    
   
 
    
 
    
@@ -2624,7 +2624,7 @@ 
    Making wider, different ways
    
 
 
    
   
 
    
 
    
@@ -2636,7 +2636,7 @@ 
    Making wider, different ways
    
 
 
    
   
 
    
 
    
@@ -2745,6 +2745,14 @@ 
    A hairy one
    
 
    
 
    
 
+
    
+  
+
    
+
    
+
    
+
 
    
   
 
    
 
    
diff --git a/tidy_extra.pdf b/tidy_extra.pdf
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diff --git a/tidying.html b/tidying.html
index 5b928e1..dae2600 100644
--- a/tidying.html
+++ b/tidying.html
@@ -2555,6 +2555,35 @@ 
    Making it longer
    
 pigs2
    
 
    
 
+
    
+  
+
    
+
    
+
    
+
    
+
    
+
    Alternatives
    
+
    Any way of choosing the columns to pivot longer is good, eg:
    
+
    
+
    pigs1 %>% pivot_longer(-pig, names_to="feed", 
+                       values_to="weight") 
    
+
    
+
+
    
+  
+
    
+
    
+
    
+
    or
    
+
    
+
    pigs1 %>% pivot_longer(starts_with("feed"), names_to="feed", 
+                       values_to="weight") 
    
+
    
+
 
    
   
 
    
 
    
@@ -2600,8 +2629,8 @@ 
    …and finally, the analysis
    
 
  • which is just what we saw before:
    • 
 
 
    
-
    weight.1 <- aov(weight ~ feed, data = pigs2)
-summary(weight.1)
    
+
    weight.1 <- aov(weight ~ feed, data = pigs2)
+summary(weight.1)
    
 
    
 
                Df Sum Sq Mean Sq F value   Pr(>F)    
 feed         3   3521  1173.5   119.1 3.72e-11 ***
@@ -2618,7 +2647,7 @@ 
    …and finally, the analysis
    
 
    
 
    Tukey
    
 
    
-
    TukeyHSD(weight.1)
    
+
    TukeyHSD(weight.1)
    
 
    
 
      Tukey multiple comparisons of means
     95% family-wise confidence level
@@ -2641,10 +2670,10 @@ 
    Tukey
    
 
    Mean weights by feed
    
 
    To find the best and worst, get mean weight by feed group. I borrowed an idea from earlier to put the means in descending order:
    
 
    
-
    pigs2 %>%
-  group_by(feed) %>%
-  summarize(mean_weight = mean(weight))%>%
-  arrange(desc(mean_weight))
    
+
    pigs2 %>%
+  group_by(feed) %>%
+  summarize(mean_weight = mean(weight))%>%
+  arrange(desc(mean_weight))
    
 
    
 
 
    
@@ -2659,7 +2688,7 @@ 
    Mean weights by feed
    
 
    
 
    Should we have any concerns about the ANOVA?
    
 
    
-
    ggplot(pigs2, aes(x = feed, y = weight)) + geom_boxplot()
    
+
    ggplot(pigs2, aes(x = feed, y = weight)) + geom_boxplot()
    
 
 
    
 
    
@@ -2678,26 +2707,26 @@ 
    Tuberculosis
    
 
  • Some data:
    • 
 
 
    
-
    my_url <- "http://ritsokiguess.site/datafiles/tb.csv"
-tb <- read_csv(my_url)
    
+
    my_url <- "http://ritsokiguess.site/datafiles/tb.csv"
+tb <- read_csv(my_url)
    
 
    
 
    
 
    
 
    The data (randomly chosen rows)
    
 
    
-
    tb %>% slice_sample(n = 10)
    
+
    tb %>% slice_sample(n = 10)
    
 
    
 
 
    
   
 
    
 
    
 
    
 
    Many rows:
    
 
    
-
    nrow(tb)
    
+
    nrow(tb)
    
 
    
 
    [1] 5769
    
 
    
@@ -2711,9 +2740,9 @@ 
    What we have
    
 
  • Abbreviations here.
    • 
 
 
    
-
    tb %>% 
-  pivot_longer(m04:fu, names_to = "genage", 
-               values_to = "freq", values_drop_na = TRUE) -> tb2
    
+
    tb %>% 
+  pivot_longer(m04:fu, names_to = "genage", 
+               values_to = "freq", values_drop_na = TRUE) -> tb2
    
 
    
 
      
 
    • Code for pivot_longer:
@@ -2728,7 +2757,7 @@ 
      What we have
      
 
      
 
      Results (some)
      
 
      
-
      tb2
      
+
      tb2
      
 
      
 
 
      
@@ -2752,10 +2781,10 @@ 
      Separating
      
 
    • For “how to split”, here “after first character”:
      • 
 
    
 
    
-
    tb2 %>% separate_wider_position(genage, 
-                                widths = c("gender" = 1, "age" = 4), 
-                                too_few = "align_start") -> tb3
-tb3
    
+
    tb2 %>% separate_wider_position(genage, 
+                                widths = c("gender" = 1, "age" = 4), 
+                                too_few = "align_start") -> tb3
+tb3
    
 
    
 
 
    
@@ -2769,7 +2798,7 @@ 
    Separating
    
 
    
 
    Tidied tuberculosis data (some)
    
 
    
-
    tb3
    
+
    tb3
    
 
    
 
 
    
@@ -2786,12 +2815,12 @@ 
    In practice…
    
 
  • instead of doing the pipe one step at a time, you debug it one step at a time, and when you have each step working, you use that step’s output as input to the next step, thus:
    • 
 
 
    
-
    tb %>%
-  pivot_longer(m04:fu, names_to = "genage", 
-               values_to = "freq", values_drop_na = TRUE) %>% 
-  separate_wider_position(genage, 
-                          widths = c("gender" = 1, "age" = 4), 
-                          too_few = "align_start")
    
+
    tb %>%
+  pivot_longer(m04:fu, names_to = "genage", 
+               values_to = "freq", values_drop_na = TRUE) %>% 
+  separate_wider_position(genage, 
+                          widths = c("gender" = 1, "age" = 4), 
+                          too_few = "align_start")
    
 
    
 
 
    
@@ -2809,9 +2838,9 @@ 
    In practice…
    
 
    
 
    Total tuberculosis cases by year (some of the years)
    
 
    
-
    tb3 %>%
-  filter(between(year, 1991, 1998)) %>% 
-  group_by(year) %>% summarize(total=sum(freq))
    
+
    tb3 %>%
+  filter(between(year, 1991, 1998)) %>% 
+  group_by(year) %>% summarize(total=sum(freq))
    
 
    
 
 
    
@@ -2831,9 +2860,9 @@ 
    To find out what
    
 
  • try counting up total cases by country:
    • 
 
 
    
-
    tb3 %>% group_by(iso2) %>% 
-  summarize(total=sum(freq)) %>% 
-  arrange(desc(total))
    
+
    tb3 %>% group_by(iso2) %>% 
+  summarize(total=sum(freq)) %>% 
+  arrange(desc(total))
    
 
    
 
 
    
@@ -2848,9 +2877,9 @@ 
    To find out what
    
 
    What years do I have for China?
    
 
    China started recording in 1995, which is at least part of the problem:
    
 
    
-
    tb3 %>% filter(iso2=="CN") %>% 
-  group_by(year) %>% 
-  summarize(total=sum(freq))
    
+
    tb3 %>% filter(iso2=="CN") %>% 
+  group_by(year) %>% 
+  summarize(total=sum(freq))
    
 
    
 
 
    
@@ -2867,9 +2896,9 @@ 
    First year of recording by country?
    
 
  • A lot of countries started recording in about 1995, in fact:
    • 
 
 
    
-
    tb3 %>% group_by(iso2) %>% 
-  summarize(first_year=min(year)) %>% 
-  count(first_year)
    
+
    tb3 %>% group_by(iso2) %>% 
+  summarize(first_year=min(year)) %>% 
+  count(first_year)
    
 
    
 
 
    
@@ -2886,10 +2915,10 @@ 
    First year of recording by country?
    
 
    
 
    Some Toronto weather data
    
 
    
-
    my_url <- 
-  "http://ritsokiguess.site/STAC32/toronto_weather.csv"
-weather <- read_csv(my_url)
-weather
    
+
    my_url <- 
+  "http://ritsokiguess.site/STAC32/toronto_weather.csv"
+weather <- read_csv(my_url)
+weather
    
 
    
 
 
    
@@ -2916,10 +2945,10 @@ 
    The columns
    
 
    Off we go
    
 
    Numbers in data frame all temperatures (for different days of the month), so first step is
    
 
    
-
    weather %>% 
-  pivot_longer(d01:d31, names_to="day", 
-               values_to="temperature", 
-               values_drop_na = TRUE)
    
+
    weather %>% 
+  pivot_longer(d01:d31, names_to="day", 
+               values_to="temperature", 
+               values_drop_na = TRUE)
    
 
    
 
 
    
@@ -2941,12 +2970,12 @@ 
    Element
    
 
    
 
    Handling element
    
 
    
-
    weather %>%
-  pivot_longer(d01:d31, names_to="day", 
-               values_to="temperature", 
-               values_drop_na = TRUE) %>% 
-  pivot_wider(names_from=element, 
-                values_from=temperature) 
    
+
    weather %>%
+  pivot_longer(d01:d31, names_to="day", 
+               values_to="temperature", 
+               values_drop_na = TRUE) %>% 
+  pivot_wider(names_from=element, 
+                values_from=temperature) 
    
 
    
 
 
    
@@ -2969,12 +2998,12 @@ 
    Further improvements 1/2
    
 
    
 
    Further improvements 2/2
    
 
    
-
    weather %>%
-  pivot_longer(d01:d31, names_to="day", 
-               values_to="temperature", values_drop_na = T) %>% 
-  pivot_wider(names_from=element, values_from=temperature) %>% 
-  mutate(Day = parse_number(day)) %>%
-  select(-station)
    
+
    weather %>%
+  pivot_longer(d01:d31, names_to="day", 
+               values_to="temperature", values_drop_na = T) %>% 
+  pivot_wider(names_from=element, values_from=temperature) %>% 
+  mutate(Day = parse_number(day)) %>%
+  select(-station)
    
 
    
 
 
    
@@ -2989,24 +3018,24 @@ 
    Further improvements 2/2
    
 
    Final step(s)
    
 
      
 
    • Make year-month-day into proper date.
      • 
-
    • Keep only date, tmax, tmin: 
      • 
+
    • Keep only date, tmax, tmin:
      • 
 
    
 
    
-
    weather %>%
-  pivot_longer(d01:d31, names_to="day", 
-               values_to="temperature", values_drop_na = T) %>% 
-  pivot_wider(names_from=element, values_from=temperature) %>% 
-  mutate(Day = parse_number(day)) %>%
-  select(-station) %>% 
-  unite(datestr, c(Year, Month, Day), sep = "-") %>%
-  mutate(date = as.Date(datestr)) %>%
-  select(c(date, tmax, tmin)) -> weather_tidy
    
+
    weather %>%
+  pivot_longer(d01:d31, names_to="day", 
+               values_to="temperature", values_drop_na = T) %>% 
+  pivot_wider(names_from=element, values_from=temperature) %>% 
+  mutate(Day = parse_number(day)) %>%
+  select(-station) %>% 
+  unite(datestr, c(Year, Month, Day), sep = "-") %>%
+  mutate(date = as.Date(datestr)) %>%
+  select(c(date, tmax, tmin)) -> weather_tidy
    
 
    
 
    
 
    
 
    Our tidy data frame
    
 
    
-
    weather_tidy
    
+
    weather_tidy
    
 
    
 
 
    
@@ -3023,8 +3052,8 @@ 
    Plotting the temperatures
    
 
  • Plot temperature against date joined by lines, but with separate lines for max and min. ggplot requires something like
    • 
 
 
    
-
    ggplot(..., aes(x = date, y = temperature)) + geom_point() + 
-  geom_line()
    
+
    ggplot(..., aes(x = date, y = temperature)) + geom_point() + 
+  geom_line()
    
 
    
 
    only we have two temperatures, one a max and one a min, that we want to keep separate.
    
 
      
@@ -3032,8 +3061,8 @@ 
      Plotting the temperatures
      
 
    • Then can do something like
      • 
 
    
 
    
-
    ggplot(d, aes(x = date, y = temperature, colour = maxmin)) 
-  + geom_point() + geom_line()
    
+
    ggplot(d, aes(x = date, y = temperature, colour = maxmin)) 
+  + geom_point() + geom_line()
    
 
    
 
    to distinguish max and min on graph.
    
 
    
@@ -3045,26 +3074,26 @@ 
    Setting up plot
    
 
  • To make those “one column”s: pivot_longer. I save the graph to show overleaf:
    • 
 
 
    
-
    weather_tidy %>%
-  pivot_longer(tmax:tmin, names_to="maxmin", 
-               values_to="temperature") %>%
-  ggplot(aes(x = date, y = temperature, colour = maxmin)) +
-      geom_line() -> g
    
+
    weather_tidy %>%
+  pivot_longer(tmax:tmin, names_to="maxmin", 
+               values_to="temperature") %>%
+  ggplot(aes(x = date, y = temperature, colour = maxmin)) + geom_point() +
+      geom_line() -> g
    
 
    
 
    
 
    
 
    The plot
    
 
    
-
    g
    
+
    g
    
 
 
    
-
    
+
    
 
    
 
    Summary of tidying “verbs”
    
 
    
 






 
 
 
    --++
diff --git a/tidying.pdf b/tidying.pdf
index 296fd72603a28de119a13f05ce83a79f973eedfb..1e73b338a2093e33be0f026d9b1489c3551aec27 100644
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   pivot_longer(tmax:tmin, names_to="maxmin", 
                values_to="temperature") %>%
   ggplot(aes(x = date, y = temperature, colour = maxmin)) + geom_point() +
-      geom_line() 
+      geom_line() -> g
 ```
 
 ## The plot
diff --git a/vector_matrix.pdf b/vector_matrix.pdf
index 2a5c452a368ceeba4010adc6e4f7f823b8473385..cf115cb83c3c760ecd363746be337fafd76d734e 100644
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diff --git a/wider_wrong.pdf b/wider_wrong.pdf
index bb735f4ccba324642509f986df3f7aabe192626d..6dea4a5be8dae363bf51091f45dfc6a885eef918 100644
GIT binary patch
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z!J1dGmsL+wRO}yNw@JXho}
zR1lq)r?FGw`^$oNRPxlTlV+hStrategy
 
 
    
 
    ggplot(windmill, aes(y = DC_output, x = wind_velocity)) +
-  geom_point() + geom_smooth(se = F) 
    
+  geom_point() + geom_smooth() 
    
 
 
 
    
@@ -2652,6 +2652,13 @@ 
    Comments on residual plot
    
 
 
 
    
+
    
+
    normal quantile plot of residuals
    
+
    
+
    ggplot(DC.1, aes(sample = .resid)) + stat_qq() + stat_qq_line()
    
+
+
    
+
    
 
    
 
    Parabolas and fitting parabola model
    
 
      
@@ -2659,9 +2666,9 @@ 
      Parabolas and fitting parabola model
      
 
    • Fit one using lm by adding \(x^2\) to right side of model formula with +:
      • 
 
    
 
    
-
    DC.2 <- lm(DC_output ~ wind_velocity + I(wind_velocity^2),
-  data = windmill
-)
    
+
    DC.2 <- lm(DC_output ~ wind_velocity + I(wind_velocity^2),
+  data = windmill
+)
    
 
    
 
      
 
    • The I() necessary because ^ in model formula otherwise means something different (to do with interactions in ANOVA).
      • 
@@ -2671,18 +2678,58 @@ 
      Parabolas and fitting parabola model
      
 
      
 
      Parabola model output
      
 
      
-
      tidy(DC.2)
      
-
      
+
      summary(DC.2)
      
+
      
+
      
+Call:
+lm(formula = DC_output ~ wind_velocity + I(wind_velocity^2), 
+    data = windmill)
 
-
      
-  
+Residuals:
+     Min       1Q   Median       3Q      Max 
+-0.26347 -0.02537  0.01264  0.03908  0.19903 
+
+Coefficients:
+                    Estimate Std. Error t value Pr(>|t|)    
+(Intercept)        -1.155898   0.174650  -6.618 1.18e-06 ***
+wind_velocity       0.722936   0.061425  11.769 5.77e-11 ***
+I(wind_velocity^2) -0.038121   0.004797  -7.947 6.59e-08 ***
+---
+Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+
+Residual standard error: 0.1227 on 22 degrees of freedom
+Multiple R-squared:  0.9676,    Adjusted R-squared:  0.9646 
+F-statistic: 328.3 on 2 and 22 DF,  p-value: < 2.2e-16
      
 
      
+
      # tidy(DC.2)
      
+
      
+
      
+
      summary(DC.2)
      
+
      
+
      
+Call:
+lm(formula = DC_output ~ wind_velocity + I(wind_velocity^2), 
+    data = windmill)
+
+Residuals:
+     Min       1Q   Median       3Q      Max 
+-0.26347 -0.02537  0.01264  0.03908  0.19903 
+
+Coefficients:
+                    Estimate Std. Error t value Pr(>|t|)    
+(Intercept)        -1.155898   0.174650  -6.618 1.18e-06 ***
+wind_velocity       0.722936   0.061425  11.769 5.77e-11 ***
+I(wind_velocity^2) -0.038121   0.004797  -7.947 6.59e-08 ***
+---
+Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+
+Residual standard error: 0.1227 on 22 degrees of freedom
+Multiple R-squared:  0.9676,    Adjusted R-squared:  0.9646 
+F-statistic: 328.3 on 2 and 22 DF,  p-value: < 2.2e-16
      
 
      
 
      
 
      
-
      glance(DC.2)
      
+
      glance(DC.2)
      
 
      
 
 
      
@@ -2706,11 +2753,19 @@ 
      Comments on output
      
 
      
 
      Residual plot from parabola model
      
 
      
-
      ggplot(DC.2, aes(y = .resid, x = .fitted)) +
-  geom_point()
      
+
      ggplot(DC.2, aes(y = .resid, x =  .fitted)) +
+  geom_point()
      
 
 
      
 
      
+
      
+
      normal quantile plot of residuals
      
+
      
+
      ggplot(DC.2, aes(sample = .resid)) + stat_qq() + stat_qq_line()
      
+
+
      
+
      This distribution has long tails, which should worry us at least some.
      
+
      
 
      
 
      Make scatterplot with fitted line and curve
      
 
        
@@ -2718,9 +2773,9 @@ 
        Make scatterplot with fitted line and curve
        
 
      • Scatterplot with fitted line and curve like this:
        • 
 
      
 
      
-
      ggplot(windmill, aes(y = DC_output, x = wind_velocity)) +
-  geom_point() + geom_smooth(method = "lm", se = F) +
-  geom_line(data = DC.2, aes(y = .fitted))
      
+
      ggplot(windmill, aes(y = DC_output, x = wind_velocity)) +
+  geom_point() + geom_smooth(method = "lm", se = F) +
+  geom_line(data = DC.2, aes(y = .fitted))
      
 
      
 
      
 
      
@@ -2769,15 +2824,36 @@ 
      How to fit asymptote model?
      
 
    • Make a scatterplot first to check for straightness (next page).
      • 
 
    
 
    
-
    windmill %>% mutate(wind_pace = 1 / wind_velocity) -> windmill
-ggplot(windmill, aes(y = DC_output, x = wind_pace)) +
-  geom_point() + geom_smooth(se = F)
    
+
    windmill %>% mutate(wind_pace = 1 / wind_velocity) -> windmill
+ggplot(windmill, aes(y = DC_output, x = wind_pace)) +
+  geom_point() + geom_smooth(se = F)
    
 
    
 
      
 
    • and run regression like this (output page after):
      • 
 
    
 
    
-
    DC.3 <- lm(DC_output ~ wind_pace, data = windmill)
    
+
    DC.3 <- lm(DC_output ~ wind_pace, data = windmill)
+summary(DC.3)
    
+
    
+
    
+Call:
+lm(formula = DC_output ~ wind_pace, data = windmill)
+
+Residuals:
+     Min       1Q   Median       3Q      Max 
+-0.20547 -0.04940  0.01100  0.08352  0.12204 
+
+Coefficients:
+            Estimate Std. Error t value Pr(>|t|)    
+(Intercept)   2.9789     0.0449   66.34   <2e-16 ***
+wind_pace    -6.9345     0.2064  -33.59   <2e-16 ***
+---
+Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+
+Residual standard error: 0.09417 on 23 degrees of freedom
+Multiple R-squared:   0.98, Adjusted R-squared:  0.9792 
+F-statistic:  1128 on 1 and 23 DF,  p-value: < 2.2e-16
    
+
    
 
    
 
    
 
    
@@ -2788,7 +2864,7 @@ 
    Scatterplot for wind_pace
    
 
    
 
    Regression output
    
 
    
-
    glance(DC.3)
    
+
    glance(DC.3)
    
 
    
 
 
    
@@ -2799,7 +2875,7 @@ 
    Regression output
    
 
    
 
    
 
    
-
    tidy(DC.3)
    
+
    tidy(DC.3)
    
 
    
 
 
    
@@ -2822,10 +2898,18 @@ 
    Comments
    
 
    
 
    Residual plot for asymptote model
    
 
    
-
    ggplot(DC.3, aes(y = .resid, x = .fitted)) + geom_point()
    
+
    ggplot(DC.3, aes(y = .resid, x = .fitted)) + geom_point()
    
 
 
    
 
    
+
    
+
    normal quantile plot of residuals
    
+
    
+
    ggplot(DC.3, aes(sample = .resid)) + stat_qq() + stat_qq_line()
    
+
+
    
+
    This is skewed (left), but is not bad (and definitely better than the one for the parabola model).
    
+
    
 
 
    
 
    What’s in w2
    
 
    
-
    w2
    
+
    w2
    
 
    
 
 
    
@@ -2865,12 +2949,12 @@ 
    Making the plot
    
 
  • pivot_longer, then plot:
    • 
 
 
    
-
    w2 %>%
-  pivot_longer(linear:asymptote, names_to="model", 
-               values_to="fit") %>%
-  ggplot(aes(x = wind_velocity, y = DC_output)) +
-  geom_point() +
-  geom_line(aes(y = fit, colour = model)) 
    
+
    w2 %>%
+  pivot_longer(linear:asymptote, names_to="model", 
+               values_to="fit") %>%
+  ggplot(aes(x = wind_velocity, y = DC_output)) +
+  geom_point() +
+  geom_line(aes(y = fit, colour = model)) 
    
 
    
 
    
 
    
@@ -2888,7 +2972,7 @@ 
    Comments
    
 
    
 
    Asymptote model summary
    
 
    
-
    tidy(DC.3)
    
+
    tidy(DC.3)
    
 
    
 
 
    
@@ -2939,8 +3023,8 @@ 
    Job done, kinda
    
 
  • Extend range of wind.velocity to 1 to 16 (steps of 0.5), and predict DC.output according to the two models:
    • 
 
 
    
-
    wv <- seq(1, 16, 0.5)
-wv
    
+
    wv <- seq(1, 16, 0.5)
+wv
    
 
    
 
     [1]  1.0  1.5  2.0  2.5  3.0  3.5  4.0  4.5  5.0  5.5  6.0  6.5  7.0
 [14]  7.5  8.0  8.5  9.0  9.5 10.0 10.5 11.0 11.5 12.0 12.5 13.0 13.5
@@ -2960,13 +3044,13 @@ 
    Setting up data frame to predict from
    
 
  • So create data frame called wv_new with those in:
    • 
 
 
    
-
    wv_new <- tibble(wind_velocity = wv, wind_pace = 1 / wv)
    
+
    wv_new <- tibble(wind_velocity = wv, wind_pace = 1 / wv)
    
 
    
 
    
 
    
 
    wv_new
    
 
    
-
    wv_new
    
+
    wv_new
    
 
    
 
 
    
@@ -2983,24 +3067,24 @@ 
    Doing predictions, one for each model
    
 
  • Use same names as before:
    • 
 
 
    
-
    linear <- predict(DC.1, wv_new)
-parabola <- predict(DC.2, wv_new)
-asymptote <- predict(DC.3, wv_new)
    
+
    linear <- predict(DC.1, wv_new)
+parabola <- predict(DC.2, wv_new)
+asymptote <- predict(DC.3, wv_new)
    
 
    
 
      
 
    • Put it all into a data frame for plotting, along with original data:
      • 
 
    
 
    
-
    my_fits <- tibble(
-  wind_velocity = wv_new$wind_velocity,
-  linear, parabola, asymptote
-)
    
+
    my_fits <- tibble(
+  wind_velocity = wv_new$wind_velocity,
+  linear, parabola, asymptote
+)
    
 
    
 
    
 
    
 
    my_fits
    
 
    
-
    my_fits
    
+
    my_fits
    
 
    
 
 
    
@@ -3017,16 +3101,16 @@ 
    Making a plot 1/2
    
 
  • To make a plot, we use the same trick as last time to get all three predictions on a plot with a legend (saving result to add to later):
    • 
 
 
    
-
    my_fits %>%
-    pivot_longer(
-    linear:asymptote,
-    names_to="model", 
-    values_to="fit"
-  ) %>%
-  ggplot(aes(
-    y = fit, x = wind_velocity,
-    colour = model
-  )) + geom_line() -> g
    
+
    my_fits %>%
+    pivot_longer(
+    linear:asymptote,
+    names_to="model", 
+    values_to="fit"
+  ) %>%
+  ggplot(aes(
+    y = fit, x = wind_velocity,
+    colour = model
+  )) + geom_line() -> g
    
 
    
 
    
 
    
@@ -3035,7 +3119,7 @@ 
    Making a plot 2/2
    
 
  • The observed wind velocities were in this range:
    • 
 
 
    
-
    (vels <- range(windmill$wind_velocity))
    
+
    (vels <- range(windmill$wind_velocity))
    
 
    
 
    [1]  2.45 10.20
    
 
    
@@ -3044,10 +3128,10 @@ 
    Making a plot 2/2
    
 
  • DC.output between 0 and 3 from asymptote model. Add rectangle to graph around where the data were:
    • 
 
 
    
-
    g + geom_rect(
-  xmin = vels[1], xmax = vels[2], ymin = 0, ymax = 3,
-  alpha=0, colour = "black"
-)
    
+
    g + geom_rect(
+  xmin = vels[1], xmax = vels[2], ymin = 0, ymax = 3,
+  alpha=0, colour = "black"
+)
    
 
    
 
    
 
    
@@ -3069,7 +3153,7 @@ 
    Comments (2)
    
 
  • For parabola model:
    • 
 
 
    
-
    tidy(DC.2)
    
+
    tidy(DC.2)
    
 
    
 
 
    
@@ -3086,7 +3170,7 @@ 
    Comments (2)
    
 
    
 
    Comments (3): asymptote model
    
 
    
-
    tidy(DC.3)
    
+
    tidy(DC.3)
    
 
    
 
 
    
diff --git a/windmill.pdf b/windmill.pdf
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z>CZ~G1hYZFJ?%{)qsjn4O*+THz!9kIHzkbl`!
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zZ8{dI(~>22xbT>%h|$b;oQT(HNK9TAVoYHFE(l@kFuyHmTxu6DP^WMzK9R7-Bft$PQ16-%vksV;SuQM^qL9pujF_EGcc#i?|MOs%CAs~b
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    and now lm
    
 
    
 
    pigs.2 <- lm(weight ~ feed, data = pigs)
-tidy(pigs.2)
    
+summary(pigs.2)
    
+
    
+
    
+Call:
+lm(formula = weight ~ feed, data = pigs)
+
+Residuals:
+   Min     1Q Median     3Q    Max 
+-3.900 -2.025 -0.570  1.845  5.000 
+
+Coefficients:
+            Estimate Std. Error t value Pr(>|t|)    
+(Intercept)   60.620      1.404  43.190  < 2e-16 ***
+feedfeed2      8.680      1.985   4.373 0.000473 ***
+feedfeed3     33.480      1.985  16.867 1.30e-11 ***
+feedfeed4     25.620      1.985  12.907 7.11e-10 ***
+---
+Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+
+Residual standard error: 3.138 on 16 degrees of freedom
+Multiple R-squared:  0.9572,    Adjusted R-squared:  0.9491 
+F-statistic: 119.1 on 3 and 16 DF,  p-value: 3.72e-11
    
+
    
+
    tidy(pigs.2)
    
 
    
 
 
    
@@ -2558,7 +2581,7 @@ 
    and now lm
    
   
 
    
 
    
-
    glance(pigs.2)
    
+
    glance(pigs.2)
    
 
    
 
 
    
@@ -2587,7 +2610,7 @@ 
    Reproducing the ANOVA
    
 
  • Pass the fitted model object into anova:
    • 
 
 
    
-
    anova(pigs.2)
    
+
    anova(pigs.2)
    
 
    
 
 
    
@@ -2602,7 +2625,7 @@ 
    Reproducing the ANOVA
    
 
  • But no Tukey this way:
    • 
 
 
    
-
    TukeyHSD(pigs.2)
    
+
    TukeyHSD(pigs.2)
    
 
    
 
    Error in UseMethod("TukeyHSD"): no applicable method for 'TukeyHSD' applied to an object of class "lm"
    
 
    
@@ -2621,14 +2644,14 @@ 
    The crickets
    
 
    The crickets data
    
 
    Read the data:
    
 
    
-
    my_url <- "http://ritsokiguess.site/datafiles/crickets2.csv"
-crickets <- read_csv(my_url)
-crickets %>% sample_n(10)
    
+
    my_url <- "http://ritsokiguess.site/datafiles/crickets2.csv"
+crickets <- read_csv(my_url)
+crickets %>% slice_sample(n = 10)
    
 
    
 
 
    
   
 
    
 
    
@@ -2637,12 +2660,12 @@ 
    The crickets data
    
 
    
 
    Fit model with lm
    
 
    
-
    crickets.1 <- lm(pulse_rate ~ temperature + species, 
-                 data = crickets)
    
+
    crickets.1 <- lm(pulse_rate ~ temperature + species, 
+                 data = crickets)
    
 
    
 
    Can I remove anything? No:
    
 
    
-
    drop1(crickets.1, test = "F") 
    
+
    drop1(crickets.1, test = "F") 
    
 
    
 
 
    
@@ -2657,7 +2680,7 @@ 
    Fit model with lm
    
 
    
 
    The summary
    
 
    
-
    summary(crickets.1)
    
+
    summary(crickets.1)
    
 
    
 
    
 Call:
@@ -2696,18 +2719,18 @@ 
    To end with a graph
    
 
  • This graph seems to need a title, which I define first.
    • 
 
 
    
-
    t1 <- "Pulse rate against temperature for two species of crickets"
-t2 <- "Temperature in degrees Celsius"
-ggplot(crickets, aes(x = temperature, y = pulse_rate,
-  colour = species)) +
-  geom_point() + geom_smooth(method = "lm", se = FALSE) +
-  ggtitle(t1, t2) -> g
    
+
    t1 <- "Pulse rate against temperature for two species of crickets"
+t2 <- "Temperature in degrees Celsius"
+ggplot(crickets, aes(x = temperature, y = pulse_rate,
+  colour = species)) +
+  geom_point() + geom_smooth(method = "lm", se = FALSE) +
+  ggtitle(t1, t2) -> g
    
 
    
 
    
 
    
 
    The graph
    
 
    
-
    g
    
+
    g
    
 
 
    
 
    
diff --git a/with_categ.pdf b/with_categ.pdf
index 36f6f81f840a4568d7cee12ee802c90fbf184c8a..c2a8f0008dd75a31ca8232a813984d692b9750c2 100644
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zG?23>4gv23N_t-2wG9#@sQN+G6B!W!Ei?UU9w{XPA}TlpAP@+Ue!O;fe0sGwqEX?xUv7d%|H$giQIN}c7cI`R|SEW|NE7H0-rpRlq6=3oqP`b<8?vs ne^m?r<179jzWM*`+oUt5q94t&D|t_V?-ib_%9hBOzWu)dTy=S0 diff --git a/_quarto.yml b/_quarto.yml index 55d961f..cd00e0c 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -6,7 +6,8 @@ project: - readfile.qmd - graphs.qmd - numsum.qmd -# - choosing.qmd + - choosing.qmd + - bootstrap_R.qmd - inference*.qmd - reports.qmd - tidy*.qmd @@ -17,6 +18,23 @@ project: - functions.qmd - dates_and_times.qmd - dip.qmd + - vector_matrix.qmd + - bootstrap.qmd + - stan.qmd + - regression.qmd + - logistic.qmd + - survival.qmd + - anova.qmd + - ancova.qmd + - manova.qmd + - profile.qmd + - discrim.qmd + - cluster.qmd + - mapping.qmd + - princomp.qmd + - factor.qmd + - multiway.qmd + format: revealjs: df-print: paged diff --git a/ancova.html b/ancova.html index d0f986c..8162598 100644 --- a/ancova.html +++ b/ancova.html @@ -2641,10 +2641,10 @@

    and then

    Taking out interaction

    @@ -2684,10 +2684,10 @@

    Predictions

    Plot of predicted values

    -
    plot_cap(prepost.2, condition = c("before", "drug"))
    +
    plot_predictions(prepost.2, condition = c("before", "drug"))
    -

    This time the lines are exactly parallel. No-interaction model forces them to have the same slope.

    +

    This time the lines are exactly parallel. No-interaction model forces them to have the same slope.

    Different look at model output

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ZSd@eHT526AC=o0hD;x!dgpwrO{{??h>wy3O diff --git a/ancova.qmd b/ancova.qmd index d817495..48a7926 100644 --- a/ancova.qmd +++ b/ancova.qmd @@ -148,7 +148,7 @@ cbind(predictions(prepost.1, newdata = new)) %>% ## Predictions (with interaction included), plotted ```{r, fig.height=4} -plot_cap(model = prepost.1, condition = c("before", "drug")) +plot_predictions(model = prepost.1, condition = c("before", "drug")) ``` Lines almost parallel, but not quite. @@ -182,7 +182,7 @@ cbind(predictions(prepost.2, newdata = new)) %>% ## Plot of predicted values ```{r, fig.height=4} -plot_cap(prepost.2, condition = c("before", "drug")) +plot_predictions(prepost.2, condition = c("before", "drug")) ``` This time the lines are *exactly* parallel. 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    Blue Jays attendances

    [25] 30430 +
      +
    • It is easier to see what is happening if we sort both the actual attendances and the bootstrap sample:
      • +
    +
    +
    sort(jays$attendance)
    +
    +
     [1] 14184 14433 15062 15086 15168 15606 16402 17264 17276 18581 19014 19217
+[13] 21195 21312 21397 21519 29306 30430 33086 34743 37929 42419 42917 44794
+[25] 48414
    +
    +
    sort(s)
    +
    +
     [1] 14433 15062 15062 15062 15086 15086 15168 16402 16402 17264 18581 19014
+[13] 19014 19217 19217 21195 21195 21312 30430 34743 34743 34743 34743 44794
+[25] 48414
    +
    +

    Getting mean of bootstrap sample

    @@ -2541,7 +2558,7 @@

    Getting mean of bootstrap sample

  • We need the mean of our bootstrap sample:
    • -
    mean(s)
    +
    mean(s)
    [1] 23055.28
    @@ -2550,7 +2567,7 @@

    Getting mean of bootstrap sample

  • This is a little different from the mean of our actual sample:
    • -
    mean(jays$attendance)
    +
    mean(jays$attendance)
    [1] 25070.16
    @@ -2566,7 +2583,7 @@

    Setting up bootstrap sampling

  • Begin by setting up a dataframe that contains a row for each bootstrap sample. I usually call this column sim. Do just 4 to get the idea:
    • -
    tibble(sim = 1:4)
    +
    tibble(sim = 1:4)
    @@ -2583,9 +2600,9 @@

    Drawing the bootstrap samples

  • Then set up to work one row at a time, and draw a bootstrap sample of the attendances in each row:
    • -
    tibble(sim = 1:4) %>% 
-  rowwise() %>% 
-  mutate(sample = list(sample(jays$attendance, replace = TRUE)))
    +
    tibble(sim = 1:4) %>% 
+  rowwise() %>% 
+  mutate(sample = list(sample(jays$attendance, replace = TRUE)))
    @@ -2605,10 +2622,10 @@

    Sample means

  • Find the mean of each sample:
    • -
    tibble(sim = 1:4) %>% 
-  rowwise() %>% 
-  mutate(sample = list(sample(jays$attendance, replace = TRUE))) %>% 
-  mutate(my_mean = mean(sample))
    +
    tibble(sim = 1:4) %>% 
+  rowwise() %>% 
+  mutate(sample = list(sample(jays$attendance, replace = TRUE))) %>%   
+  mutate(my_mean = mean(sample))
    @@ -2628,17 +2645,17 @@

    Make a histogram of them

  • rather pointless here, but to get the idea:
    • -
    tibble(sim = 1:4) %>% 
-  rowwise() %>% 
-  mutate(sample = list(sample(jays$attendance, replace = TRUE))) %>% 
-  mutate(my_mean = mean(sample)) %>% 
-  ggplot(aes(x = my_mean)) + geom_histogram(bins = 3) -> g
    +
    tibble(sim = 1:4) %>% 
+  rowwise() %>% 
+  mutate(sample = list(sample(jays$attendance, replace = TRUE))) %>% 
+  mutate(my_mean = mean(sample)) %>% 
+  ggplot(aes(x = my_mean)) + geom_histogram(bins = 3) -> g

    The (pointless) histogram

    -
    g
    +
    g
    @@ -2648,17 +2665,17 @@

    Now do again with a decent number of bootstrap samples

  • say 1000, and put a decent number of bins on the histogram also:
    • -
    tibble(sim = 1:1000) %>% 
-  rowwise() %>% 
-  mutate(sample = list(sample(jays$attendance, replace = TRUE))) %>% 
-  mutate(my_mean = mean(sample)) %>% 
-  ggplot(aes(x = my_mean)) + geom_histogram(bins = 10) -> g
    +
    tibble(sim = 1:1000) %>% 
+  rowwise() %>% 
+  mutate(sample = list(sample(jays$attendance, replace = TRUE))) %>% 
+  mutate(my_mean = mean(sample)) %>% 
+  ggplot(aes(x = my_mean)) + geom_histogram(bins = 10) -> g

    The (better) histogram

    -
    g
    +
    g
    @@ -2679,9 +2696,9 @@

    Comments on the code 1/2

  • You might have been wondering about this:
    • -
    tibble(sim = 1:4) %>% 
-  rowwise() %>% 
-  mutate(sample = list(sample(jays$attendance, replace = TRUE)))
    +
    tibble(sim = 1:4) %>% 
+  rowwise() %>% 
+  mutate(sample = list(sample(jays$attendance, replace = TRUE)))
    @@ -2710,14 +2727,24 @@

    Comments on the code 2/2

    Two samples

    • Assumption: both samples are from a normal distribution.
      • -
    • In practice, each sample is “normal enough” given its sample size, since Central Limit Theorem will help.
      • +
    • In this case, each sample should be “normal enough” given its sample size, since Central Limit Theorem will help.
    • Use bootstrap on each group independently, as above.

    Kids learning to read

    -
    ggplot(kids, aes(x=group, y=score)) + geom_boxplot()
    +
    + +
    + +
    +
    +
    +
    +
    ggplot(kids, aes(x=group, y=score)) + geom_boxplot()
    @@ -2727,8 +2754,8 @@

    Getting just the control group

  • Use filter to select rows where something is true:
    • -
    kids %>% filter(group=="c") -> controls
-controls
    +
    kids %>% filter(group=="c") -> controls
+controls
    @@ -2742,23 +2769,23 @@

    Getting just the control group

    Bootstrap these

    -
    tibble(sim = 1:1000) %>% 
-  rowwise() %>% 
-  mutate(sample = list(sample(controls$score, replace = TRUE))) %>% 
-  mutate(my_mean = mean(sample)) %>% 
-  ggplot(aes(x = my_mean)) + geom_histogram(bins = 10)
    +
    tibble(sim = 1:1000) %>% 
+  rowwise() %>% 
+  mutate(sample = list(sample(controls$score, replace = TRUE))) %>% 
+  mutate(my_mean = mean(sample)) %>% 
+  ggplot(aes(x = my_mean)) + geom_histogram(bins = 10)

    … and the treatment group:

    -
    kids %>% filter(group=="t") -> treats
-tibble(sim = 1:1000) %>% 
-  rowwise() %>% 
-  mutate(sample = list(sample(treats$score, replace = TRUE))) %>% 
-  mutate(my_mean = mean(sample)) %>% 
-  ggplot(aes(x = my_mean)) + geom_histogram(bins = 10)
    +
    kids %>% filter(group=="t") -> treats
+tibble(sim = 1:1000) %>% 
+  rowwise() %>% 
+  mutate(sample = list(sample(treats$score, replace = TRUE))) %>% 
+  mutate(my_mean = mean(sample)) %>% 
+  ggplot(aes(x = my_mean)) + geom_histogram(bins = 10)
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tibble(sim = 1:4) %>% rowwise() %>% - mutate(sample = list(sample(jays$attendance, replace = TRU - - + mutate(sample = list(sample(jays$attendance, replace = TRUE))) %>% + mutate(my_mean = mean(sample)) ``` -```{r} - - -``` - -`{E))) %>%} mutate(my_mean = mean(sample))` - - These are (four simulated values of) the bootstrapped sampling distribution of the sample mean. diff --git a/choosing.html b/choosing.html index ab87ca7..521fb21 100644 --- a/choosing.html +++ b/choosing.html @@ -1,392 +1,2484 @@ - - - - + + + + + + + + + + Choosing things in dataframes - - + + + + + + + + + + + + + + resizeNewWidth = newWidth; + resizeNewHeight = newHeight; + + if (!resizePending) resizeDelayed(); + }; +}; + +var PagedTableDoc; +(function (PagedTableDoc) { + var allPagedTables = []; + + PagedTableDoc.initAll = function() { + allPagedTables = []; + + var pagedTables = [].slice.call(document.querySelectorAll('[data-pagedtable="false"],[data-pagedtable=""]')); + pagedTables.forEach(function(pagedTable, idx) { + pagedTable.setAttribute("data-pagedtable", "true"); + pagedTable.setAttribute("pagedtable-page", 0); + pagedTable.setAttribute("class", "pagedtable-wrapper"); + + var pagedTableInstance = new PagedTable(pagedTable); + pagedTableInstance.init(); + + allPagedTables.push(pagedTableInstance); + }); + }; + + PagedTableDoc.resizeAll = function() { + allPagedTables.forEach(function(pagedTable) { + pagedTable.render(); + }); + }; + + window.addEventListener("resize", PagedTableDoc.resizeAll); + + return PagedTableDoc; +})(PagedTableDoc || (PagedTableDoc = {})); + +window.onload = function() { + PagedTableDoc.initAll(); +}; + - +
    -
    +

    Choosing things in dataframes

    @@ -397,14 +2489,14 @@

    Choosing things in dataframes

    Packages

    The usual:

    -
    library(tidyverse)
    +
    library(tidyverse)

    Doing things with data frames

    Let’s go back to our Australian athletes:

    -
    athletes
    +
    athletes
    @@ -418,7 +2510,7 @@

    Doing things with data frames

    Choosing a column

    -
    athletes %>% select(Sport)
    +
    athletes %>% select(Sport)
    @@ -432,7 +2524,7 @@

    Choosing a column

    Choosing several columns

    -
    athletes %>% select(Sport, Hg, BMI)
    +
    athletes %>% select(Sport, Hg, BMI)
    @@ -446,7 +2538,7 @@

    Choosing several columns

    Choosing consecAutive columns

    -
    athletes %>% select(Sex:WCC)
    +
    athletes %>% select(Sex:WCC)
    @@ -460,7 +2552,7 @@

    Choosing consecAutive columns

    Choosing all-but some columns

    -
    athletes %>% select(-(RCC:LBM))
    +
    athletes %>% select(-(RCC:LBM))
    @@ -485,7 +2577,7 @@

    Select-helpers

    Columns whose names Abegin with S

    -
    athletes %>% select(starts_with("S"))
    +
    athletes %>% select(starts_with("S"))
    @@ -500,7 +2592,7 @@

    Columns whose names Abegin with S

    Columns whose names end with C

    either uppercase or lowercase:

    -
    athletes %>% select(ends_with("c"))
    +
    athletes %>% select(ends_with("c"))
    @@ -515,7 +2607,7 @@

    Columns whose names end with C

    Case-sensitive

    This works with any of the select-helpers:

    -
    athletes %>% select(ends_with("C", ignore.case=FALSE))
    +
    athletes %>% select(ends_with("C", ignore.case=FALSE))
    @@ -529,7 +2621,7 @@

    Case-sensitive

    Column names containing letter R

    -
    athletes %>% select(contains("r"))
    +
    athletes %>% select(contains("r"))
    @@ -549,7 +2641,7 @@

    Exactly two characters, ending with T

  • $ means “end of text”.
    • -
    athletes %>% select(matches("^.t$"))
    +
    athletes %>% select(matches("^.t$"))
    @@ -567,7 +2659,7 @@

    Choosing columns by property

  • eg, to choose text columns:
    • -
    athletes %>% select(where(is.character))
    +
    athletes %>% select(where(is.character))
    @@ -581,7 +2673,7 @@

    Choosing columns by property

    Choosing rows by number

    -
    athletes %>% slice(16:25)
    +
    athletes %>% slice(16:25)
    @@ -595,8 +2687,8 @@

    Choosing rows by number

    Non-consecutive rows

    -
    athletes %>% 
-  slice(10, 13, 17, 42)
    +
    athletes %>% 
+  slice(10, 13, 17, 42)
    @@ -610,7 +2702,7 @@

    Non-consecutive rows

    A random sample of rows

    -
    athletes %>% slice_sample(n=8)
    +
    athletes %>% slice_sample(n=8)
    @@ -624,7 +2716,7 @@

    A random sample of rows

    Rows for which something is true

    -
    athletes %>% filter(Sport == "Tennis")
    +
    athletes %>% filter(Sport == "Tennis")
    @@ -638,7 +2730,7 @@

    Rows for which something is true

    More complicated selections

    -
    athletes %>% filter(Sport == "Tennis", RCC < 5)
    +
    athletes %>% filter(Sport == "Tennis", RCC < 5)
    @@ -652,8 +2744,8 @@

    More complicated selections

    Another way to do “and”

    -
    athletes %>% filter(Sport == "Tennis") %>% 
-  filter(RCC < 5)
    +
    athletes %>% filter(Sport == "Tennis") %>% 
+  filter(RCC < 5)
    @@ -667,7 +2759,7 @@

    Another way to do “and”

    Either/Or

    -
    athletes %>% filter(Sport == "Tennis" | RCC > 5)
    +
    athletes %>% filter(Sport == "Tennis" | RCC > 5)
    @@ -681,7 +2773,7 @@

    Either/Or

    Sorting into order

    -
    athletes %>% arrange(RCC)
    +
    athletes %>% arrange(RCC)
    @@ -695,7 +2787,7 @@

    Sorting into order

    Breaking ties by another variable

    -
    athletes %>% arrange(RCC, BMI)
    +
    athletes %>% arrange(RCC, BMI)
    @@ -709,7 +2801,7 @@

    Breaking ties by another variable

    Descending order

    -
    athletes %>% arrange(desc(BMI))
    +
    athletes %>% arrange(desc(BMI))
    @@ -723,10 +2815,10 @@

    Descending order

    “The top ones”

    -
    athletes %>%
+
    athletes %>%
   arrange(desc(Wt)) %>%
   slice(1:7) %>%
-  select(Sport, Wt)
    
+  select(Sport, Wt)
    @@ -740,9 +2832,9 @@

    “The top ones”

    Another way

    -
    athletes %>% 
+
    athletes %>% 
   slice_max(order_by = Wt, n=7) %>% 
-  select(Sport, Wt)
    
+  select(Sport, Wt)
    @@ -756,10 +2848,10 @@

    Another way

    Create new variables from old ones

    -
    athletes %>%
+
    athletes %>%
   mutate(wt_lb = Wt * 2.2) %>%
   select(Sport, Sex, Wt, wt_lb) %>% 
-  arrange(Wt)
    
+  arrange(Wt)
    @@ -774,17 +2866,17 @@

    Create new variables from old ones

    Turning the result into a number

    Output is always data frame unless you explicitly turn it into something else, eg. the weight of the heaviest athlete, as a number:

    -
    athletes %>% arrange(desc(Wt)) %>% pluck("Wt", 1)
    +
    athletes %>% arrange(desc(Wt)) %>% pluck("Wt", 1)
    [1] 123.2

    Or the 20 heaviest weights in descending order:

    -
    athletes %>%
+
    athletes %>%
   arrange(desc(Wt)) %>%
   slice(1:20) %>%
-  pluck("Wt")
    
+  pluck("Wt")
     [1] 123.20 113.70 111.30 108.20 102.70 101.00 100.20  98.00  97.90  97.90
 [11]  97.00  96.90  96.30  94.80  94.80  94.70  94.70  94.60  94.25  94.20
    @@ -794,10 +2886,10 @@

    Turning the result into a number

    Another way to do the last one

    -
    athletes %>%
+
    athletes %>%
   arrange(desc(Wt)) %>%
   slice(1:20) %>%
-  pull("Wt")
    
+  pull("Wt")
     [1] 123.20 113.70 111.30 108.20 102.70 101.00 100.20  98.00  97.90  97.90
 [11]  97.00  96.90  96.30  94.80  94.80  94.70  94.70  94.60  94.25  94.20
    @@ -809,7 +2901,7 @@

    Another way to do the last one

    To find the mean height of the women athletes

    Two ways:

    -
    athletes %>% group_by(Sex) %>% summarize(m = mean(Ht))
    +
    athletes %>% group_by(Sex) %>% summarize(m = mean(Ht))
    @@ -820,9 +2912,9 @@

    To find the mean height of the women athletes

    -
    athletes %>%
+
    athletes %>%
   filter(Sex == "female") %>%
-  summarize(m = mean(Ht))
    
+  summarize(m = mean(Ht))
    @@ -834,11 +2926,11 @@

    To find the mean height of the women athletes

    -

    Summary of data selection/arrangement “verbs”

    +

    Summary of data selection/arrangement “verbs”

    --++ @@ -900,9 +2992,9 @@

    Looking things up in another data frame

  • Suppose you are working in the nails department of a hardware store and you find that you have sold these items:
    • -
    my_url <- "http://ritsokiguess.site/datafiles/nail_sales.csv"
+
    my_url <- "http://ritsokiguess.site/datafiles/nail_sales.csv"
 sales <- read_csv(my_url)
-sales
    
+sales
    @@ -920,9 +3012,9 @@

    Product descriptions and prices

  • Fortunately you found a list of product descriptions and prices:

    • -
    my_url <- "http://ritsokiguess.site/datafiles/nail_desc.csv"
+
    my_url <- "http://ritsokiguess.site/datafiles/nail_desc.csv"
 desc <- read_csv(my_url)
-desc
    
+desc
    @@ -933,7 +3025,7 @@

    Product descriptions and prices

      -
    • the size values are measured in inches (symbol "), but R uses the same symbol for the start and end of text, so the " representing “inches” is “escaped”. Hence the odd look.
      • +
    • the size values are measured in inches (symbol "), but R uses the same symbol for the start and end of text, so the " representing “inches” is “escaped”. Hence the odd look.
    @@ -943,7 +3035,7 @@

    The lookup

  • left_join.
    • -
    sales %>% left_join(desc)
    +
    sales %>% left_join(desc)
    @@ -963,9 +3055,9 @@

    What we have

    So now can work out how much the total revenue was:

    -
    sales %>% left_join(desc) %>% 
+
    sales %>% left_join(desc) %>% 
   mutate(product_revenue = sales*price) %>% 
-  summarize(total_revenue = sum(product_revenue))
    
+  summarize(total_revenue = sum(product_revenue))
    @@ -994,8 +3086,8 @@

    Matching on only some matching names

  • Suppose the sales dataframe also had a column qty (which was the quantity sold):
    • -
    sales %>% rename("qty"="sales") -> sales1
-sales1
    +
    sales %>% rename("qty"="sales") -> sales1
+sales1
    @@ -1012,8 +3104,8 @@

    Matching on only some matching names

    Matching only on product code

    -
    sales1 %>% 
-  left_join(desc, join_by(product_code))
    +
    sales1 %>% 
+  left_join(desc, join_by(product_code))
    @@ -1024,8 +3116,8 @@

    Matching only on product code

    -
    sales1 %>% 
-  left_join(desc)
    +
    sales1 %>% 
+  left_join(desc)
    @@ -1045,8 +3137,8 @@

    Matching on different names 1/2

  • Suppose the product code in sales was just code:
    • -
    sales %>% rename("code" = "product_code") -> sales2
-sales2
    +
    sales %>% rename("code" = "product_code") -> sales2
+sales2
    @@ -1066,8 +3158,8 @@

    Matching on different names 2/2

  • Use join_by, but like this:
    • -
    sales2 %>% 
-  left_join(desc, join_by(code == product_code))
    +
    sales2 %>% 
+  left_join(desc, join_by(code == product_code))
    @@ -1089,7 +3181,7 @@

    Other types of join

    Full join here

    -
    sales %>% full_join(desc)
    +
    sales %>% full_join(desc)
    @@ -1107,7 +3199,7 @@

    Full join here

    The same thing, but with anti_join

    Anything in first df but not in second?

    -
    desc %>% anti_join(sales)
    +
    desc %>% anti_join(sales)
    @@ -1116,7 +3208,7 @@

    The same thing, but with anti_join

    -
    sales %>% anti_join(desc)
    +
    sales %>% anti_join(desc)
    @@ -1135,26 +3227,854 @@

    The same thing, but with anti_join

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a/choosing_files/execute-results/html.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "hash": "8a1b88b16954fb13b78c31ee298ba634", - "result": { - "markdown": "---\ntitle: \"Choosing things in dataframes\"\n---\n\n\n## Packages\n\nThe usual:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\n```\n:::\n\n\n## Doing things with data frames\n\nLet's go back to our Australian athletes:\n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## Choosing a column\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(Sport)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## Choosing several columns\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(Sport, Hg, BMI)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## Choosing consecAutive columns\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(Sex:WCC)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## Choosing all-but some columns\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(-(RCC:LBM))\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## Select-helpers\n\nOther ways to select columns: those whose name:\n\n- `starts_with` something\n- `ends_with` something\n- `contains` something\n- `matches` a \"regular expression\"\n- `everything()` select all the columns\n\n## Columns whose names Abegin with S\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(starts_with(\"S\"))\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## Columns whose names end with C\n\neither uppercase or lowercase:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(ends_with(\"c\"))\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## Case-sensitive\n\nThis works with any of the select-helpers:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(ends_with(\"C\", ignore.case=FALSE))\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## Column names containing letter R\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(contains(\"r\"))\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## Exactly two characters, ending with T\n\nIn regular expression terms, this is `^.t$`:\n\n- `^` means \"start of text\"\n- `.` means \"exactly one character, but could be anything\"\n- `$` means \"end of text\".\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(matches(\"^.t$\"))\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## Choosing columns by property\n\n- Use `where` as with summarizing several columns\n- eg, to choose text columns:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(where(is.character))\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## Choosing rows by number\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% slice(16:25)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## Non-consecutive rows\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% \n slice(10, 13, 17, 42)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## A random sample of rows\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% slice_sample(n=8)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## Rows for which something is true\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% filter(Sport == \"Tennis\")\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## More complicated selections\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% filter(Sport == \"Tennis\", RCC < 5)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## Another way to do \"and\"\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% filter(Sport == \"Tennis\") %>% \n filter(RCC < 5)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## Either/Or\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% filter(Sport == \"Tennis\" | RCC > 5)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## Sorting into order\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% arrange(RCC)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## Breaking ties by another variable\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% arrange(RCC, BMI)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## Descending order\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% arrange(desc(BMI))\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## \"The top ones\"\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>%\n arrange(desc(Wt)) %>%\n slice(1:7) %>%\n select(Sport, Wt)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## Another way\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% \n slice_max(order_by = Wt, n=7) %>% \n select(Sport, Wt)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## Create new variables from old ones\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>%\n mutate(wt_lb = Wt * 2.2) %>%\n select(Sport, Sex, Wt, wt_lb) %>% \n arrange(Wt)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## Turning the result into a number\n\nOutput is always data frame unless you explicitly turn it into something\nelse, eg. the weight of the heaviest athlete, as a number:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% arrange(desc(Wt)) %>% pluck(\"Wt\", 1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 123.2\n```\n:::\n:::\n\n\nOr the 20 heaviest weights in descending order:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>%\n arrange(desc(Wt)) %>%\n slice(1:20) %>%\n pluck(\"Wt\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 123.20 113.70 111.30 108.20 102.70 101.00 100.20 98.00 97.90 97.90\n[11] 97.00 96.90 96.30 94.80 94.80 94.70 94.70 94.60 94.25 94.20\n```\n:::\n:::\n\n\n## Another way to do the last one\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>%\n arrange(desc(Wt)) %>%\n slice(1:20) %>%\n pull(\"Wt\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 123.20 113.70 111.30 108.20 102.70 101.00 100.20 98.00 97.90 97.90\n[11] 97.00 96.90 96.30 94.80 94.80 94.70 94.70 94.60 94.25 94.20\n```\n:::\n:::\n\n\n`pull` grabs the column you name *as a vector* (of whatever it\ncontains).\n\n## To find the mean height of the women athletes\n\nTwo ways:\n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% group_by(Sex) %>% summarize(m = mean(Ht))\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>%\n filter(Sex == \"female\") %>%\n summarize(m = mean(Ht))\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n\\normalsize\n\n## Summary of data selection/arrangement \"verbs\" {.smaller}\n\n| Verb | Purpose |\n|:---------------|:-------------------------------------------------------|\n| `select` | Choose columns |\n| `slice` | Choose rows by number |\n| `slice_sample` | Choose random rows |\n| `slice_max` | Choose rows with largest values on a variable (also `slice_min`) |\n| `filter` | Choose rows satisfying conditions |\n| `arrange` | Sort in order by column(s) |\n| `mutate` | Create new variables |\n| `group_by` | Create groups to work with |\n| `summarize` | Calculate summary statistics (by groups if defined) |\n| `pluck` | Extract items from data frame |\n| `pull` | Extract a single column from a data frame as a vector |\n\n## Looking things up in another data frame\n\n- Suppose you are working in the nails department of a hardware store\n and you find that you have sold these items:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \"http://ritsokiguess.site/datafiles/nail_sales.csv\"\nsales <- read_csv(my_url)\nsales\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## Product descriptions and prices\n\n- but you don't remember what these product codes are, and you would\n like to know the total revenue from these sales.\n\n- Fortunately you found a list of product descriptions and prices:\n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \"http://ritsokiguess.site/datafiles/nail_desc.csv\"\ndesc <- read_csv(my_url)\ndesc\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n- the `size` values are measured in inches (symbol `\"`), but R uses\n the same symbol for the start and end of text, so the `\"`\n representing \"inches\" is \"escaped\". Hence the odd look.\n\n\\normalsize\n\n## The lookup\n\n- How do you \"look up\" the product codes to find the product\n descriptions and prices?\n- `left_join`.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales %>% left_join(desc)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## What we have\n\n- this looks up all the rows in the *first* dataframe that are also in\n the *second*.\n- by default matches all columns with same name in two dataframes\n (`product_code` here)\n- get *all* columns in *both* dataframes. The rows are the ones for\n that `product_code`.\n\nSo now can work out how much the total revenue was:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales %>% left_join(desc) %>% \n mutate(product_revenue = sales*price) %>% \n summarize(total_revenue = sum(product_revenue))\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## More comments\n\n- if any product codes are not matched, you get NA in the added\n columns\n- anything in the *second* dataframe that was not in the first does\n not appear (here, any products that were not sold)\n- other variations (examples follow):\n - if there are two columns with the same name in the two\n dataframes, and you only want to match on one, use `by` with one\n column name\n - if the columns you want to look up have different names in the\n two dataframes, use `by` with a \"named list\"\n\n## Matching on only some matching names\n\n- Suppose the `sales` dataframe *also* had a column `qty` (which was\n the quantity sold):\n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales %>% rename(\"qty\"=\"sales\") -> sales1\nsales1\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n\\normalsize\n\n- The `qty` in `sales1` is the quantity sold, but the `qty` in `desc`\n is the number of nails in a package. These should *not* be matched:\n they are different things.\n\n## Matching only on product code\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales1 %>% \n left_join(desc, join_by(product_code))\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nsales1 %>% \n left_join(desc)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n- Get `qty.x` (from `sales1`) and `qty.y` (from `desc`).\n\n## Matching on different names 1/2\n\n- Suppose the product code in `sales` was just `code`:\n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales %>% rename(\"code\" = \"product_code\") -> sales2\nsales2\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n\\normalsize\n\n- How to match the two product codes that have different names?\n\n## Matching on different names 2/2\n\n- Use `join_by`, but like this:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales2 %>% \n left_join(desc, join_by(code == product_code))\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n## Other types of join\n\n- `right_join`: interchanges roles, looking up keys from second\n dataframe in first.\n- `anti_join`: give me all the rows in the first dataframe that are\n *not* in the second. (Use this eg. to see whether the product\n descriptions are incomplete.)\n- `full_join`: give me all the rows in both dataframes, with missings\n as needed.\n\n## Full join here\n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales %>% full_join(desc)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\n\\normalsize\n\n- The missing `sales` for \"masonry nail\" says that it was in the\n lookup table `desc`, but we didn't sell any.\n\n## The same thing, but with `anti_join`\n\nAnything in first df but not in second?\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndesc %>% anti_join(sales)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n\n```{.r .cell-code}\nsales %>% anti_join(desc)\n```\n\n::: {.cell-output-display}\n`````{=html}\n
    \n \n
    \n`````\n:::\n:::\n\n\nMasonry nails are the only thing in our product description file that we\ndid not sell any of.\n", - "supporting": [ - "choosing_files" - ], - "filters": [ - "rmarkdown/pagebreak.lua" - ], - "includes": { - "include-in-header": [ - "\n\n" - ], - "include-after-body": [ - "\n\n\n" - ] - }, - "engineDependencies": {}, - "preserve": {}, - "postProcess": true - } -} \ No newline at end of file diff --git a/choosing_files/execute-results/tex.json b/choosing_files/execute-results/tex.json deleted file mode 100644 index b6df1ec..0000000 --- a/choosing_files/execute-results/tex.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "hash": "1a27c83e2f5ea4aee31a32c65b194163", - "result": { - "markdown": "---\ntitle: \"Choosing things in dataframes\"\n---\n\n\n\n## Packages\n\nThe usual:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\n```\n:::\n\n\n\n\n## Doing things with data frames\nLet’s go back to our Australian athletes: \n\n\n\n::: {.cell}\n\n:::\n\n\n\n\\scriptsize\n\n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM\n \n 1 female Netba~ 4.56 13.3 42.2 13.6 20 19.2 49 11.3 53.1\n 2 female Netba~ 4.15 6 38 12.7 59 21.2 110. 25.3 47.1\n 3 female Netba~ 4.16 7.6 37.5 12.3 22 21.4 89 19.4 53.4\n 4 female Netba~ 4.32 6.4 37.7 12.3 30 21.0 98.3 19.6 48.8\n 5 female Netba~ 4.06 5.8 38.7 12.8 78 21.8 122. 23.1 56.0\n 6 female Netba~ 4.12 6.1 36.6 11.8 21 21.4 90.4 16.9 56.4\n 7 female Netba~ 4.17 5 37.4 12.7 109 21.5 107. 21.3 53.1\n 8 female Netba~ 3.8 6.6 36.5 12.4 102 24.4 157. 26.6 54.4\n 9 female Netba~ 3.96 5.5 36.3 12.4 71 22.6 101. 17.9 56.0\n10 female Netba~ 4.44 9.7 41.4 14.1 64 22.8 126. 25.0 51.6\n# i 192 more rows\n# i 2 more variables: Ht , Wt \n```\n:::\n:::\n\n\n\\normalsize\n\n## Choosing a column\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(Sport)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 1\n Sport \n \n 1 Netball\n 2 Netball\n 3 Netball\n 4 Netball\n 5 Netball\n 6 Netball\n 7 Netball\n 8 Netball\n 9 Netball\n10 Netball\n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Choosing several columns\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(Sport, Hg, BMI)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 3\n Sport Hg BMI\n \n 1 Netball 13.6 19.2\n 2 Netball 12.7 21.2\n 3 Netball 12.3 21.4\n 4 Netball 12.3 21.0\n 5 Netball 12.8 21.8\n 6 Netball 11.8 21.4\n 7 Netball 12.7 21.5\n 8 Netball 12.4 24.4\n 9 Netball 12.4 22.6\n10 Netball 14.1 22.8\n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Choosing consecutive columns\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(Sex:WCC)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 4\n Sex Sport RCC WCC\n \n 1 female Netball 4.56 13.3\n 2 female Netball 4.15 6 \n 3 female Netball 4.16 7.6\n 4 female Netball 4.32 6.4\n 5 female Netball 4.06 5.8\n 6 female Netball 4.12 6.1\n 7 female Netball 4.17 5 \n 8 female Netball 3.8 6.6\n 9 female Netball 3.96 5.5\n10 female Netball 4.44 9.7\n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Choosing all-but some columns\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(-(RCC:LBM))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 4\n Sex Sport Ht Wt\n \n 1 female Netball 177. 59.9\n 2 female Netball 173. 63 \n 3 female Netball 176 66.3\n 4 female Netball 170. 60.7\n 5 female Netball 183 72.9\n 6 female Netball 178. 67.9\n 7 female Netball 177. 67.5\n 8 female Netball 174. 74.1\n 9 female Netball 174. 68.2\n10 female Netball 174. 68.8\n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Select-helpers\nOther ways to select columns: those whose name:\n\n- `starts_with` something\n- `ends_with` something\n- `contains` something\n- `matches` a “regular expression”\n- `everything()` select all the columns\n\n## Columns whose names begin with S \n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(starts_with(\"S\"))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 3\n Sex Sport SSF\n \n 1 female Netball 49 \n 2 female Netball 110. \n 3 female Netball 89 \n 4 female Netball 98.3\n 5 female Netball 122. \n 6 female Netball 90.4\n 7 female Netball 107. \n 8 female Netball 157. \n 9 female Netball 101. \n10 female Netball 126. \n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Columns whose names end with C\n\neither uppercase or lowercase:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(ends_with(\"c\"))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 3\n RCC WCC Hc\n \n 1 4.56 13.3 42.2\n 2 4.15 6 38 \n 3 4.16 7.6 37.5\n 4 4.32 6.4 37.7\n 5 4.06 5.8 38.7\n 6 4.12 6.1 36.6\n 7 4.17 5 37.4\n 8 3.8 6.6 36.5\n 9 3.96 5.5 36.3\n10 4.44 9.7 41.4\n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Case-sensitive\n\nThis works with any of the select-helpers:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(ends_with(\"C\", ignore.case=FALSE))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 2\n RCC WCC\n \n 1 4.56 13.3\n 2 4.15 6 \n 3 4.16 7.6\n 4 4.32 6.4\n 5 4.06 5.8\n 6 4.12 6.1\n 7 4.17 5 \n 8 3.8 6.6\n 9 3.96 5.5\n10 4.44 9.7\n# i 192 more rows\n```\n:::\n:::\n\n\n\n\n## Column names containing letter R\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(contains(\"r\"))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 3\n Sport RCC Ferr\n \n 1 Netball 4.56 20\n 2 Netball 4.15 59\n 3 Netball 4.16 22\n 4 Netball 4.32 30\n 5 Netball 4.06 78\n 6 Netball 4.12 21\n 7 Netball 4.17 109\n 8 Netball 3.8 102\n 9 Netball 3.96 71\n10 Netball 4.44 64\n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Exactly two characters, ending with T\n\nIn regular expression terms, this is `^.t$`:\n\n- `^` means “start of text”\n- `.` means “exactly one character, but could be anything”\n- `$` means “end of text”.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(matches(\"^.t$\"))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 2\n Ht Wt\n \n 1 177. 59.9\n 2 173. 63 \n 3 176 66.3\n 4 170. 60.7\n 5 183 72.9\n 6 178. 67.9\n 7 177. 67.5\n 8 174. 74.1\n 9 174. 68.2\n10 174. 68.8\n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Choosing columns by property\n\n- Use `where` as with summarizing several columns\n- eg, to choose text columns:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% select(where(is.character))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 2\n Sex Sport \n \n 1 female Netball\n 2 female Netball\n 3 female Netball\n 4 female Netball\n 5 female Netball\n 6 female Netball\n 7 female Netball\n 8 female Netball\n 9 female Netball\n10 female Netball\n# i 192 more rows\n```\n:::\n:::\n\n\n\n\n## Choosing rows by number \n\n\\tiny\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% slice(16:25)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 10 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM\n \n 1 female Netba~ 4.25 10.7 39.5 13.2 127 24.5 157. 26.5 54.5\n 2 female Netba~ 4.46 10.9 39.7 13.7 102 24.0 116. 23.0 57.2\n 3 female Netba~ 4.4 9.3 40.4 13.6 86 26.2 182. 30.1 54.4\n 4 female Netba~ 4.83 8.4 41.8 13.4 40 20.0 71.6 13.9 57.6\n 5 female Netba~ 4.23 6.9 38.3 12.6 50 25.7 144. 26.6 61.5\n 6 female Netba~ 4.24 8.4 37.6 12.5 58 25.6 201. 35.5 53.5\n 7 female Netba~ 3.95 6.6 38.4 12.8 33 19.9 68.9 15.6 54.1\n 8 female Netba~ 4.03 8.5 37.7 13 51 23.4 104. 19.6 55.4\n 9 female BBall 3.96 7.5 37.5 12.3 60 20.6 109. 19.8 63.3\n10 female BBall 4.41 8.3 38.2 12.7 68 20.7 103. 21.3 58.6\n# i 2 more variables: Ht , Wt \n```\n:::\n:::\n\n\n\\normalsize\n\n\n\n## Non-consecutive rows \n\n\\tiny\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% \n slice(10, 13, 17, 42)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 4 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM\n \n1 female Netball 4.44 9.7 41.4 14.1 64 22.8 126. 25.0 51.6\n2 female Netball 4.02 9.1 37.7 12.7 107 23.0 77 18.1 57.3\n3 female Netball 4.46 10.9 39.7 13.7 102 24.0 116. 23.0 57.2\n4 female Row 4.37 8.1 41.8 14.3 53 23.5 98 21.8 63.0\n# i 2 more variables: Ht , Wt \n```\n:::\n:::\n\n\n\n\\normalsize\n\n## A random sample of rows\n\n\\tiny\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% slice_sample(n=8)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 8 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM\n \n1 female Row 4.87 6.4 44.8 15 64 20.2 99.8 20.1 52.7\n2 male Tennis 5.66 8.3 50.2 17.7 38 23.8 56.5 10.0 72 \n3 male T400m 4.55 5.55 42.6 14.4 106 21.2 34.1 6.06 57 \n4 female BBall 4.35 7.8 41.4 14.1 30 22.0 118. 23.3 48.3\n5 male Row 5.22 6 46.6 15.7 72 25.1 43.1 7.49 83 \n6 male WPolo 4.63 14.3 44.8 15 133 25.4 49.5 8.97 79 \n7 male WPolo 4.91 10.2 45 15.2 234 23.7 56.5 10.1 68 \n8 male Row 5.22 8.4 47.5 16.2 89 25.3 44.5 9.36 79 \n# i 2 more variables: Ht , Wt \n```\n:::\n:::\n\n\n\\normalsize\n\n## Rows for which something is true\n\n\\tiny\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% filter(Sport == \"Tennis\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 11 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM\n \n 1 female Tennis 4 4.2 36.6 12 57 25.4 109 20.9 56.6\n 2 female Tennis 4.4 4 40.8 13.9 73 22.1 98.1 19.6 56.0\n 3 female Tennis 4.38 7.9 39.8 13.5 88 21.2 80.6 17.1 46.5\n 4 female Tennis 4.08 6.6 37.8 12.1 182 20.5 68.3 15.3 51.8\n 5 female Tennis 4.98 6.4 44.8 14.8 80 17.1 47.6 11.1 42.2\n 6 female Tennis 5.16 7.2 44.3 14.5 88 18.3 61.9 12.9 48.8\n 7 female Tennis 4.66 6.4 40.9 13.9 109 18.4 38.2 8.45 41.9\n 8 male Tennis 5.66 8.3 50.2 17.7 38 23.8 56.5 10.0 72 \n 9 male Tennis 5.03 6.4 42.7 14.3 122 22.0 47.6 8.51 68 \n10 male Tennis 4.97 8.8 43 14.9 233 22.3 60.4 11.5 63 \n11 male Tennis 5.38 6.3 46 15.7 32 21.1 34.9 6.26 72 \n# i 2 more variables: Ht , Wt \n```\n:::\n:::\n\n\n\\normalsize\n\n## More complicated selections\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% filter(Sport == \"Tennis\", RCC < 5)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 7 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM\n \n1 female Tennis 4 4.2 36.6 12 57 25.4 109 20.9 56.6\n2 female Tennis 4.4 4 40.8 13.9 73 22.1 98.1 19.6 56.0\n3 female Tennis 4.38 7.9 39.8 13.5 88 21.2 80.6 17.1 46.5\n4 female Tennis 4.08 6.6 37.8 12.1 182 20.5 68.3 15.3 51.8\n5 female Tennis 4.98 6.4 44.8 14.8 80 17.1 47.6 11.1 42.2\n6 female Tennis 4.66 6.4 40.9 13.9 109 18.4 38.2 8.45 41.9\n7 male Tennis 4.97 8.8 43 14.9 233 22.3 60.4 11.5 63 \n# i 2 more variables: Ht , Wt \n```\n:::\n:::\n\n\n\n## Another way to do \"and\"\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% filter(Sport == \"Tennis\") %>% \n filter(RCC < 5)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 7 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM\n \n1 female Tennis 4 4.2 36.6 12 57 25.4 109 20.9 56.6\n2 female Tennis 4.4 4 40.8 13.9 73 22.1 98.1 19.6 56.0\n3 female Tennis 4.38 7.9 39.8 13.5 88 21.2 80.6 17.1 46.5\n4 female Tennis 4.08 6.6 37.8 12.1 182 20.5 68.3 15.3 51.8\n5 female Tennis 4.98 6.4 44.8 14.8 80 17.1 47.6 11.1 42.2\n6 female Tennis 4.66 6.4 40.9 13.9 109 18.4 38.2 8.45 41.9\n7 male Tennis 4.97 8.8 43 14.9 233 22.3 60.4 11.5 63 \n# i 2 more variables: Ht , Wt \n```\n:::\n:::\n\n\n\n\n## Either/Or\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% filter(Sport == \"Tennis\" | RCC > 5)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 66 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM\n \n 1 female Row 5.02 6.4 44.8 15.2 48 19.8 91 19.2 53.6\n 2 female T400m 5.31 9.5 47.1 15.9 29 21.4 57.9 11.1 57.5\n 3 female Field 5.33 9.3 47 15 62 25.3 103. 19.5 59.9\n 4 female TSprnt 5.16 8.2 45.3 14.7 34 20.3 46.1 10.2 51.5\n 5 female Tennis 4 4.2 36.6 12 57 25.4 109 20.9 56.6\n 6 female Tennis 4.4 4 40.8 13.9 73 22.1 98.1 19.6 56.0\n 7 female Tennis 4.38 7.9 39.8 13.5 88 21.2 80.6 17.1 46.5\n 8 female Tennis 4.08 6.6 37.8 12.1 182 20.5 68.3 15.3 51.8\n 9 female Tennis 4.98 6.4 44.8 14.8 80 17.1 47.6 11.1 42.2\n10 female Tennis 5.16 7.2 44.3 14.5 88 18.3 61.9 12.9 48.8\n# i 56 more rows\n# i 2 more variables: Ht , Wt \n```\n:::\n:::\n\n\n\n## Sorting into order\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% arrange(RCC)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM\n \n 1 female Netba~ 3.8 6.6 36.5 12.4 102 24.4 157. 26.6 54.4\n 2 female Netba~ 3.9 6.3 35.9 12.1 78 20.1 70 15.0 57.3\n 3 female T400m 3.9 6 38.9 13.5 16 19.4 48.4 10.5 53.7\n 4 female Row 3.91 7.3 37.6 12.9 43 22.3 126. 25.2 54.8\n 5 female Netba~ 3.95 6.6 38.4 12.8 33 19.9 68.9 15.6 54.1\n 6 female Row 3.95 3.3 36.9 12.5 40 24.5 74.9 16.4 63.0\n 7 female Netba~ 3.96 5.5 36.3 12.4 71 22.6 101. 17.9 56.0\n 8 female BBall 3.96 7.5 37.5 12.3 60 20.6 109. 19.8 63.3\n 9 female Tennis 4 4.2 36.6 12 57 25.4 109 20.9 56.6\n10 female Netba~ 4.02 9.1 37.7 12.7 107 23.0 77 18.1 57.3\n# i 192 more rows\n# i 2 more variables: Ht , Wt \n```\n:::\n:::\n\n\n\n## Breaking ties by another variable\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% arrange(RCC, BMI)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM\n \n 1 female Netba~ 3.8 6.6 36.5 12.4 102 24.4 157. 26.6 54.4\n 2 female T400m 3.9 6 38.9 13.5 16 19.4 48.4 10.5 53.7\n 3 female Netba~ 3.9 6.3 35.9 12.1 78 20.1 70 15.0 57.3\n 4 female Row 3.91 7.3 37.6 12.9 43 22.3 126. 25.2 54.8\n 5 female Netba~ 3.95 6.6 38.4 12.8 33 19.9 68.9 15.6 54.1\n 6 female Row 3.95 3.3 36.9 12.5 40 24.5 74.9 16.4 63.0\n 7 female BBall 3.96 7.5 37.5 12.3 60 20.6 109. 19.8 63.3\n 8 female Netba~ 3.96 5.5 36.3 12.4 71 22.6 101. 17.9 56.0\n 9 female Tennis 4 4.2 36.6 12 57 25.4 109 20.9 56.6\n10 female Netba~ 4.02 9.1 37.7 12.7 107 23.0 77 18.1 57.3\n# i 192 more rows\n# i 2 more variables: Ht , Wt \n```\n:::\n:::\n\n\n\n## Descending order\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% arrange(desc(BMI))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 13\n Sex Sport RCC WCC Hc Hg Ferr BMI SSF `%Bfat` LBM\n \n 1 male Field 5.48 6.2 48.2 16.3 94 34.4 82.7 13.9 106 \n 2 male Field 4.96 8.3 45.3 15.7 141 33.7 114. 17.4 89 \n 3 male Field 5.48 4.6 49.4 18 132 32.5 55.7 8.51 102 \n 4 female Field 4.75 7.5 43.8 15.2 90 31.9 132. 23.0 73.0\n 5 male Field 5.01 8.9 46 15.9 212 30.2 112. 19.9 78 \n 6 male Field 5.01 8.9 46 15.9 212 30.2 96.9 18.1 80 \n 7 male Field 5.09 8.9 46.3 15.4 44 30.0 71.1 14.0 88 \n 8 female Field 4.58 5.8 42.1 14.7 164 28.6 110. 21.3 68.9\n 9 female Field 4.51 9 39.7 14.3 36 28.1 136. 24.9 63.0\n10 male WPolo 5.34 6.2 49.8 17.2 143 27.8 75.7 13.5 82 \n# i 192 more rows\n# i 2 more variables: Ht , Wt \n```\n:::\n:::\n\n\n\n## “The top ones”\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>%\n arrange(desc(Wt)) %>%\n slice(1:7) %>%\n select(Sport, Wt)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 7 x 2\n Sport Wt\n \n1 Field 123.\n2 BBall 114.\n3 Field 111.\n4 Field 108.\n5 Field 103.\n6 WPolo 101 \n7 BBall 100.\n```\n:::\n:::\n\n\n\n## Another way\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% \n slice_max(order_by = Wt, n=7) %>% \n select(Sport, Wt)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 7 x 2\n Sport Wt\n \n1 Field 123.\n2 BBall 114.\n3 Field 111.\n4 Field 108.\n5 Field 103.\n6 WPolo 101 \n7 BBall 100.\n```\n:::\n:::\n\n\n\n\n## Create new variables from old ones\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>%\n mutate(wt_lb = Wt * 2.2) %>%\n select(Sport, Sex, Wt, wt_lb) %>% \n arrange(Wt)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 202 x 4\n Sport Sex Wt wt_lb\n \n 1 Gym female 37.8 83.2\n 2 Gym female 43.8 96.4\n 3 Gym female 45.1 99.2\n 4 Tennis female 45.8 101. \n 5 Tennis female 47.4 104. \n 6 Gym female 47.8 105. \n 7 T400m female 49.2 108. \n 8 Row female 49.8 110. \n 9 T400m female 50.9 112. \n10 Netball female 51.9 114. \n# i 192 more rows\n```\n:::\n:::\n\n\n\n## Turning the result into a number\nOutput is always data frame unless you explicitly turn it into something\nelse, eg. the weight of the heaviest athlete, as a number:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% arrange(desc(Wt)) %>% pluck(\"Wt\", 1)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 123.2\n```\n:::\n:::\n\n\n\nOr the 20 heaviest weights in descending order:\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>%\n arrange(desc(Wt)) %>%\n slice(1:20) %>%\n pluck(\"Wt\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 123.20 113.70 111.30 108.20 102.70 101.00 100.20 98.00 97.90\n[10] 97.90 97.00 96.90 96.30 94.80 94.80 94.70 94.70 94.60\n[19] 94.25 94.20\n```\n:::\n:::\n\n\n\n## Another way to do the last one\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>%\n arrange(desc(Wt)) %>%\n slice(1:20) %>%\n pull(\"Wt\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n [1] 123.20 113.70 111.30 108.20 102.70 101.00 100.20 98.00 97.90\n[10] 97.90 97.00 96.90 96.30 94.80 94.80 94.70 94.70 94.60\n[19] 94.25 94.20\n```\n:::\n:::\n\n\n\n`pull` grabs the column you name *as a vector* (of whatever it contains).\n\n## To find the mean height of the women athletes\nTwo ways:\n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>% group_by(Sex) %>% summarize(m = mean(Ht))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 2 x 2\n Sex m\n \n1 female 175.\n2 male 186.\n```\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nathletes %>%\n filter(Sex == \"female\") %>%\n summarize(m = mean(Ht))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 1 x 1\n m\n \n1 175.\n```\n:::\n:::\n\n\n\n\\normalsize\n\n## Summary of data selection/arrangement \"verbs\" {.smaller}\n\n | Verb | Purpose|\n |:-----|:-------------------------------|\n |`select` | Choose columns|\n |`slice` | Choose rows by number|\n |`slice_sample` | Choose random rows |\n |`slice_max` | Choose rows with largest values on a variable (also `slice_min`) |\n |`filter` | Choose rows satisfying conditions|\n | `arrange` | Sort in order by column(s) |\n | `mutate` | Create new variables | \n | `group_by` | Create groups to work with|\n |`summarize` | Calculate summary statistics (by groups if defined) |\n | `pluck` | Extract items from data frame |\n | `pull` | Extract a single column from a data frame as a vector|\n\n \n## Looking things up in another data frame\n\n- Suppose you are working in the nails department of a hardware store and you find that you have sold these items:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \"http://ritsokiguess.site/datafiles/nail_sales.csv\"\nsales <- read_csv(my_url)\nsales\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 6 x 2\n product_code sales\n \n1 061-5344-6 10\n2 161-0090-0 6\n3 061-5388-2 2\n4 161-0199-4 8\n5 061-5375-2 5\n6 061-4525-2 3\n```\n:::\n:::\n\n\n\n\n## Product descriptions and prices\n\n- but you don't remember what these product codes are, and you would like to know the total revenue from these sales.\n\n- Fortunately you found a list of product descriptions and prices:\n\n\\small\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_url <- \"http://ritsokiguess.site/datafiles/nail_desc.csv\"\ndesc <- read_csv(my_url)\ndesc\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 7 x 5\n product_code description size qty price\n \n1 061-4525-2 spike nail \"10\\\"\" 1 1.49\n2 061-5329-4 masonry nail \"1.5\\\"\" 112 8.19\n3 061-5344-6 finishing nail \"1\\\"\" 1298 6.99\n4 061-5375-2 roofing nail \"1.25\\\"\" 192 6.99\n5 061-5388-2 framing nail \"4\\\"\" 25 8.19\n6 161-0090-0 wood nail \"1\\\"\" 25 2.39\n7 161-0199-4 panel nail \"1-5/8\\\"\" 20 4.69\n```\n:::\n:::\n\n\n\n- the `size` values are measured in inches (symbol `\"`), but R uses the same symbol for the start and end of text, so the `\"` representing \"inches\" is \"escaped\". Hence the odd look.\n\n\\normalsize\n\n## The lookup\n\n- How do you \"look up\" the product codes to find the product descriptions and prices?\n- `left_join`.\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales %>% left_join(desc)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 6 x 6\n product_code sales description size qty price\n \n1 061-5344-6 10 finishing nail \"1\\\"\" 1298 6.99\n2 161-0090-0 6 wood nail \"1\\\"\" 25 2.39\n3 061-5388-2 2 framing nail \"4\\\"\" 25 8.19\n4 161-0199-4 8 panel nail \"1-5/8\\\"\" 20 4.69\n5 061-5375-2 5 roofing nail \"1.25\\\"\" 192 6.99\n6 061-4525-2 3 spike nail \"10\\\"\" 1 1.49\n```\n:::\n:::\n\n\n\n## What we have\n\n- this looks up all the rows in the *first* dataframe that are also in the *second*. \n- by default matches all columns with same name in two dataframes (`product_code` here)\n- get *all* columns in *both* dataframes. The rows are the ones for that `product_code`.\n\nSo now can work out how much the total revenue was:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales %>% left_join(desc) %>% \n mutate(product_revenue = sales*price) %>% \n summarize(total_revenue = sum(product_revenue))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 1 x 1\n total_revenue\n \n1 178.\n```\n:::\n:::\n\n\n\n\n## More comments\n\n- if any product codes are not matched, you get NA in the added columns\n- anything in the *second* dataframe that was not in the first does not appear (here, any products that were not sold)\n- other variations (examples follow):\n - if there are two columns with the same name in the two dataframes, and you only want to match on one, use `by` with one column name\n - if the columns you want to look up have different names in the two dataframes, use `by` with a \"named list\"\n\n## Matching on only some matching names\n\n- Suppose the `sales` dataframe *also* had a column `qty` (which was the quantity sold): \n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales %>% rename(\"qty\"=\"sales\") -> sales1\nsales1\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 6 x 2\n product_code qty\n \n1 061-5344-6 10\n2 161-0090-0 6\n3 061-5388-2 2\n4 161-0199-4 8\n5 061-5375-2 5\n6 061-4525-2 3\n```\n:::\n:::\n\n\n\\normalsize\n\n- The `qty` in `sales1` is the quantity sold, but the `qty` in `desc` is the number of nails in a package. These should *not* be matched: they are different things.\n\n## Matching only on product code\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales1 %>% \n left_join(desc, join_by(product_code))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 6 x 6\n product_code qty.x description size qty.y price\n \n1 061-5344-6 10 finishing nail \"1\\\"\" 1298 6.99\n2 161-0090-0 6 wood nail \"1\\\"\" 25 2.39\n3 061-5388-2 2 framing nail \"4\\\"\" 25 8.19\n4 161-0199-4 8 panel nail \"1-5/8\\\"\" 20 4.69\n5 061-5375-2 5 roofing nail \"1.25\\\"\" 192 6.99\n6 061-4525-2 3 spike nail \"10\\\"\" 1 1.49\n```\n:::\n:::\n\n\n\n- Get `qty.x` (from `sales1`) and `qty.y` (from `desc`).\n\n## Matching on different names 1/2\n\n- Suppose the product code in `sales` was just `code`:\n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales %>% rename(\"code\" = \"product_code\") -> sales2\nsales2\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 6 x 2\n code sales\n \n1 061-5344-6 10\n2 161-0090-0 6\n3 061-5388-2 2\n4 161-0199-4 8\n5 061-5375-2 5\n6 061-4525-2 3\n```\n:::\n:::\n\n\n\\normalsize\n\n- How to match the two product codes that have different names?\n\n## Matching on different names 2/2\n\n- Use `by`, but like this:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales2 %>% \n left_join(desc, join_by(code == product_code))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 6 x 6\n code sales description size qty price\n \n1 061-5344-6 10 finishing nail \"1\\\"\" 1298 6.99\n2 161-0090-0 6 wood nail \"1\\\"\" 25 2.39\n3 061-5388-2 2 framing nail \"4\\\"\" 25 8.19\n4 161-0199-4 8 panel nail \"1-5/8\\\"\" 20 4.69\n5 061-5375-2 5 roofing nail \"1.25\\\"\" 192 6.99\n6 061-4525-2 3 spike nail \"10\\\"\" 1 1.49\n```\n:::\n:::\n\n\n\n## Other types of join\n\n- `right_join`: interchanges roles, looking up keys from second dataframe in first.\n- `anti_join`: give me all the rows in the first dataframe that are *not* in the second. (Use this eg. to see whether the product descriptions are incomplete.)\n- `full_join`: give me all the rows in both dataframes, with missings as needed.\n\n## Full join here\n\n\\small\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsales %>% full_join(desc)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 7 x 6\n product_code sales description size qty price\n \n1 061-5344-6 10 finishing nail \"1\\\"\" 1298 6.99\n2 161-0090-0 6 wood nail \"1\\\"\" 25 2.39\n3 061-5388-2 2 framing nail \"4\\\"\" 25 8.19\n4 161-0199-4 8 panel nail \"1-5/8\\\"\" 20 4.69\n5 061-5375-2 5 roofing nail \"1.25\\\"\" 192 6.99\n6 061-4525-2 3 spike nail \"10\\\"\" 1 1.49\n7 061-5329-4 NA masonry nail \"1.5\\\"\" 112 8.19\n```\n:::\n:::\n\n\n\\normalsize\n\n- The missing `sales` for \"masonry nail\" says that it was in the lookup table `desc`, but we didn't sell any.\n\n\n## The same thing, but with `anti_join`\n\nAnything in first df but not in second?\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndesc %>% 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"\f890"; } -.bi-house-gear::before { content: "\f891"; } -.bi-house-lock-fill::before { content: "\f892"; } -.bi-house-lock::before { content: "\f893"; } -.bi-house-slash-fill::before { content: "\f894"; } -.bi-house-slash::before { content: "\f895"; } -.bi-house-up-fill::before { content: "\f896"; } -.bi-house-up::before { content: "\f897"; } -.bi-house-x-fill::before { content: "\f898"; } -.bi-house-x::before { content: "\f899"; } -.bi-person-add::before { content: "\f89a"; } -.bi-person-down::before { content: "\f89b"; } -.bi-person-exclamation::before { content: "\f89c"; } -.bi-person-fill-add::before { content: "\f89d"; } -.bi-person-fill-check::before { content: "\f89e"; } -.bi-person-fill-dash::before { content: "\f89f"; } -.bi-person-fill-down::before { content: "\f8a0"; } -.bi-person-fill-exclamation::before { content: "\f8a1"; } -.bi-person-fill-gear::before { content: "\f8a2"; } -.bi-person-fill-lock::before { content: "\f8a3"; } -.bi-person-fill-slash::before { content: 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0===i&&(i={x:0,y:0}),{top:t.top-e.height-i.y,right:t.right-e.width+i.x,bottom:t.bottom-e.height+i.y,left:t.left-e.width-i.x}}function Se(t){return[mt,_t,gt,bt].some((function(e){return t[e]>=0}))}const Ne={name:"hide",enabled:!0,phase:"main",requiresIfExists:["preventOverflow"],fn:function(t){var e=t.state,i=t.name,n=e.rects.reference,s=e.rects.popper,o=e.modifiersData.preventOverflow,r=ke(e,{elementContext:"reference"}),a=ke(e,{altBoundary:!0}),l=De(r,n),c=De(a,s,o),h=Se(l),d=Se(c);e.modifiersData[i]={referenceClippingOffsets:l,popperEscapeOffsets:c,isReferenceHidden:h,hasPopperEscaped:d},e.attributes.popper=Object.assign({},e.attributes.popper,{"data-popper-reference-hidden":h,"data-popper-escaped":d})}},Ie={name:"offset",enabled:!0,phase:"main",requires:["popperOffsets"],fn:function(t){var e=t.state,i=t.options,n=t.name,s=i.offset,o=void 0===s?[0,0]:s,r=Lt.reduce((function(t,i){return t[i]=function(t,e,i){var n=Ut(t),s=[bt,mt].indexOf(n)>=0?-1:1,o="function"==typeof 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0===p?0:p,g=ke(e,{boundary:l,rootBoundary:c,padding:d,altBoundary:h}),_=Ut(e.placement),b=ce(e.placement),v=!b,y=ee(_),w="x"===y?"y":"x",E=e.modifiersData.popperOffsets,A=e.rects.reference,T=e.rects.popper,O="function"==typeof m?m(Object.assign({},e.rects,{placement:e.placement})):m,C={x:0,y:0};if(E){if(o||a){var k="y"===y?mt:bt,L="y"===y?gt:_t,x="y"===y?"height":"width",D=E[y],S=E[y]+g[k],N=E[y]-g[L],I=f?-T[x]/2:0,P=b===wt?A[x]:T[x],j=b===wt?-T[x]:-A[x],M=e.elements.arrow,H=f&&M?Kt(M):{width:0,height:0},B=e.modifiersData["arrow#persistent"]?e.modifiersData["arrow#persistent"].padding:{top:0,right:0,bottom:0,left:0},R=B[k],W=B[L],$=oe(0,A[x],H[x]),z=v?A[x]/2-I-$-R-O:P-$-R-O,q=v?-A[x]/2+I+$+W+O:j+$+W+O,F=e.elements.arrow&&te(e.elements.arrow),U=F?"y"===y?F.clientTop||0:F.clientLeft||0:0,V=e.modifiersData.offset?e.modifiersData.offset[e.placement][y]:0,K=E[y]+z-V-U,X=E[y]+q-V;if(o){var Y=oe(f?ne(S,K):S,D,f?ie(N,X):N);E[y]=Y,C[y]=Y-D}if(a){var Q="x"===y?mt:bt,G="x"===y?gt:_t,Z=E[w],J=Z+g[Q],tt=Z-g[G],et=oe(f?ne(J,K):J,Z,f?ie(tt,X):tt);E[w]=et,C[w]=et-Z}}e.modifiersData[n]=C}},requiresIfExists:["offset"]};function Me(t,e,i){void 0===i&&(i=!1);var n=zt(e);zt(e)&&function(t){var e=t.getBoundingClientRect();e.width,t.offsetWidth,e.height,t.offsetHeight}(e);var s,o,r=Gt(e),a=Vt(t),l={scrollLeft:0,scrollTop:0},c={x:0,y:0};return(n||!n&&!i)&&(("body"!==Rt(e)||we(r))&&(l=(s=e)!==Wt(s)&&zt(s)?{scrollLeft:(o=s).scrollLeft,scrollTop:o.scrollTop}:ve(s)),zt(e)?((c=Vt(e)).x+=e.clientLeft,c.y+=e.clientTop):r&&(c.x=ye(r))),{x:a.left+l.scrollLeft-c.x,y:a.top+l.scrollTop-c.y,width:a.width,height:a.height}}function He(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var Be={placement:"bottom",modifiers:[],strategy:"absolute"};function Re(){for(var t=arguments.length,e=new Array(t),i=0;ij.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(t){if(void 0===Fe)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let e=this._element;"parent"===this._config.reference?e=t:o(this._config.reference)?e=r(this._config.reference):"object"==typeof this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;ie+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'

      ',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" ").forEach((t=>{if("click"===t)j.on(this._element,this.constructor.Event.CLICK,this._config.selector,(t=>this.toggle(t)));else if("manual"!==t){const e=t===hn?this.constructor.Event.MOUSEENTER:this.constructor.Event.FOCUSIN,i=t===hn?this.constructor.Event.MOUSELEAVE:this.constructor.Event.FOCUSOUT;j.on(this._element,e,this._config.selector,(t=>this._enter(t))),j.on(this._element,i,this._config.selector,(t=>this._leave(t)))}})),this._hideModalHandler=()=>{this._element&&this.hide()},j.on(this._element.closest(ln),cn,this._hideModalHandler),this._config.selector?this._config={...this._config,trigger:"manual",selector:""}:this._fixTitle()}_fixTitle(){const t=this._element.getAttribute("title"),e=typeof this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=U.getDataAttributes(this._element);return Object.keys(e).forEach((t=>{Gi.has(t)&&delete e[t]})),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),a(Qi,t,this.constructor.DefaultType),t.sanitize&&(t.template=Yi(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new RegExp(`(^|\\s)${this._getBasicClassPrefix()}\\S+`,"g"),i=t.getAttribute("class").match(e);null!==i&&i.length>0&&i.map((t=>t.trim())).forEach((e=>t.classList.remove(e)))}_getBasicClassPrefix(){return"bs-tooltip"}_handlePopperPlacementChange(t){const{state:e}=t;e&&(this.tip=e.elements.popper,this._cleanTipClass(),this._addAttachmentClass(this._getAttachment(e.placement)))}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=un.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(un);const fn={...un.Default,placement:"right",offset:[0,8],trigger:"click",content:"",template:''},pn={...un.DefaultType,content:"(string|element|function)"},mn={HIDE:"hide.bs.popover",HIDDEN:"hidden.bs.popover",SHOW:"show.bs.popover",SHOWN:"shown.bs.popover",INSERTED:"inserted.bs.popover",CLICK:"click.bs.popover",FOCUSIN:"focusin.bs.popover",FOCUSOUT:"focusout.bs.popover",MOUSEENTER:"mouseenter.bs.popover",MOUSELEAVE:"mouseleave.bs.popover"};class gn extends un{static get Default(){return fn}static get NAME(){return"popover"}static get Event(){return mn}static get DefaultType(){return pn}isWithContent(){return this.getTitle()||this._getContent()}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),".popover-header"),this._sanitizeAndSetContent(t,this._getContent(),".popover-body")}_getContent(){return this._resolvePossibleFunction(this._config.content)}_getBasicClassPrefix(){return"bs-popover"}static jQueryInterface(t){return this.each((function(){const e=gn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(gn);const _n="scrollspy",bn={offset:10,method:"auto",target:""},vn={offset:"number",method:"string",target:"(string|element)"},yn="active",wn=".nav-link, .list-group-item, .dropdown-item",En="position";class An extends B{constructor(t,e){super(t),this._scrollElement="BODY"===this._element.tagName?window:this._element,this._config=this._getConfig(e),this._offsets=[],this._targets=[],this._activeTarget=null,this._scrollHeight=0,j.on(this._scrollElement,"scroll.bs.scrollspy",(()=>this._process())),this.refresh(),this._process()}static get Default(){return bn}static get NAME(){return _n}refresh(){const t=this._scrollElement===this._scrollElement.window?"offset":En,e="auto"===this._config.method?t:this._config.method,n=e===En?this._getScrollTop():0;this._offsets=[],this._targets=[],this._scrollHeight=this._getScrollHeight(),V.find(wn,this._config.target).map((t=>{const s=i(t),o=s?V.findOne(s):null;if(o){const t=o.getBoundingClientRect();if(t.width||t.height)return[U[e](o).top+n,s]}return null})).filter((t=>t)).sort(((t,e)=>t[0]-e[0])).forEach((t=>{this._offsets.push(t[0]),this._targets.push(t[1])}))}dispose(){j.off(this._scrollElement,".bs.scrollspy"),super.dispose()}_getConfig(t){return(t={...bn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}}).target=r(t.target)||document.documentElement,a(_n,t,vn),t}_getScrollTop(){return this._scrollElement===window?this._scrollElement.pageYOffset:this._scrollElement.scrollTop}_getScrollHeight(){return this._scrollElement.scrollHeight||Math.max(document.body.scrollHeight,document.documentElement.scrollHeight)}_getOffsetHeight(){return this._scrollElement===window?window.innerHeight:this._scrollElement.getBoundingClientRect().height}_process(){const t=this._getScrollTop()+this._config.offset,e=this._getScrollHeight(),i=this._config.offset+e-this._getOffsetHeight();if(this._scrollHeight!==e&&this.refresh(),t>=i){const t=this._targets[this._targets.length-1];this._activeTarget!==t&&this._activate(t)}else{if(this._activeTarget&&t0)return this._activeTarget=null,void this._clear();for(let e=this._offsets.length;e--;)this._activeTarget!==this._targets[e]&&t>=this._offsets[e]&&(void 0===this._offsets[e+1]||t`${e}[data-bs-target="${t}"],${e}[href="${t}"]`)),i=V.findOne(e.join(","),this._config.target);i.classList.add(yn),i.classList.contains("dropdown-item")?V.findOne(".dropdown-toggle",i.closest(".dropdown")).classList.add(yn):V.parents(i,".nav, .list-group").forEach((t=>{V.prev(t,".nav-link, .list-group-item").forEach((t=>t.classList.add(yn))),V.prev(t,".nav-item").forEach((t=>{V.children(t,".nav-link").forEach((t=>t.classList.add(yn)))}))})),j.trigger(this._scrollElement,"activate.bs.scrollspy",{relatedTarget:t})}_clear(){V.find(wn,this._config.target).filter((t=>t.classList.contains(yn))).forEach((t=>t.classList.remove(yn)))}static jQueryInterface(t){return this.each((function(){const e=An.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}j.on(window,"load.bs.scrollspy.data-api",(()=>{V.find('[data-bs-spy="scroll"]').forEach((t=>new An(t)))})),g(An);const Tn="active",On="fade",Cn="show",kn=".active",Ln=":scope > li > .active";class xn extends B{static get NAME(){return"tab"}show(){if(this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE&&this._element.classList.contains(Tn))return;let t;const e=n(this._element),i=this._element.closest(".nav, .list-group");if(i){const e="UL"===i.nodeName||"OL"===i.nodeName?Ln:kn;t=V.find(e,i),t=t[t.length-1]}const s=t?j.trigger(t,"hide.bs.tab",{relatedTarget:this._element}):null;if(j.trigger(this._element,"show.bs.tab",{relatedTarget:t}).defaultPrevented||null!==s&&s.defaultPrevented)return;this._activate(this._element,i);const o=()=>{j.trigger(t,"hidden.bs.tab",{relatedTarget:this._element}),j.trigger(this._element,"shown.bs.tab",{relatedTarget:t})};e?this._activate(e,e.parentNode,o):o()}_activate(t,e,i){const n=(!e||"UL"!==e.nodeName&&"OL"!==e.nodeName?V.children(e,kn):V.find(Ln,e))[0],s=i&&n&&n.classList.contains(On),o=()=>this._transitionComplete(t,n,i);n&&s?(n.classList.remove(Cn),this._queueCallback(o,t,!0)):o()}_transitionComplete(t,e,i){if(e){e.classList.remove(Tn);const t=V.findOne(":scope > .dropdown-menu .active",e.parentNode);t&&t.classList.remove(Tn),"tab"===e.getAttribute("role")&&e.setAttribute("aria-selected",!1)}t.classList.add(Tn),"tab"===t.getAttribute("role")&&t.setAttribute("aria-selected",!0),u(t),t.classList.contains(On)&&t.classList.add(Cn);let n=t.parentNode;if(n&&"LI"===n.nodeName&&(n=n.parentNode),n&&n.classList.contains("dropdown-menu")){const e=t.closest(".dropdown");e&&V.find(".dropdown-toggle",e).forEach((t=>t.classList.add(Tn))),t.setAttribute("aria-expanded",!0)}i&&i()}static jQueryInterface(t){return this.each((function(){const e=xn.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}j.on(document,"click.bs.tab.data-api",'[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this)||xn.getOrCreateInstance(this).show()})),g(xn);const Dn="toast",Sn="hide",Nn="show",In="showing",Pn={animation:"boolean",autohide:"boolean",delay:"number"},jn={animation:!0,autohide:!0,delay:5e3};class Mn extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get DefaultType(){return Pn}static get Default(){return jn}static get NAME(){return Dn}show(){j.trigger(this._element,"show.bs.toast").defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(Sn),u(this._element),this._element.classList.add(Nn),this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.remove(In),j.trigger(this._element,"shown.bs.toast"),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this._element.classList.contains(Nn)&&(j.trigger(this._element,"hide.bs.toast").defaultPrevented||(this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.add(Sn),this._element.classList.remove(In),this._element.classList.remove(Nn),j.trigger(this._element,"hidden.bs.toast")}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this._element.classList.contains(Nn)&&this._element.classList.remove(Nn),super.dispose()}_getConfig(t){return t={...jn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}},a(Dn,t,this.constructor.DefaultType),t}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){j.on(this._element,"mouseover.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"mouseout.bs.toast",(t=>this._onInteraction(t,!1))),j.on(this._element,"focusin.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"focusout.bs.toast",(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=Mn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}return R(Mn),g(Mn),{Alert:W,Button:z,Carousel:st,Collapse:pt,Dropdown:hi,Modal:Hi,Offcanvas:Fi,Popover:gn,ScrollSpy:An,Tab:xn,Toast:Mn,Tooltip:un}})); -//# sourceMappingURL=bootstrap.bundle.min.js.map \ No newline at end of file diff --git a/choosing_files/libs/clipboard/clipboard.min.js b/choosing_files/libs/clipboard/clipboard.min.js deleted file mode 100644 index 1103f81..0000000 --- a/choosing_files/libs/clipboard/clipboard.min.js +++ /dev/null @@ -1,7 +0,0 @@ -/*! - * clipboard.js v2.0.11 - * https://clipboardjs.com/ - * - * Licensed MIT © Zeno Rocha - */ -!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return b}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),r=n.n(e);function c(t){try{return document.execCommand(t)}catch(t){return}}var a=function(t){t=r()(t);return c("cut"),t};function o(t,e){var n,o,t=(n=t,o="rtl"===document.documentElement.getAttribute("dir"),(t=document.createElement("textarea")).style.fontSize="12pt",t.style.border="0",t.style.padding="0",t.style.margin="0",t.style.position="absolute",t.style[o?"right":"left"]="-9999px",o=window.pageYOffset||document.documentElement.scrollTop,t.style.top="".concat(o,"px"),t.setAttribute("readonly",""),t.value=n,t);return e.container.appendChild(t),e=r()(t),c("copy"),t.remove(),e}var f=function(t){var e=1>> 0; - - // 4. 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Call the Call internal method of callback with T as - // the this value and argument list containing kValue, k, and O. - callback.call(T, kValue, k, O); - } - // d. Increase k by 1. - k++; - } - // 8. return undefined - }; -} - -// Production steps of ECMA-262, Edition 5, 15.4.4.19 -// Reference: http://es5.github.io/#x15.4.4.19 -if (!Array.prototype.map) { - - Array.prototype.map = function(callback, thisArg) { - - var T, A, k; - - if (this == null) { - throw new TypeError(' this is null or not defined'); - } - - // 1. Let O be the result of calling ToObject passing the |this| - // value as the argument. - var O = Object(this); - - // 2. Let lenValue be the result of calling the Get internal - // method of O with the argument "length". - // 3. Let len be ToUint32(lenValue). - var len = O.length >>> 0; - - // 4. 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Increase k by 1. - k++; - } - - // 9. return A - return A; - }; -} - -var PagedTable = function (pagedTable) { - var me = this; - - var source = function(pagedTable) { - var sourceElems = [].slice.call(pagedTable.children).filter(function(e) { - return e.hasAttribute("data-pagedtable-source"); - }); - - if (sourceElems === null || sourceElems.length !== 1) { - throw("A single data-pagedtable-source was not found"); - } - - return JSON.parse(sourceElems[0].innerHTML); - }(pagedTable); - - var options = function(source) { - var options = typeof(source.options) !== "undefined" && - source.options !== null ? source.options : {}; - - var columns = typeof(options.columns) !== "undefined" ? options.columns : {}; - var rows = typeof(options.rows) !== "undefined" ? options.rows : {}; - - var positiveIntOrNull = function(value) { - return parseInt(value) >= 0 ? parseInt(value) : null; - }; - - return { - pages: positiveIntOrNull(options.pages), - rows: { - min: positiveIntOrNull(rows.min), - max: positiveIntOrNull(rows.max), - total: positiveIntOrNull(rows.total) - }, - columns: { - min: positiveIntOrNull(columns.min), - max: positiveIntOrNull(columns.max), - total: positiveIntOrNull(columns.total) - } - }; - }(source); - - var Measurer = function() { - - // set some default initial values that will get adjusted in runtime - me.measures = { - padding: 12, - character: 8, - height: 15, - defaults: true - }; - - me.calculate = function(measuresCell) { - if (!me.measures.defaults) - return; - - var measuresCellStyle = window.getComputedStyle(measuresCell, null); - - var newPadding = parsePadding(measuresCellStyle.paddingLeft) + - parsePadding(measuresCellStyle.paddingRight); - - var sampleString = "ABCDEFGHIJ0123456789"; - var newCharacter = Math.ceil(measuresCell.clientWidth / sampleString.length); - - if (newPadding <= 0 || newCharacter <= 0) - return; - - me.measures.padding = newPadding; - me.measures.character = newCharacter; - me.measures.height = measuresCell.clientHeight; - me.measures.defaults = false; - }; - - return me; - }; - - var Page = function(data, options) { - var me = this; - - var defaults = { - max: 7, - rows: 10 - }; - - var totalPages = function() { - return Math.ceil(data.length / me.rows); - }; - - me.number = 0; - me.max = options.pages !== null ? options.pages : defaults.max; - me.visible = me.max; - me.rows = options.rows.min !== null ? options.rows.min : defaults.rows; - me.total = totalPages(); - - me.setRows = function(newRows) { - me.rows = newRows; - me.total = totalPages(); - }; - - me.setPageNumber = function(newPageNumber) { - if (newPageNumber < 0) newPageNumber = 0; - if (newPageNumber >= me.total) newPageNumber = me.total - 1; - - me.number = newPageNumber; - }; - - me.setVisiblePages = function(visiblePages) { - me.visible = Math.min(me.max, visiblePages); - me.setPageNumber(me.number); - }; - - me.getVisiblePageRange = function() { - var start = me.number - Math.max(Math.floor((me.visible - 1) / 2), 0); - var end = me.number + Math.floor(me.visible / 2) + 1; - var pageCount = me.total; - - if (start < 0) { - var diffToStart = 0 - start; - start += diffToStart; - end += diffToStart; - } - - if (end > pageCount) { - var diffToEnd = end - pageCount; - start -= diffToEnd; - end -= diffToEnd; - } - - start = start < 0 ? 0 : start; - end = end >= pageCount ? pageCount : end; - - var first = false; - var last = false; - - if (start > 0 && me.visible > 1) { - start = start + 1; - first = true; - } - - if (end < pageCount && me.visible > 2) { - end = end - 1; - last = true; - } - - return { - first: first, - start: start, - end: end, - last: last - }; - }; - - me.getRowStart = function() { - var rowStart = page.number * page.rows; - if (rowStart < 0) - rowStart = 0; - - return rowStart; - }; - - me.getRowEnd = function() { - var rowStart = me.getRowStart(); - return Math.min(rowStart + me.rows, data.length); - }; - - me.getPaddingRows = function() { - var rowStart = me.getRowStart(); - var rowEnd = me.getRowEnd(); - return data.length > me.rows ? me.rows - (rowEnd - rowStart) : 0; - }; - }; - - var Columns = function(data, columns, options) { - var me = this; - - me.defaults = { - min: 5 - }; - - me.number = 0; - me.visible = 0; - me.total = columns.length; - me.subset = []; - me.padding = 0; - me.min = options.columns.min !== null ? options.columns.min : me.defaults.min; - me.max = options.columns.max !== null ? options.columns.max : null; - me.widths = {}; - - var widthsLookAhead = Math.max(100, options.rows.min); - var paddingColChars = 10; - - me.emptyNames = function() { - columns.forEach(function(column) { - if (columns.label !== null && columns.label !== "") - return false; - }); - - return true; - }; - - var parsePadding = function(value) { - return parseInt(value) >= 0 ? parseInt(value) : 0; - }; - - me.calculateWidths = function(measures) { - columns.forEach(function(column) { - var maxChars = Math.max( - column.label.toString().length, - column.type.toString().length - ); - - for (var idxRow = 0; idxRow < Math.min(widthsLookAhead, data.length); idxRow++) { - maxChars = Math.max(maxChars, data[idxRow][column.name.toString()].length); - } - - me.widths[column.name] = { - // width in characters - chars: maxChars, - // width for the inner html columns - inner: maxChars * measures.character, - // width adding outer styles like padding - outer: maxChars * measures.character + measures.padding - }; - }); - }; - - me.getWidth = function() { - var widthOuter = 0; - for (var idxCol = 0; idxCol < me.subset.length; idxCol++) { - var columnName = me.subset[idxCol].name; - widthOuter = widthOuter + me.widths[columnName].outer; - } - - widthOuter = widthOuter + me.padding * paddingColChars * measurer.measures.character; - - if (me.hasMoreLeftColumns()) { - widthOuter = widthOuter + columnNavigationWidthPX + measurer.measures.padding; - } - - if (me.hasMoreRightColumns()) { - widthOuter = widthOuter + columnNavigationWidthPX + measurer.measures.padding; - } - - return widthOuter; - }; - - me.updateSlice = function() { - if (me.number + me.visible >= me.total) - me.number = me.total - me.visible; - - if (me.number < 0) me.number = 0; - - me.subset = columns.slice(me.number, Math.min(me.number + me.visible, me.total)); - - me.subset = me.subset.map(function(column) { - Object.keys(column).forEach(function(colKey) { - column[colKey] = column[colKey] === null ? "" : column[colKey].toString(); - }); - - column.width = null; - return column; - }); - }; - - me.setVisibleColumns = function(columnNumber, newVisibleColumns, paddingCount) { - me.number = columnNumber; - me.visible = newVisibleColumns; - me.padding = paddingCount; - - me.updateSlice(); - }; - - me.incColumnNumber = function(increment) { - me.number = me.number + increment; - }; - - me.setColumnNumber = function(newNumber) { - me.number = newNumber; - }; - - me.setPaddingCount = function(newPadding) { - me.padding = newPadding; - }; - - me.getPaddingCount = function() { - return me.padding; - }; - - me.hasMoreLeftColumns = function() { - return me.number > 0; - }; - - me.hasMoreRightColumns = function() { - return me.number + me.visible < me.total; - }; - - me.updateSlice(0); - return me; - }; - - var data = source.data; - var page = new Page(data, options); - var measurer = new Measurer(data, options); - var columns = new Columns(data, source.columns, options); - - var table = null; - var tableDiv = null; - var header = null; - var footer = null; - var tbody = null; - - // Caches pagedTable.clientWidth, specially for webkit - var cachedPagedTableClientWidth = null; - - var onChangeCallbacks = []; - - var clearSelection = function() { - if(document.selection && document.selection.empty) { - document.selection.empty(); - } else if(window.getSelection) { - var sel = window.getSelection(); - sel.removeAllRanges(); - } - }; - - var columnNavigationWidthPX = 5; - - var renderColumnNavigation = function(increment, backwards) { - var arrow = document.createElement("div"); - arrow.setAttribute("style", - "border-top: " + columnNavigationWidthPX + "px solid transparent;" + - "border-bottom: " + columnNavigationWidthPX + "px solid transparent;" + - "border-" + (backwards ? "right" : "left") + ": " + columnNavigationWidthPX + "px solid;"); - - var header = document.createElement("th"); - header.appendChild(arrow); - header.setAttribute("style", - "cursor: pointer;" + - "vertical-align: middle;" + - "min-width: " + columnNavigationWidthPX + "px;" + - "width: " + columnNavigationWidthPX + "px;"); - - header.onclick = function() { - columns.incColumnNumber(backwards ? -1 : increment); - - me.animateColumns(backwards); - renderFooter(); - - clearSelection(); - triggerOnChange(); - }; - - return header; - }; - - var maxColumnWidth = function(width) { - var padding = 80; - var columnMax = Math.max(cachedPagedTableClientWidth - padding, 0); - - return parseInt(width) > 0 ? - Math.min(columnMax, parseInt(width)) + "px" : - columnMax + "px"; - }; - - var clearHeader = function() { - var thead = pagedTable.querySelectorAll("thead")[0]; - thead.innerHTML = ""; - }; - - var renderHeader = function(clear) { - cachedPagedTableClientWidth = pagedTable.clientWidth; - - var fragment = document.createDocumentFragment(); - - header = document.createElement("tr"); - fragment.appendChild(header); - - if (columns.number > 0) - header.appendChild(renderColumnNavigation(-columns.visible, true)); - - columns.subset = columns.subset.map(function(columnData) { - var column = document.createElement("th"); - column.setAttribute("align", columnData.align); - column.style.textAlign = columnData.align; - - column.style.maxWidth = maxColumnWidth(null); - if (columnData.width) { - column.style.minWidth = - column.style.maxWidth = maxColumnWidth(columnData.width); - } - - var columnName = document.createElement("div"); - columnName.setAttribute("class", "pagedtable-header-name"); - if (columnData.label === "") { - columnName.innerHTML = " "; - } - else { - columnName.appendChild(document.createTextNode(columnData.label)); - } - column.appendChild(columnName); - - var columnType = document.createElement("div"); - columnType.setAttribute("class", "pagedtable-header-type"); - if (columnData.type === "") { - columnType.innerHTML = " "; - } - else { - columnType.appendChild(document.createTextNode("<" + columnData.type + ">")); - } - column.appendChild(columnType); - - header.appendChild(column); - - columnData.element = column; - - return columnData; - }); - - for (var idx = 0; idx < columns.getPaddingCount(); idx++) { - var paddingCol = document.createElement("th"); - paddingCol.setAttribute("class", "pagedtable-padding-col"); - header.appendChild(paddingCol); - } - - if (columns.number + columns.visible < columns.total) - header.appendChild(renderColumnNavigation(columns.visible, false)); - - if (typeof(clear) == "undefined" || clear) clearHeader(); - var thead = pagedTable.querySelectorAll("thead")[0]; - thead.appendChild(fragment); - }; - - me.animateColumns = function(backwards) { - var thead = pagedTable.querySelectorAll("thead")[0]; - - var headerOld = thead.querySelectorAll("tr")[0]; - var tbodyOld = table.querySelectorAll("tbody")[0]; - - me.fitColumns(backwards); - - renderHeader(false); - - header.style.opacity = "0"; - header.style.transform = backwards ? "translateX(-30px)" : "translateX(30px)"; - header.style.transition = "transform 200ms linear, opacity 200ms"; - header.style.transitionDelay = "0"; - - renderBody(false); - - if (headerOld) { - headerOld.style.position = "absolute"; - headerOld.style.transform = "translateX(0px)"; - headerOld.style.opacity = "1"; - headerOld.style.transition = "transform 100ms linear, opacity 100ms"; - headerOld.setAttribute("class", "pagedtable-remove-head"); - if (headerOld.style.transitionEnd) { - headerOld.addEventListener("transitionend", function() { - var headerOldByClass = thead.querySelector(".pagedtable-remove-head"); - if (headerOldByClass) thead.removeChild(headerOldByClass); - }); - } - else { - thead.removeChild(headerOld); - } - } - - if (tbodyOld) table.removeChild(tbodyOld); - - tbody.style.opacity = "0"; - tbody.style.transition = "transform 200ms linear, opacity 200ms"; - tbody.style.transitionDelay = "0ms"; - - // force relayout - window.getComputedStyle(header).opacity; - window.getComputedStyle(tbody).opacity; - - if (headerOld) { - headerOld.style.transform = backwards ? "translateX(20px)" : "translateX(-30px)"; - headerOld.style.opacity = "0"; - } - - header.style.transform = "translateX(0px)"; - header.style.opacity = "1"; - - tbody.style.opacity = "1"; - } - - me.onChange = function(callback) { - onChangeCallbacks.push(callback); - }; - - var triggerOnChange = function() { - onChangeCallbacks.forEach(function(onChange) { - onChange(); - }); - }; - - var clearBody = function() { - if (tbody) { - table.removeChild(tbody); - tbody = null; - } - }; - - var renderBody = function(clear) { - cachedPagedTableClientWidth = pagedTable.clientWidth - - var fragment = document.createDocumentFragment(); - - var pageData = data.slice(page.getRowStart(), page.getRowEnd()); - - pageData.forEach(function(dataRow, idxRow) { - var htmlRow = document.createElement("tr"); - htmlRow.setAttribute("class", (idxRow % 2 !==0) ? "even" : "odd"); - - if (columns.hasMoreLeftColumns()) - htmlRow.appendChild(document.createElement("td")); - - columns.subset.forEach(function(columnData) { - var cellName = columnData.name; - var dataCell = dataRow[cellName]; - var htmlCell = document.createElement("td"); - - if (dataCell === "NA") htmlCell.setAttribute("class", "pagedtable-na-cell"); - if (dataCell === "__NA__") dataCell = "NA"; - - var cellText = document.createTextNode(dataCell); - htmlCell.appendChild(cellText); - if (dataCell.length > 50) { - htmlCell.setAttribute("title", dataCell); - } - htmlCell.setAttribute("align", columnData.align); - htmlCell.style.textAlign = columnData.align; - htmlCell.style.maxWidth = maxColumnWidth(null); - if (columnData.width) { - htmlCell.style.minWidth = htmlCell.style.maxWidth = maxColumnWidth(columnData.width); - } - htmlRow.appendChild(htmlCell); - }); - - for (var idx = 0; idx < columns.getPaddingCount(); idx++) { - var paddingCol = document.createElement("td"); - paddingCol.setAttribute("class", "pagedtable-padding-col"); - htmlRow.appendChild(paddingCol); - } - - if (columns.hasMoreRightColumns()) - htmlRow.appendChild(document.createElement("td")); - - fragment.appendChild(htmlRow); - }); - - for (var idxPadding = 0; idxPadding < page.getPaddingRows(); idxPadding++) { - var paddingRow = document.createElement("tr"); - - var paddingCellRow = document.createElement("td"); - paddingCellRow.innerHTML = " "; - paddingCellRow.setAttribute("colspan", "100%"); - paddingRow.appendChild(paddingCellRow); - - fragment.appendChild(paddingRow); - } - - if (typeof(clear) == "undefined" || clear) clearBody(); - tbody = document.createElement("tbody"); - tbody.appendChild(fragment); - - table.appendChild(tbody); - }; - - var getLabelInfo = function() { - var pageStart = page.getRowStart(); - var pageEnd = page.getRowEnd(); - var totalRows = data.length; - - var totalRowsLabel = options.rows.total ? options.rows.total : totalRows; - var totalRowsLabelFormat = totalRowsLabel.toString().replace(/(\d)(?=(\d\d\d)+(?!\d))/g, '$1,'); - - var infoText = (pageStart + 1) + "-" + pageEnd + " of " + totalRowsLabelFormat + " rows"; - if (totalRows < page.rows) { - infoText = totalRowsLabel + " row" + (totalRows != 1 ? "s" : ""); - } - if (columns.total > columns.visible) { - var totalColumnsLabel = options.columns.total ? options.columns.total : columns.total; - - infoText = infoText + " | " + (columns.number + 1) + "-" + - (Math.min(columns.number + columns.visible, columns.total)) + - " of " + totalColumnsLabel + " columns"; - } - - return infoText; - }; - - var clearFooter = function() { - footer = pagedTable.querySelectorAll("div.pagedtable-footer")[0]; - footer.innerHTML = ""; - - return footer; - }; - - var createPageLink = function(idxPage) { - var pageLink = document.createElement("a"); - pageLinkClass = idxPage === page.number ? "pagedtable-index pagedtable-index-current" : "pagedtable-index"; - pageLink.setAttribute("class", pageLinkClass); - pageLink.setAttribute("data-page-index", idxPage); - pageLink.onclick = function() { - page.setPageNumber(parseInt(this.getAttribute("data-page-index"))); - renderBody(); - renderFooter(); - - triggerOnChange(); - }; - - pageLink.appendChild(document.createTextNode(idxPage + 1)); - - return pageLink; - } - - var renderFooter = function() { - footer = clearFooter(); - - var next = document.createElement("a"); - next.appendChild(document.createTextNode("Next")); - next.onclick = function() { - page.setPageNumber(page.number + 1); - renderBody(); - renderFooter(); - - triggerOnChange(); - }; - if (data.length > page.rows) footer.appendChild(next); - - var pageNumbers = document.createElement("div"); - pageNumbers.setAttribute("class", "pagedtable-indexes"); - - var pageRange = page.getVisiblePageRange(); - - if (pageRange.first) { - var pageLink = createPageLink(0); - pageNumbers.appendChild(pageLink); - - var pageSeparator = document.createElement("div"); - pageSeparator.setAttribute("class", "pagedtable-index-separator-left"); - pageSeparator.appendChild(document.createTextNode("...")) - pageNumbers.appendChild(pageSeparator); - } - - for (var idxPage = pageRange.start; idxPage < pageRange.end; idxPage++) { - var pageLink = createPageLink(idxPage); - - pageNumbers.appendChild(pageLink); - } - - if (pageRange.last) { - var pageSeparator = document.createElement("div"); - pageSeparator.setAttribute("class", "pagedtable-index-separator-right"); - pageSeparator.appendChild(document.createTextNode("...")) - pageNumbers.appendChild(pageSeparator); - - var pageLink = createPageLink(page.total - 1); - pageNumbers.appendChild(pageLink); - } - - if (data.length > page.rows) footer.appendChild(pageNumbers); - - var previous = document.createElement("a"); - previous.appendChild(document.createTextNode("Previous")); - previous.onclick = function() { - page.setPageNumber(page.number - 1); - renderBody(); - renderFooter(); - - triggerOnChange(); - }; - if (data.length > page.rows) footer.appendChild(previous); - - var infoLabel = document.createElement("div"); - infoLabel.setAttribute("class", "pagedtable-info"); - infoLabel.setAttribute("title", getLabelInfo()); - infoLabel.appendChild(document.createTextNode(getLabelInfo())); - footer.appendChild(infoLabel); - - var enabledClass = "pagedtable-index-nav"; - var disabledClass = "pagedtable-index-nav pagedtable-index-nav-disabled"; - previous.setAttribute("class", page.number <= 0 ? disabledClass : enabledClass); - next.setAttribute("class", (page.number + 1) * page.rows >= data.length ? disabledClass : enabledClass); - }; - - var measuresCell = null; - - var renderMeasures = function() { - var measuresTable = document.createElement("table"); - measuresTable.style.visibility = "hidden"; - measuresTable.style.position = "absolute"; - measuresTable.style.whiteSpace = "nowrap"; - measuresTable.style.height = "auto"; - measuresTable.style.width = "auto"; - - var measuresRow = document.createElement("tr"); - measuresTable.appendChild(measuresRow); - - measuresCell = document.createElement("td"); - var sampleString = "ABCDEFGHIJ0123456789"; - measuresCell.appendChild(document.createTextNode(sampleString)); - - measuresRow.appendChild(measuresCell); - - tableDiv.appendChild(measuresTable); - } - - me.init = function() { - tableDiv = document.createElement("div"); - pagedTable.appendChild(tableDiv); - var pagedTableClass = data.length > 0 ? - "pagedtable pagedtable-not-empty" : - "pagedtable pagedtable-empty"; - - if (columns.total == 0 || (columns.emptyNames() && data.length == 0)) { - pagedTableClass = pagedTableClass + " pagedtable-empty-columns"; - } - - tableDiv.setAttribute("class", pagedTableClass); - - renderMeasures(); - measurer.calculate(measuresCell); - columns.calculateWidths(measurer.measures); - - table = document.createElement("table"); - table.setAttribute("cellspacing", "0"); - table.setAttribute("class", "table table-condensed"); - tableDiv.appendChild(table); - - table.appendChild(document.createElement("thead")); - - var footerDiv = document.createElement("div"); - footerDiv.setAttribute("class", "pagedtable-footer"); - tableDiv.appendChild(footerDiv); - - // if the host has not yet provided horizontal space, render hidden - if (tableDiv.clientWidth <= 0) { - tableDiv.style.opacity = "0"; - } - - me.render(); - - // retry seizing columns later if the host has not provided space - function retryFit() { - if (tableDiv.clientWidth <= 0) { - setTimeout(retryFit, 100); - } else { - me.render(); - triggerOnChange(); - } - } - if (tableDiv.clientWidth <= 0) { - retryFit(); - } - }; - - var registerWidths = function() { - columns.subset = columns.subset.map(function(column) { - column.width = columns.widths[column.name].inner; - return column; - }); - }; - - var parsePadding = function(value) { - return parseInt(value) >= 0 ? parseInt(value) : 0; - }; - - me.fixedHeight = function() { - return options.rows.max != null; - } - - me.fitRows = function() { - if (me.fixedHeight()) - return; - - measurer.calculate(measuresCell); - - var rows = options.rows.min !== null ? options.rows.min : 0; - var headerHeight = header !== null && header.offsetHeight > 0 ? header.offsetHeight : 0; - var footerHeight = footer !== null && footer.offsetHeight > 0 ? footer.offsetHeight : 0; - - if (pagedTable.offsetHeight > 0) { - var availableHeight = pagedTable.offsetHeight - headerHeight - footerHeight; - rows = Math.floor((availableHeight) / measurer.measures.height); - } - - rows = options.rows.min !== null ? Math.max(options.rows.min, rows) : rows; - - page.setRows(rows); - } - - // The goal of this function is to add as many columns as possible - // starting from left-to-right, when the right most limit is reached - // it tries to add columns from the left as well. - // - // When startBackwards is true columns are added from right-to-left - me.fitColumns = function(startBackwards) { - measurer.calculate(measuresCell); - columns.calculateWidths(measurer.measures); - - if (tableDiv.clientWidth > 0) { - tableDiv.style.opacity = 1; - } - - var visibleColumns = tableDiv.clientWidth <= 0 ? 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(columns.max !== null && - columns.visible + columns.getPaddingCount() >= columns.max) { - break; - } - - // if we run out of right-columns - if (!backwards && columnNumber + columns.visible >= columns.total) { - // if we started adding right-columns, try adding left-columns - if (!startBackwards && columnNumber > 0) { - backwards = true; - } - else if (columns.min === null || visibleColumns + columns.getPaddingCount() >= columns.min) { - break; - } - else { - paddingCount = paddingCount + 1; - } - } - - // if we run out of left-columns - if (backwards && columnNumber == 0) { - // if we started adding left-columns, try adding right-columns - if (startBackwards && columnNumber + columns.visible < columns.total) { - backwards = false; - } - else if (columns.min === null || visibleColumns + columns.getPaddingCount() >= columns.min) { - break; - } - else { - paddingCount = paddingCount + 1; - } - } - - // when moving backwards try fitting left columns first - if (backwards && columnNumber 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@@ -// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat -// -// AnchorJS - v4.3.1 - 2021-04-17 -// https://www.bryanbraun.com/anchorjs/ -// Copyright (c) 2021 Bryan Braun; Licensed MIT -// -// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat -!function(A,e){"use strict";"function"==typeof define&&define.amd?define([],e):"object"==typeof module&&module.exports?module.exports=e():(A.AnchorJS=e(),A.anchors=new A.AnchorJS)}(this,function(){"use strict";return function(A){function 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a/choosing_files/libs/quarto-html/quarto-html.min.css +++ /dev/null @@ -1 +0,0 @@ -/*# sourceMappingURL=0a6b880beb84f9b6f36107a76f82c5b1.css.map */ diff --git a/choosing_files/libs/quarto-html/quarto-syntax-highlighting.css b/choosing_files/libs/quarto-html/quarto-syntax-highlighting.css deleted file mode 100644 index d9fd98f..0000000 --- a/choosing_files/libs/quarto-html/quarto-syntax-highlighting.css +++ /dev/null @@ -1,203 +0,0 @@ -/* quarto syntax highlight colors */ -:root { - --quarto-hl-ot-color: #003B4F; - --quarto-hl-at-color: #657422; - --quarto-hl-ss-color: #20794D; - --quarto-hl-an-color: #5E5E5E; - --quarto-hl-fu-color: #4758AB; - --quarto-hl-st-color: #20794D; - --quarto-hl-cf-color: #003B4F; - --quarto-hl-op-color: #5E5E5E; - --quarto-hl-er-color: #AD0000; - --quarto-hl-bn-color: #AD0000; - --quarto-hl-al-color: #AD0000; - --quarto-hl-va-color: #111111; - --quarto-hl-bu-color: inherit; - --quarto-hl-ex-color: inherit; - --quarto-hl-pp-color: #AD0000; - --quarto-hl-in-color: #5E5E5E; - --quarto-hl-vs-color: #20794D; - --quarto-hl-wa-color: #5E5E5E; - --quarto-hl-do-color: #5E5E5E; - --quarto-hl-im-color: #00769E; - --quarto-hl-ch-color: #20794D; - --quarto-hl-dt-color: #AD0000; - --quarto-hl-fl-color: #AD0000; - --quarto-hl-co-color: #5E5E5E; - --quarto-hl-cv-color: #5E5E5E; - --quarto-hl-cn-color: #8f5902; - --quarto-hl-sc-color: #5E5E5E; - --quarto-hl-dv-color: #AD0000; - --quarto-hl-kw-color: #003B4F; -} - -/* other quarto variables */ -:root { - --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; -} - -pre > code.sourceCode > span { - color: #003B4F; -} - -code span { - color: #003B4F; -} - -code.sourceCode > span { - color: #003B4F; -} - -div.sourceCode, -div.sourceCode pre.sourceCode { - color: #003B4F; -} - -code span.ot { - color: #003B4F; - font-style: inherit; -} - -code span.at { - color: #657422; - font-style: inherit; -} - -code span.ss { - color: #20794D; - 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color: #00769E; - font-style: inherit; -} - -code span.ch { - color: #20794D; - font-style: inherit; -} - -code span.dt { - color: #AD0000; - font-style: inherit; -} - -code span.fl { - color: #AD0000; - font-style: inherit; -} - -code span.co { - color: #5E5E5E; - font-style: inherit; -} - -code span.cv { - color: #5E5E5E; - font-style: italic; -} - -code span.cn { - color: #8f5902; - font-style: inherit; -} - -code span.sc { - color: #5E5E5E; - font-style: inherit; -} - -code span.dv { - color: #AD0000; - font-style: inherit; -} - -code span.kw { - color: #003B4F; - font-style: inherit; -} - -.prevent-inlining { - content: " { - const sibling = el.previousElementSibling; - if (sibling && sibling.tagName === "A") { - return sibling.classList.contains("active"); - } else { - return false; - } - }; - - // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) - function fireSlideEnter(e) { - const event = window.document.createEvent("Event"); - event.initEvent("slideenter", true, true); - window.document.dispatchEvent(event); - } - const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); - tabs.forEach((tab) => { - tab.addEventListener("shown.bs.tab", fireSlideEnter); - }); - - // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) - document.addEventListener("tabby", fireSlideEnter, false); - - // Track scrolling and mark TOC links as active - // get table of contents and sidebar (bail if we don't have at least one) - const tocLinks = tocEl - ? [...tocEl.querySelectorAll("a[data-scroll-target]")] - : []; - const makeActive = (link) => tocLinks[link].classList.add("active"); - const removeActive = (link) => tocLinks[link].classList.remove("active"); - const removeAllActive = () => - [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); - - // activate the anchor for a section associated with this TOC entry - tocLinks.forEach((link) => { - link.addEventListener("click", () => { - if (link.href.indexOf("#") !== -1) { - const anchor = link.href.split("#")[1]; - const heading = window.document.querySelector( - `[data-anchor-id=${anchor}]` - ); - if (heading) { - // Add the class - heading.classList.add("reveal-anchorjs-link"); - - // function to show the anchor - const handleMouseout = () => { - heading.classList.remove("reveal-anchorjs-link"); - heading.removeEventListener("mouseout", handleMouseout); - }; - - // add a function to clear the anchor when the user mouses out of it - heading.addEventListener("mouseout", handleMouseout); 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- } - } - } catch { - // Ignore errors - } - }; - function hasTitleCategories() { - return window.document.querySelector(categorySelector) !== null; - } - - function offsetRelativeUrl(url) { - const offset = getMeta("quarto:offset"); - return offset ? offset + url : url; - } - - function offsetAbsoluteUrl(url) { - const offset = getMeta("quarto:offset"); - const baseUrl = new URL(offset, window.location); - - const projRelativeUrl = url.replace(baseUrl, ""); - if (projRelativeUrl.startsWith("/")) { - return projRelativeUrl; - } else { - return "/" + projRelativeUrl; - } - } - - // read a meta tag value - function getMeta(metaName) { - const metas = window.document.getElementsByTagName("meta"); - for (let i = 0; i < metas.length; i++) { - if (metas[i].getAttribute("name") === metaName) { - return metas[i].getAttribute("content"); - } - } - return ""; - } - - async function findAndActivateCategories() { - const currentPagePath = offsetAbsoluteUrl(window.location.href); - const response = await fetch(offsetRelativeUrl("listings.json")); - if (response.status == 200) { - return response.json().then(function (listingPaths) { - const listingHrefs = []; - for (const listingPath of listingPaths) { - const pathWithoutLeadingSlash = listingPath.listing.substring(1); - for (const item of listingPath.items) { - if ( - item === currentPagePath || - item === currentPagePath + "index.html" - ) { - // Resolve this path against the offset to be sure - // we already are using the correct path to the listing - // (this adjusts the listing urls to be rooted against - // whatever root the page is actually running against) - const relative = offsetRelativeUrl(pathWithoutLeadingSlash); - const baseUrl = window.location; - const resolvedPath = new URL(relative, baseUrl); - listingHrefs.push(resolvedPath.pathname); - break; - } - } - } - - // Look up the tree for a nearby linting and use that if we find one - const nearestListing = findNearestParentListing( - offsetAbsoluteUrl(window.location.pathname), - listingHrefs - ); - if (nearestListing) { - activateCategories(nearestListing); - } else { - // See if the referrer is a listing page for this item - const referredRelativePath = offsetAbsoluteUrl(document.referrer); - const referrerListing = listingHrefs.find((listingHref) => { - const isListingReferrer = - listingHref === referredRelativePath || - listingHref === referredRelativePath + "index.html"; - return isListingReferrer; - }); - - if (referrerListing) { - // Try to use the referrer if possible - activateCategories(referrerListing); - } else if (listingHrefs.length > 0) { - // Otherwise, just fall back to the first listing - activateCategories(listingHrefs[0]); - } - } - }); - } - } - if (hasTitleCategories()) { - findAndActivateCategories(); - } - - const findNearestParentListing = (href, listingHrefs) => { - if (!href || !listingHrefs) { - return undefined; - } - // Look up the tree for a nearby linting and use that if we find one - const relativeParts = href.substring(1).split("/"); - while (relativeParts.length > 0) { - const path = relativeParts.join("/"); - for (const listingHref of listingHrefs) { - if (listingHref.startsWith(path)) { - return listingHref; - } - } - relativeParts.pop(); - } - - return undefined; - }; - - const manageSidebarVisiblity = (el, placeholderDescriptor) => { - let isVisible = true; - let elRect; - - return (hiddenRegions) => { - if (el === null) { - return; - } - - // Find the last element of the TOC - const lastChildEl = el.lastElementChild; - - if (lastChildEl) { - // Converts the sidebar to a menu - const convertToMenu = () => { - for (const child of el.children) { - child.style.opacity = 0; - child.style.overflow = "hidden"; - } - - nexttick(() => { - const toggleContainer = window.document.createElement("div"); - toggleContainer.style.width = "100%"; - toggleContainer.classList.add("zindex-over-content"); - toggleContainer.classList.add("quarto-sidebar-toggle"); - toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom - toggleContainer.id = placeholderDescriptor.id; - toggleContainer.style.position = "fixed"; - - const toggleIcon = window.document.createElement("i"); - toggleIcon.classList.add("quarto-sidebar-toggle-icon"); - toggleIcon.classList.add("bi"); - toggleIcon.classList.add("bi-caret-down-fill"); - - const toggleTitle = window.document.createElement("div"); - const titleEl = window.document.body.querySelector( - placeholderDescriptor.titleSelector - ); - if (titleEl) { - toggleTitle.append( - titleEl.textContent || titleEl.innerText, - toggleIcon - ); - } - toggleTitle.classList.add("zindex-over-content"); - toggleTitle.classList.add("quarto-sidebar-toggle-title"); - toggleContainer.append(toggleTitle); - - const toggleContents = window.document.createElement("div"); - toggleContents.classList = el.classList; - toggleContents.classList.add("zindex-over-content"); - toggleContents.classList.add("quarto-sidebar-toggle-contents"); - for (const child of el.children) { - if (child.id === "toc-title") { - continue; - } - - const clone = child.cloneNode(true); - clone.style.opacity = 1; - clone.style.display = null; - toggleContents.append(clone); - } - toggleContents.style.height = "0px"; - const positionToggle = () => { - // position the element (top left of parent, same width as parent) - if (!elRect) { - elRect = el.getBoundingClientRect(); - } - toggleContainer.style.left = `${elRect.left}px`; - toggleContainer.style.top = `${elRect.top}px`; - toggleContainer.style.width = `${elRect.width}px`; - }; - positionToggle(); - - toggleContainer.append(toggleContents); - el.parentElement.prepend(toggleContainer); - - // Process clicks - let tocShowing = false; - // Allow the caller to control whether this is dismissed - // when it is clicked (e.g. sidebar navigation supports - // opening and closing the nav tree, so don't dismiss on click) - const clickEl = placeholderDescriptor.dismissOnClick - ? toggleContainer - : toggleTitle; - - const closeToggle = () => { - if (tocShowing) { - toggleContainer.classList.remove("expanded"); - toggleContents.style.height = "0px"; - tocShowing = false; - } - }; - - // Get rid of any expanded toggle if the user scrolls - window.document.addEventListener( - "scroll", - throttle(() => { - closeToggle(); - }, 50) - ); - - // Handle positioning of the toggle - window.addEventListener( - "resize", - throttle(() => { - elRect = undefined; - positionToggle(); - }, 50) - ); - - window.addEventListener("quarto-hrChanged", () => { - elRect = undefined; - }); - - // Process the click - clickEl.onclick = () => { - if (!tocShowing) { - toggleContainer.classList.add("expanded"); - toggleContents.style.height = null; - tocShowing = true; - } else { - closeToggle(); - } - }; - }); - }; - - // Converts a sidebar from a menu back to a sidebar - const convertToSidebar = () => { - for (const child of el.children) { - child.style.opacity = 1; - child.style.overflow = null; - } - - const placeholderEl = window.document.getElementById( - placeholderDescriptor.id - ); - if (placeholderEl) { - placeholderEl.remove(); - } - - el.classList.remove("rollup"); - }; - - if (isReaderMode()) { - convertToMenu(); - isVisible = false; - } else { - // Find the top and bottom o the element that is being managed - const elTop = el.offsetTop; - const elBottom = - elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; - - if (!isVisible) { - // If the element is current not visible reveal if there are - // no conflicts with overlay regions - if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { - convertToSidebar(); - isVisible = true; - } - } else { - // If the element is visible, hide it if it conflicts with overlay regions - // and insert a placeholder toggle (or if we're in reader mode) - if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { - convertToMenu(); - isVisible = false; - } - } - } - } - }; - }; - - // Find any conflicting margin elements and add margins to the - // top to prevent overlap - const marginChildren = window.document.querySelectorAll( - ".column-margin.column-container > * " - ); - - const layoutMarginEls = () => { - let lastBottom = 0; - for (const marginChild of marginChildren) { - if (marginChild.offsetParent !== null) { - // clear the top margin so we recompute it - marginChild.style.marginTop = null; - const top = marginChild.getBoundingClientRect().top + window.scrollY; - if (top < lastBottom) { - const margin = lastBottom - top; - marginChild.style.marginTop = `${margin}px`; - } - const styles = window.getComputedStyle(marginChild); - const marginTop = parseFloat(styles["marginTop"]); - lastBottom = - top + marginChild.getBoundingClientRect().height + marginTop; - } - } - }; - nexttick(layoutMarginEls); - - const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); - for (const tabEl of tabEls) { - const id = tabEl.getAttribute("data-bs-target"); - if (id) { - const columnEl = document.querySelector( - `${id} .column-margin, .tabset-margin-content` - ); - if (columnEl) - tabEl.addEventListener("shown.bs.tab", function (event) { - - const el = event.srcElement; - if (el) { - const visibleCls = `${el.id}-margin-content`; - // walk up until we find a parent tabset - let panelTabsetEl = el.parentElement; - while (panelTabsetEl) { - if (panelTabsetEl.classList.contains("panel-tabset")) { - break; - } - panelTabsetEl = panelTabsetEl.parentElement; - } - - if (panelTabsetEl) { - const prevSib = panelTabsetEl.previousElementSibling; - if ( - prevSib && - prevSib.classList.contains("tabset-margin-container") - ) { - const childNodes = prevSib.querySelectorAll( - ".tabset-margin-content" - ); - for (const childEl of childNodes) { - if (childEl.classList.contains(visibleCls)) { - childEl.classList.remove("collapse"); - } else { - childEl.classList.add("collapse"); - } - } - } - } - } - - layoutMarginEls(); - }); - } - } - - // Manage the visibility of the toc and the sidebar - const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { - id: "quarto-toc-toggle", - titleSelector: "#toc-title", - dismissOnClick: true, - }); - const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { - id: "quarto-sidebarnav-toggle", - titleSelector: ".title", - dismissOnClick: false, - }); - let tocLeftScrollVisibility; - if (leftTocEl) { - tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { - id: "quarto-lefttoc-toggle", - titleSelector: "#toc-title", - dismissOnClick: true, - }); - } - - // Find the first element that uses formatting in special columns - const conflictingEls = window.document.body.querySelectorAll( - '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' - ); - - // Filter all the possibly conflicting elements into ones - // the do conflict on the left or ride side - const arrConflictingEls = Array.from(conflictingEls); - const leftSideConflictEls = arrConflictingEls.filter((el) => { - if (el.tagName === "ASIDE") { - return false; - } - return Array.from(el.classList).find((className) => { - return ( - className !== "column-body" && - className.startsWith("column-") && - !className.endsWith("right") && - !className.endsWith("container") && - className !== "column-margin" - ); - }); - }); - const rightSideConflictEls = arrConflictingEls.filter((el) => { - if (el.tagName === "ASIDE") { - return true; - } - - const hasMarginCaption = Array.from(el.classList).find((className) => { - return className == "margin-caption"; - }); - if (hasMarginCaption) { - return true; - } - - return Array.from(el.classList).find((className) => { - return ( - className !== "column-body" && - !className.endsWith("container") && - className.startsWith("column-") && - !className.endsWith("left") - ); - }); - }); - - const kOverlapPaddingSize = 10; - function toRegions(els) { - return els.map((el) => { - const boundRect = el.getBoundingClientRect(); - const top = - boundRect.top + - document.documentElement.scrollTop - - kOverlapPaddingSize; - return { - top, - bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, - }; - }); - } - - let hasObserved = false; - const visibleItemObserver = (els) => { - let visibleElements = [...els]; - const intersectionObserver = new IntersectionObserver( - (entries, _observer) => { - entries.forEach((entry) => { - if (entry.isIntersecting) { - if (visibleElements.indexOf(entry.target) === -1) { - visibleElements.push(entry.target); - } - } else { - visibleElements = visibleElements.filter((visibleEntry) => { - return visibleEntry !== entry; - }); - } - }); - - if (!hasObserved) { - hideOverlappedSidebars(); - } - hasObserved = true; - }, - {} - ); - els.forEach((el) => { - intersectionObserver.observe(el); - }); - - return { - getVisibleEntries: () => { - return visibleElements; - }, - }; - }; - - const rightElementObserver = visibleItemObserver(rightSideConflictEls); - const leftElementObserver = visibleItemObserver(leftSideConflictEls); - - const hideOverlappedSidebars = () => { - marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); - sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); - if (tocLeftScrollVisibility) { - tocLeftScrollVisibility( - toRegions(leftElementObserver.getVisibleEntries()) - ); - } - }; - - window.quartoToggleReader = () => { - // Applies a slow class (or removes it) - // to update the transition speed - const slowTransition = (slow) => { - const manageTransition = (id, slow) => { - const el = document.getElementById(id); - if (el) { - if (slow) { - el.classList.add("slow"); - } else { - el.classList.remove("slow"); - } - } - }; - - manageTransition("TOC", slow); - manageTransition("quarto-sidebar", slow); - }; - const readerMode = !isReaderMode(); - setReaderModeValue(readerMode); - - // If we're entering reader mode, slow the transition - if (readerMode) { - slowTransition(readerMode); - } - highlightReaderToggle(readerMode); - hideOverlappedSidebars(); - - // If we're exiting reader mode, restore the non-slow transition - if (!readerMode) { - slowTransition(!readerMode); - } - }; - - const highlightReaderToggle = (readerMode) => { - const els = document.querySelectorAll(".quarto-reader-toggle"); - if (els) { - els.forEach((el) => { - if (readerMode) { - el.classList.add("reader"); - } else { - el.classList.remove("reader"); - } - }); - } - }; - - const setReaderModeValue = (val) => { - if (window.location.protocol !== "file:") { - window.localStorage.setItem("quarto-reader-mode", val); - } else { - localReaderMode = val; - } - }; - - const isReaderMode = () => { - if (window.location.protocol !== "file:") { - return window.localStorage.getItem("quarto-reader-mode") === "true"; - } else { - return localReaderMode; - } - }; - let localReaderMode = null; - - const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); - const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; - - // Walk the TOC and collapse/expand nodes - // Nodes are expanded if: - // - they are top level - // - they have children that are 'active' links - // - they are directly below an link that is 'active' - const walk = (el, depth) => { - // Tick depth when we enter a UL - if (el.tagName === "UL") { - depth = depth + 1; - } - - // It this is active link - let isActiveNode = false; - if (el.tagName === "A" && el.classList.contains("active")) { - isActiveNode = true; - } - - // See if there is an active child to this element - let hasActiveChild = false; - for (child of el.children) { - hasActiveChild = walk(child, depth) || hasActiveChild; - } - - // Process the collapse state if this is an UL - if (el.tagName === "UL") { - if (tocOpenDepth === -1 && depth > 1) { - el.classList.add("collapse"); - } else if ( - depth <= tocOpenDepth || - hasActiveChild || - prevSiblingIsActiveLink(el) - ) { - el.classList.remove("collapse"); - } else { - el.classList.add("collapse"); - } - - // untick depth when we leave a UL - depth = depth - 1; - } - return hasActiveChild || isActiveNode; - }; - - // walk the TOC and expand / collapse any items that should be shown - - if (tocEl) { - walk(tocEl, 0); - updateActiveLink(); - } - - // Throttle the scroll event and walk peridiocally - window.document.addEventListener( - "scroll", - throttle(() => { - if (tocEl) { - updateActiveLink(); - walk(tocEl, 0); - } - if (!isReaderMode()) { - hideOverlappedSidebars(); - } - }, 5) - ); - window.addEventListener( - "resize", - throttle(() => { - if (!isReaderMode()) { - hideOverlappedSidebars(); - } - }, 10) - ); - hideOverlappedSidebars(); - highlightReaderToggle(isReaderMode()); -}); - -// grouped tabsets -window.addEventListener("pageshow", (_event) => { - function getTabSettings() { - const data = localStorage.getItem("quarto-persistent-tabsets-data"); - if (!data) { - localStorage.setItem("quarto-persistent-tabsets-data", "{}"); - return {}; - } - if (data) { - return JSON.parse(data); - } - } - - function setTabSettings(data) { - localStorage.setItem( - "quarto-persistent-tabsets-data", - JSON.stringify(data) - ); - } - - function setTabState(groupName, groupValue) { - const data = getTabSettings(); - data[groupName] = groupValue; - setTabSettings(data); - } - - function toggleTab(tab, active) { - const tabPanelId = tab.getAttribute("aria-controls"); - const tabPanel = document.getElementById(tabPanelId); - if (active) { - tab.classList.add("active"); - tabPanel.classList.add("active"); - } else { - tab.classList.remove("active"); - tabPanel.classList.remove("active"); - } - } - - function toggleAll(selectedGroup, selectorsToSync) { - for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { - const active = selectedGroup === thisGroup; - for (const tab of tabs) { - toggleTab(tab, active); - } - } - } - - function findSelectorsToSyncByLanguage() { - const result = {}; - const tabs = Array.from( - document.querySelectorAll(`div[data-group] a[id^='tabset-']`) - ); - for (const item of tabs) { - const div = item.parentElement.parentElement.parentElement; - const group = div.getAttribute("data-group"); - if (!result[group]) { - result[group] = {}; - } - const selectorsToSync = result[group]; - const value = item.innerHTML; - if (!selectorsToSync[value]) { - selectorsToSync[value] = []; - } - selectorsToSync[value].push(item); - } - return result; - } - - function setupSelectorSync() { - const selectorsToSync = findSelectorsToSyncByLanguage(); - Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { - Object.entries(tabSetsByValue).forEach(([value, items]) => { - items.forEach((item) => { - item.addEventListener("click", (_event) => { - setTabState(group, value); - toggleAll(value, selectorsToSync[group]); - }); - }); - }); - }); - return selectorsToSync; - } - - const selectorsToSync = setupSelectorSync(); - for (const [group, selectedName] of Object.entries(getTabSettings())) { - const selectors = selectorsToSync[group]; - // it's possible that stale state gives us empty selections, so we explicitly check here. - if (selectors) { - toggleAll(selectedName, selectors); - } - } -}); - -function throttle(func, wait) { - let waiting = false; - return function () { - if (!waiting) { - func.apply(this, arguments); - waiting = true; - setTimeout(function () { - waiting = false; - }, wait); - } - }; -} - -function nexttick(func) { - return setTimeout(func, 0); -} diff --git a/choosing_files/libs/quarto-html/tabby.min.js b/choosing_files/libs/quarto-html/tabby.min.js deleted file mode 100644 index 4f44c7d..0000000 --- a/choosing_files/libs/quarto-html/tabby.min.js +++ /dev/null @@ -1,418 +0,0 @@ -(function (root, factory) { - if (typeof define === "function" && define.amd) { - define([], function () { - return factory(root); - }); - } else if (typeof exports === "object") { - module.exports = factory(root); - } else { - root.Tabby = factory(root); - } -})( - typeof global !== "undefined" - ? global - : typeof window !== "undefined" - ? window - : this, - function (window) { - "use strict"; - - // - // Variables - // - - var defaults = { - idPrefix: "tabby-toggle_", - default: "[data-tabby-default]", - }; - - // - // Methods - // - - /** - * Merge two or more objects together. - * @param {Object} objects The objects to merge together - * @returns {Object} Merged values of defaults and options - */ - var extend = function () { - var merged = {}; - Array.prototype.forEach.call(arguments, function (obj) { - for (var key in obj) { - if (!obj.hasOwnProperty(key)) return; - merged[key] = obj[key]; - } - }); - return merged; - }; - - /** - * Emit a custom event - * @param {String} type The event type - * @param {Node} tab The tab to attach the event to - * @param {Node} details Details about the event - */ - var emitEvent = function (tab, details) { - // Create a new event - var event; - if (typeof window.CustomEvent === "function") { - event = new CustomEvent("tabby", { - bubbles: true, - cancelable: true, - detail: details, - }); - } else { - event = document.createEvent("CustomEvent"); - event.initCustomEvent("tabby", true, true, details); - } - - // Dispatch the event - tab.dispatchEvent(event); - }; - - var focusHandler = function (event) { - toggle(event.target); - }; - - var getKeyboardFocusableElements = function (element) { - return [ - ...element.querySelectorAll( - 'a[href], button, input, textarea, select, details,[tabindex]:not([tabindex="-1"])' - ), - ].filter( - (el) => !el.hasAttribute("disabled") && !el.getAttribute("aria-hidden") - ); - }; - - /** - * Remove roles and attributes from a tab and its content - * @param {Node} tab The tab - * @param {Node} content The tab content - * @param {Object} settings User settings and options - */ - var destroyTab = function (tab, content, settings) { - // Remove the generated ID - if (tab.id.slice(0, settings.idPrefix.length) === settings.idPrefix) { - tab.id = ""; - } - - // remove event listener - tab.removeEventListener("focus", focusHandler, true); - - // Remove roles - tab.removeAttribute("role"); - tab.removeAttribute("aria-controls"); - tab.removeAttribute("aria-selected"); - tab.removeAttribute("tabindex"); - tab.closest("li").removeAttribute("role"); - content.removeAttribute("role"); - content.removeAttribute("aria-labelledby"); - content.removeAttribute("hidden"); - }; - - /** - * Add the required roles and attributes to a tab and its content - * @param {Node} tab The tab - * @param {Node} content The tab content - * @param {Object} settings User settings and options - */ - var setupTab = function (tab, content, settings) { - // Give tab an ID if it doesn't already have one - if (!tab.id) { - tab.id = settings.idPrefix + content.id; - } - - // Add roles - tab.setAttribute("role", "tab"); - tab.setAttribute("aria-controls", content.id); - tab.closest("li").setAttribute("role", "presentation"); - content.setAttribute("role", "tabpanel"); - content.setAttribute("aria-labelledby", tab.id); - - // Add selected state - if (tab.matches(settings.default)) { - tab.setAttribute("aria-selected", "true"); - } else { - tab.setAttribute("aria-selected", "false"); - content.setAttribute("hidden", "hidden"); - } - - // add focus event listender - tab.addEventListener("focus", focusHandler); - }; - - /** - * Hide a tab and its content - * @param {Node} newTab The new tab that's replacing it - */ - var hide = function (newTab) { - // Variables - var tabGroup = newTab.closest('[role="tablist"]'); - if (!tabGroup) return {}; - var tab = tabGroup.querySelector('[role="tab"][aria-selected="true"]'); - if (!tab) return {}; - var content = document.querySelector(tab.hash); - - // Hide the tab - tab.setAttribute("aria-selected", "false"); - - // Hide the content - if (!content) return { previousTab: tab }; - content.setAttribute("hidden", "hidden"); - - // Return the hidden tab and content - return { - previousTab: tab, - previousContent: content, - }; - }; - - /** - * Show a tab and its content - * @param {Node} tab The tab - * @param {Node} content The tab content - */ - var show = function (tab, content) { - tab.setAttribute("aria-selected", "true"); - content.removeAttribute("hidden"); - tab.focus(); - }; - - /** - * Toggle a new tab - * @param {Node} tab The tab to show - */ - var toggle = function (tab) { - // Make sure there's a tab to toggle and it's not already active - if (!tab || tab.getAttribute("aria-selected") == "true") return; - - // Variables - var content = document.querySelector(tab.hash); - if (!content) return; - - // Hide active tab and content - var details = hide(tab); - - // Show new tab and content - show(tab, content); - - // Add event details - details.tab = tab; - details.content = content; - - // Emit a custom event - emitEvent(tab, details); - }; - - /** - * Get all of the tabs in a tablist - * @param {Node} tab A tab from the list - * @return {Object} The tabs and the index of the currently active one - */ - var getTabsMap = function (tab) { - var tabGroup = tab.closest('[role="tablist"]'); - var tabs = tabGroup ? tabGroup.querySelectorAll('[role="tab"]') : null; - if (!tabs) return; - return { - tabs: tabs, - index: Array.prototype.indexOf.call(tabs, tab), - }; - }; - - /** - * Switch the active tab based on keyboard activity - * @param {Node} tab The currently active tab - * @param {Key} key The key that was pressed - */ - var switchTabs = function (tab, key) { - // Get a map of tabs - var map = getTabsMap(tab); - if (!map) return; - var length = map.tabs.length - 1; - var index; - - // Go to previous tab - if (["ArrowUp", "ArrowLeft", "Up", "Left"].indexOf(key) > -1) { - index = map.index < 1 ? length : map.index - 1; - } - - // Go to next tab - else if (["ArrowDown", "ArrowRight", "Down", "Right"].indexOf(key) > -1) { - index = map.index === length ? 0 : map.index + 1; - } - - // Go to home - else if (key === "Home") { - index = 0; - } - - // Go to end - else if (key === "End") { - index = length; - } - - // Toggle the tab - toggle(map.tabs[index]); - }; - - /** - * Create the Constructor object - */ - var Constructor = function (selector, options) { - // - // Variables - // - - var publicAPIs = {}; - var settings, tabWrapper; - - // - // Methods - // - - publicAPIs.destroy = function () { - // Get all tabs - var tabs = tabWrapper.querySelectorAll("a"); - - // Add roles to tabs - Array.prototype.forEach.call(tabs, function (tab) { - // Get the tab content - var content = document.querySelector(tab.hash); - if (!content) return; - - // Setup the tab - destroyTab(tab, content, settings); - }); - - // Remove role from wrapper - tabWrapper.removeAttribute("role"); - - // Remove event listeners - document.documentElement.removeEventListener( - "click", - clickHandler, - true - ); - tabWrapper.removeEventListener("keydown", keyHandler, true); - - // Reset variables - settings = null; - tabWrapper = null; - }; - - /** - * Setup the DOM with the proper attributes - */ - publicAPIs.setup = function () { - // Variables - tabWrapper = document.querySelector(selector); - if (!tabWrapper) return; - var tabs = tabWrapper.querySelectorAll("a"); - - // Add role to wrapper - tabWrapper.setAttribute("role", "tablist"); - - // Add roles to tabs. provide dynanmic tab indexes if we are within reveal - var contentTabindexes = - window.document.body.classList.contains("reveal-viewport"); - var nextTabindex = 1; - Array.prototype.forEach.call(tabs, function (tab) { - if (contentTabindexes) { - tab.setAttribute("tabindex", "" + nextTabindex++); - } else { - tab.setAttribute("tabindex", "0"); - } - - // Get the tab content - var content = document.querySelector(tab.hash); - if (!content) return; - - // set tab indexes for content - if (contentTabindexes) { - getKeyboardFocusableElements(content).forEach(function (el) { - el.setAttribute("tabindex", "" + nextTabindex++); - }); - } - - // Setup the tab - 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e(h,{...Ot,announceStatus:fe,getStatusText:be,print:oe,focus:le,progress:ae,controls:se,location:ie,overview:ee,fragments:G,slideContent:_,slideNumber:J,onUserInput:Mt,closeOverlay:ze,updateSlidesVisibility:at,layoutSlideContents:qe,transformSlides:xe,cueAutoSlide:Et,cancelAutoSlide:Rt}),Ot}let W=U,K=[];W.initialize=e=>(Object.assign(W,new U(document.querySelector(".reveal"),e)),K.map((e=>e(W))),W.initialize()),["configure","on","off","addEventListener","removeEventListener","registerPlugin"].forEach((e=>{W[e]=(...t)=>{K.push((i=>i[e].call(null,...t)))}})),W.isReady=()=>!1,W.VERSION="4.3.1";export default W; -//# sourceMappingURL=reveal.esm.js.map diff --git a/choosing_files/libs/revealjs/dist/reveal.esm.js.map b/choosing_files/libs/revealjs/dist/reveal.esm.js.map deleted file mode 100644 index 286c75a..0000000 --- a/choosing_files/libs/revealjs/dist/reveal.esm.js.map +++ /dev/null @@ -1 +0,0 @@ -{"version":3,"file":"reveal.esm.js","sources":["../js/utils/util.js","../js/utils/device.js","../node_modules/fitty/dist/fitty.module.js","../js/controllers/slidecontent.js","../js/controllers/slidenumber.js","../js/utils/color.js","../js/controllers/backgrounds.js","../js/utils/constants.js","../js/controllers/autoanimate.js","../js/controllers/fragments.js","../js/controllers/overview.js","../js/controllers/keyboard.js","../js/controllers/location.js","../js/controllers/controls.js","../js/controllers/progress.js","../js/controllers/pointer.js","../js/utils/loader.js","../js/controllers/plugins.js","../js/controllers/print.js","../js/controllers/touch.js","../js/controllers/focus.js","../js/controllers/notes.js","../js/components/playback.js","../js/config.js","../js/reveal.js","../js/index.js"],"sourcesContent":["/**\n * Extend object a with the properties of object b.\n * If there's a conflict, object b takes precedence.\n *\n * @param {object} a\n * @param {object} b\n */\nexport const extend = ( a, b ) => {\n\n\tfor( let i in b ) {\n\t\ta[ i ] = b[ i ];\n\t}\n\n\treturn a;\n\n}\n\n/**\n * querySelectorAll but returns an Array.\n */\nexport const queryAll = ( el, selector ) => {\n\n\treturn Array.from( el.querySelectorAll( selector ) );\n\n}\n\n/**\n * classList.toggle() with cross browser support\n */\nexport const toggleClass = ( el, className, value ) => {\n\tif( value ) {\n\t\tel.classList.add( className );\n\t}\n\telse {\n\t\tel.classList.remove( className );\n\t}\n}\n\n/**\n * Utility for deserializing a value.\n *\n * @param {*} value\n * @return {*}\n */\nexport const deserialize = ( value ) => {\n\n\tif( typeof value === 'string' ) {\n\t\tif( value === 'null' ) return null;\n\t\telse if( value === 'true' ) return true;\n\t\telse if( value === 'false' ) return false;\n\t\telse if( value.match( /^-?[\\d\\.]+$/ ) ) return parseFloat( value );\n\t}\n\n\treturn value;\n\n}\n\n/**\n * Measures the distance in pixels between point a\n * and point b.\n *\n * @param {object} a point with x/y properties\n * @param {object} b point with x/y properties\n *\n * @return {number}\n */\nexport const distanceBetween = ( a, b ) => {\n\n\tlet dx = a.x - b.x,\n\t\tdy = a.y - b.y;\n\n\treturn Math.sqrt( dx*dx + dy*dy );\n\n}\n\n/**\n * Applies a CSS transform to the target element.\n *\n * @param {HTMLElement} element\n * @param {string} transform\n */\nexport const transformElement = ( element, transform ) => {\n\n\telement.style.transform = transform;\n\n}\n\n/**\n * Element.matches with IE support.\n *\n * @param {HTMLElement} target The element to match\n * @param {String} selector The CSS selector to match\n * the element against\n *\n * @return {Boolean}\n */\nexport const matches = ( target, selector ) => {\n\n\tlet matchesMethod = target.matches || target.matchesSelector || target.msMatchesSelector;\n\n\treturn !!( matchesMethod && matchesMethod.call( target, selector ) );\n\n}\n\n/**\n * Find the closest parent that matches the given\n * selector.\n *\n * @param {HTMLElement} target The child element\n * @param {String} selector The CSS selector to match\n * the parents against\n *\n * @return {HTMLElement} The matched parent or null\n * if no matching parent was found\n */\nexport const closest = ( target, selector ) => {\n\n\t// Native Element.closest\n\tif( typeof target.closest === 'function' ) {\n\t\treturn target.closest( selector );\n\t}\n\n\t// Polyfill\n\twhile( target ) {\n\t\tif( matches( target, selector ) ) {\n\t\t\treturn target;\n\t\t}\n\n\t\t// Keep searching\n\t\ttarget = target.parentNode;\n\t}\n\n\treturn null;\n\n}\n\n/**\n * Handling the fullscreen functionality via the fullscreen API\n *\n * @see http://fullscreen.spec.whatwg.org/\n * @see https://developer.mozilla.org/en-US/docs/DOM/Using_fullscreen_mode\n */\nexport const enterFullscreen = element => {\n\n\telement = element || document.documentElement;\n\n\t// Check which implementation is available\n\tlet requestMethod = element.requestFullscreen ||\n\t\t\t\t\t\telement.webkitRequestFullscreen ||\n\t\t\t\t\t\telement.webkitRequestFullScreen ||\n\t\t\t\t\t\telement.mozRequestFullScreen ||\n\t\t\t\t\t\telement.msRequestFullscreen;\n\n\tif( requestMethod ) {\n\t\trequestMethod.apply( element );\n\t}\n\n}\n\n/**\n * Creates an HTML element and returns a reference to it.\n * If the element already exists the existing instance will\n * be returned.\n *\n * @param {HTMLElement} container\n * @param {string} tagname\n * @param {string} classname\n * @param {string} innerHTML\n *\n * @return {HTMLElement}\n */\nexport const createSingletonNode = ( container, tagname, classname, innerHTML='' ) => {\n\n\t// Find all nodes matching the description\n\tlet nodes = container.querySelectorAll( '.' + classname );\n\n\t// Check all matches to find one which is a direct child of\n\t// the specified container\n\tfor( let i = 0; i < nodes.length; i++ ) {\n\t\tlet testNode = nodes[i];\n\t\tif( testNode.parentNode === container ) {\n\t\t\treturn testNode;\n\t\t}\n\t}\n\n\t// If no node was found, create it now\n\tlet node = document.createElement( tagname );\n\tnode.className = classname;\n\tnode.innerHTML = innerHTML;\n\tcontainer.appendChild( node );\n\n\treturn node;\n\n}\n\n/**\n * Injects the given CSS styles into the DOM.\n *\n * @param {string} value\n */\nexport const createStyleSheet = ( value ) => {\n\n\tlet tag = document.createElement( 'style' );\n\ttag.type = 'text/css';\n\n\tif( value && value.length > 0 ) {\n\t\tif( tag.styleSheet ) {\n\t\t\ttag.styleSheet.cssText = value;\n\t\t}\n\t\telse {\n\t\t\ttag.appendChild( document.createTextNode( value ) );\n\t\t}\n\t}\n\n\tdocument.head.appendChild( tag );\n\n\treturn tag;\n\n}\n\n/**\n * Returns a key:value hash of all query params.\n */\nexport const getQueryHash = () => {\n\n\tlet query = {};\n\n\tlocation.search.replace( /[A-Z0-9]+?=([\\w\\.%-]*)/gi, a => {\n\t\tquery[ a.split( '=' ).shift() ] = a.split( '=' ).pop();\n\t} );\n\n\t// Basic deserialization\n\tfor( let i in query ) {\n\t\tlet value = query[ i ];\n\n\t\tquery[ i ] = deserialize( unescape( value ) );\n\t}\n\n\t// Do not accept new dependencies via query config to avoid\n\t// the potential of malicious script injection\n\tif( typeof query['dependencies'] !== 'undefined' ) delete query['dependencies'];\n\n\treturn query;\n\n}\n\n/**\n * Returns the remaining height within the parent of the\n * target element.\n *\n * remaining height = [ configured parent height ] - [ current parent height ]\n *\n * @param {HTMLElement} element\n * @param {number} [height]\n */\nexport const getRemainingHeight = ( element, height = 0 ) => {\n\n\tif( element ) {\n\t\tlet newHeight, oldHeight = element.style.height;\n\n\t\t// Change the .stretch element height to 0 in order find the height of all\n\t\t// the other elements\n\t\telement.style.height = '0px';\n\n\t\t// In Overview mode, the parent (.slide) height is set of 700px.\n\t\t// Restore it temporarily to its natural height.\n\t\telement.parentNode.style.height = 'auto';\n\n\t\tnewHeight = height - element.parentNode.offsetHeight;\n\n\t\t// Restore the old height, just in case\n\t\telement.style.height = oldHeight + 'px';\n\n\t\t// Clear the parent (.slide) height. .removeProperty works in IE9+\n\t\telement.parentNode.style.removeProperty('height');\n\n\t\treturn newHeight;\n\t}\n\n\treturn height;\n\n}\n\nconst fileExtensionToMimeMap = {\n\t'mp4': 'video/mp4',\n\t'm4a': 'video/mp4',\n\t'ogv': 'video/ogg',\n\t'mpeg': 'video/mpeg',\n\t'webm': 'video/webm'\n}\n\n/**\n * Guess the MIME type for common file formats.\n */\nexport const getMimeTypeFromFile = ( filename='' ) => {\n\treturn fileExtensionToMimeMap[filename.split('.').pop()]\n}","const UA = navigator.userAgent;\n\nexport const isMobile = /(iphone|ipod|ipad|android)/gi.test( UA ) ||\n\t\t\t\t\t\t( navigator.platform === 'MacIntel' && navigator.maxTouchPoints > 1 ); // iPadOS\n\nexport const isChrome = /chrome/i.test( UA ) && !/edge/i.test( UA );\n\nexport const isAndroid = /android/gi.test( UA );","/*\n * fitty v2.3.3 - Snugly resizes text to fit its parent container\n * Copyright (c) 2020 Rik Schennink (https://pqina.nl/)\n */\n'use strict';\n\nObject.defineProperty(exports, \"__esModule\", {\n value: true\n});\n\nvar _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; };\n\nexports.default = function (w) {\n\n // no window, early exit\n if (!w) return;\n\n // node list to array helper method\n var toArray = function toArray(nl) {\n return [].slice.call(nl);\n };\n\n // states\n var DrawState = {\n IDLE: 0,\n DIRTY_CONTENT: 1,\n DIRTY_LAYOUT: 2,\n DIRTY: 3\n };\n\n // all active fitty elements\n var fitties = [];\n\n // group all redraw calls till next frame, we cancel each frame request when a new one comes in. If no support for request animation frame, this is an empty function and supports for fitty stops.\n var redrawFrame = null;\n var requestRedraw = 'requestAnimationFrame' in w ? function () {\n w.cancelAnimationFrame(redrawFrame);\n redrawFrame = w.requestAnimationFrame(function () {\n return redraw(fitties.filter(function (f) {\n return f.dirty && f.active;\n }));\n });\n } : function () {};\n\n // sets all fitties to dirty so they are redrawn on the next redraw loop, then calls redraw\n var redrawAll = function redrawAll(type) {\n return function () {\n fitties.forEach(function (f) {\n return f.dirty = type;\n });\n requestRedraw();\n };\n };\n\n // redraws fitties so they nicely fit their parent container\n var redraw = function redraw(fitties) {\n\n // getting info from the DOM at this point should not trigger a reflow, let's gather as much intel as possible before triggering a reflow\n\n // check if styles of all fitties have been computed\n fitties.filter(function (f) {\n return !f.styleComputed;\n }).forEach(function (f) {\n f.styleComputed = computeStyle(f);\n });\n\n // restyle elements that require pre-styling, this triggers a reflow, please try to prevent by adding CSS rules (see docs)\n fitties.filter(shouldPreStyle).forEach(applyStyle);\n\n // we now determine which fitties should be redrawn\n var fittiesToRedraw = fitties.filter(shouldRedraw);\n\n // we calculate final styles for these fitties\n fittiesToRedraw.forEach(calculateStyles);\n\n // now we apply the calculated styles from our previous loop\n fittiesToRedraw.forEach(function (f) {\n applyStyle(f);\n markAsClean(f);\n });\n\n // now we dispatch events for all restyled fitties\n fittiesToRedraw.forEach(dispatchFitEvent);\n };\n\n var markAsClean = function markAsClean(f) {\n return f.dirty = DrawState.IDLE;\n };\n\n var calculateStyles = function calculateStyles(f) {\n\n // get available width from parent node\n f.availableWidth = f.element.parentNode.clientWidth;\n\n // the space our target element uses\n f.currentWidth = f.element.scrollWidth;\n\n // remember current font size\n f.previousFontSize = f.currentFontSize;\n\n // let's calculate the new font size\n f.currentFontSize = Math.min(Math.max(f.minSize, f.availableWidth / f.currentWidth * f.previousFontSize), f.maxSize);\n\n // if allows wrapping, only wrap when at minimum font size (otherwise would break container)\n f.whiteSpace = f.multiLine && f.currentFontSize === f.minSize ? 'normal' : 'nowrap';\n };\n\n // should always redraw if is not dirty layout, if is dirty layout, only redraw if size has changed\n var shouldRedraw = function shouldRedraw(f) {\n return f.dirty !== DrawState.DIRTY_LAYOUT || f.dirty === DrawState.DIRTY_LAYOUT && f.element.parentNode.clientWidth !== f.availableWidth;\n };\n\n // every fitty element is tested for invalid styles\n var computeStyle = function computeStyle(f) {\n\n // get style properties\n var style = w.getComputedStyle(f.element, null);\n\n // get current font size in pixels (if we already calculated it, use the calculated version)\n f.currentFontSize = parseFloat(style.getPropertyValue('font-size'));\n\n // get display type and wrap mode\n f.display = style.getPropertyValue('display');\n f.whiteSpace = style.getPropertyValue('white-space');\n };\n\n // determines if this fitty requires initial styling, can be prevented by applying correct styles through CSS\n var shouldPreStyle = function shouldPreStyle(f) {\n\n var preStyle = false;\n\n // if we already tested for prestyling we don't have to do it again\n if (f.preStyleTestCompleted) return false;\n\n // should have an inline style, if not, apply\n if (!/inline-/.test(f.display)) {\n preStyle = true;\n f.display = 'inline-block';\n }\n\n // to correctly calculate dimensions the element should have whiteSpace set to nowrap\n if (f.whiteSpace !== 'nowrap') {\n preStyle = true;\n f.whiteSpace = 'nowrap';\n }\n\n // we don't have to do this twice\n f.preStyleTestCompleted = true;\n\n return preStyle;\n };\n\n // apply styles to single fitty\n var applyStyle = function applyStyle(f) {\n f.element.style.whiteSpace = f.whiteSpace;\n f.element.style.display = f.display;\n f.element.style.fontSize = f.currentFontSize + 'px';\n };\n\n // dispatch a fit event on a fitty\n var dispatchFitEvent = function dispatchFitEvent(f) {\n f.element.dispatchEvent(new CustomEvent('fit', {\n detail: {\n oldValue: f.previousFontSize,\n newValue: f.currentFontSize,\n scaleFactor: f.currentFontSize / f.previousFontSize\n }\n }));\n };\n\n // fit method, marks the fitty as dirty and requests a redraw (this will also redraw any other fitty marked as dirty)\n var fit = function fit(f, type) {\n return function () {\n f.dirty = type;\n if (!f.active) return;\n requestRedraw();\n };\n };\n\n var init = function init(f) {\n\n // save some of the original CSS properties before we change them\n f.originalStyle = {\n whiteSpace: f.element.style.whiteSpace,\n display: f.element.style.display,\n fontSize: f.element.style.fontSize\n };\n\n // should we observe DOM mutations\n observeMutations(f);\n\n // this is a new fitty so we need to validate if it's styles are in order\n f.newbie = true;\n\n // because it's a new fitty it should also be dirty, we want it to redraw on the first loop\n f.dirty = true;\n\n // we want to be able to update this fitty\n fitties.push(f);\n };\n\n var destroy = function destroy(f) {\n return function () {\n\n // remove from fitties array\n fitties = fitties.filter(function (_) {\n return _.element !== f.element;\n });\n\n // stop observing DOM\n if (f.observeMutations) f.observer.disconnect();\n\n // reset the CSS properties we changes\n f.element.style.whiteSpace = f.originalStyle.whiteSpace;\n f.element.style.display = f.originalStyle.display;\n f.element.style.fontSize = f.originalStyle.fontSize;\n };\n };\n\n // add a new fitty, does not redraw said fitty\n var subscribe = function subscribe(f) {\n return function () {\n if (f.active) return;\n f.active = true;\n requestRedraw();\n };\n };\n\n // remove an existing fitty\n var unsubscribe = function unsubscribe(f) {\n return function () {\n return f.active = false;\n };\n };\n\n var observeMutations = function observeMutations(f) {\n\n // no observing?\n if (!f.observeMutations) return;\n\n // start observing mutations\n f.observer = new MutationObserver(fit(f, DrawState.DIRTY_CONTENT));\n\n // start observing\n f.observer.observe(f.element, f.observeMutations);\n };\n\n // default mutation observer settings\n var mutationObserverDefaultSetting = {\n subtree: true,\n childList: true,\n characterData: true\n };\n\n // default fitty options\n var defaultOptions = {\n minSize: 16,\n maxSize: 512,\n multiLine: true,\n observeMutations: 'MutationObserver' in w ? mutationObserverDefaultSetting : false\n };\n\n // array of elements in, fitty instances out\n function fittyCreate(elements, options) {\n\n // set options object\n var fittyOptions = _extends({}, defaultOptions, options);\n\n // create fitties\n var publicFitties = elements.map(function (element) {\n\n // create fitty instance\n var f = _extends({}, fittyOptions, {\n\n // internal options for this fitty\n element: element,\n active: true\n });\n\n // initialise this fitty\n init(f);\n\n // expose API\n return {\n element: element,\n fit: fit(f, DrawState.DIRTY),\n unfreeze: subscribe(f),\n freeze: unsubscribe(f),\n unsubscribe: destroy(f)\n };\n });\n\n // call redraw on newly initiated fitties\n requestRedraw();\n\n // expose fitties\n return publicFitties;\n }\n\n // fitty creation function\n function fitty(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n\n // if target is a string\n return typeof target === 'string' ?\n\n // treat it as a querySelector\n fittyCreate(toArray(document.querySelectorAll(target)), options) :\n\n // create single fitty\n fittyCreate([target], options)[0];\n }\n\n // handles viewport changes, redraws all fitties, but only does so after a timeout\n var resizeDebounce = null;\n var onWindowResized = function onWindowResized() {\n w.clearTimeout(resizeDebounce);\n resizeDebounce = w.setTimeout(redrawAll(DrawState.DIRTY_LAYOUT), fitty.observeWindowDelay);\n };\n\n // define observe window property, so when we set it to true or false events are automatically added and removed\n var events = ['resize', 'orientationchange'];\n Object.defineProperty(fitty, 'observeWindow', {\n set: function set(enabled) {\n var method = (enabled ? 'add' : 'remove') + 'EventListener';\n events.forEach(function (e) {\n w[method](e, onWindowResized);\n });\n }\n });\n\n // fitty global properties (by setting observeWindow to true the events above get added)\n fitty.observeWindow = true;\n fitty.observeWindowDelay = 100;\n\n // public fit all method, will force redraw no matter what\n fitty.fitAll = redrawAll(DrawState.DIRTY);\n\n // export our fitty function, we don't want to keep it to our selves\n return fitty;\n}(typeof window === 'undefined' ? null : window);","import { extend, queryAll, closest, getMimeTypeFromFile } from '../utils/util.js'\nimport { isMobile } from '../utils/device.js'\n\nimport fitty from 'fitty';\n\n/**\n * Handles loading, unloading and playback of slide\n * content such as images, videos and iframes.\n */\nexport default class SlideContent {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t\tthis.startEmbeddedIframe = this.startEmbeddedIframe.bind( this );\n\n\t}\n\n\t/**\n\t * Should the given element be preloaded?\n\t * Decides based on local element attributes and global config.\n\t *\n\t * @param {HTMLElement} element\n\t */\n\tshouldPreload( element ) {\n\n\t\t// Prefer an explicit global preload setting\n\t\tlet preload = this.Reveal.getConfig().preloadIframes;\n\n\t\t// If no global setting is available, fall back on the element's\n\t\t// own preload setting\n\t\tif( typeof preload !== 'boolean' ) {\n\t\t\tpreload = element.hasAttribute( 'data-preload' );\n\t\t}\n\n\t\treturn preload;\n\t}\n\n\t/**\n\t * Called when the given slide is within the configured view\n\t * distance. Shows the slide element and loads any content\n\t * that is set to load lazily (data-src).\n\t *\n\t * @param {HTMLElement} slide Slide to show\n\t */\n\tload( slide, options = {} ) {\n\n\t\t// Show the slide element\n\t\tslide.style.display = this.Reveal.getConfig().display;\n\n\t\t// Media elements with data-src attributes\n\t\tqueryAll( slide, 'img[data-src], video[data-src], audio[data-src], iframe[data-src]' ).forEach( element => {\n\t\t\tif( element.tagName !== 'IFRAME' || this.shouldPreload( element ) ) {\n\t\t\t\telement.setAttribute( 'src', element.getAttribute( 'data-src' ) );\n\t\t\t\telement.setAttribute( 'data-lazy-loaded', '' );\n\t\t\t\telement.removeAttribute( 'data-src' );\n\t\t\t}\n\t\t} );\n\n\t\t// Media elements with children\n\t\tqueryAll( slide, 'video, audio' ).forEach( media => {\n\t\t\tlet sources = 0;\n\n\t\t\tqueryAll( media, 'source[data-src]' ).forEach( source => {\n\t\t\t\tsource.setAttribute( 'src', source.getAttribute( 'data-src' ) );\n\t\t\t\tsource.removeAttribute( 'data-src' );\n\t\t\t\tsource.setAttribute( 'data-lazy-loaded', '' );\n\t\t\t\tsources += 1;\n\t\t\t} );\n\n\t\t\t// Enable inline video playback in mobile Safari\n\t\t\tif( isMobile && media.tagName === 'VIDEO' ) {\n\t\t\t\tmedia.setAttribute( 'playsinline', '' );\n\t\t\t}\n\n\t\t\t// If we rewrote sources for this video/audio element, we need\n\t\t\t// to manually tell it to load from its new origin\n\t\t\tif( sources > 0 ) {\n\t\t\t\tmedia.load();\n\t\t\t}\n\t\t} );\n\n\n\t\t// Show the corresponding background element\n\t\tlet background = slide.slideBackgroundElement;\n\t\tif( background ) {\n\t\t\tbackground.style.display = 'block';\n\n\t\t\tlet backgroundContent = slide.slideBackgroundContentElement;\n\t\t\tlet backgroundIframe = slide.getAttribute( 'data-background-iframe' );\n\n\t\t\t// If the background contains media, load it\n\t\t\tif( background.hasAttribute( 'data-loaded' ) === false ) {\n\t\t\t\tbackground.setAttribute( 'data-loaded', 'true' );\n\n\t\t\t\tlet backgroundImage = slide.getAttribute( 'data-background-image' ),\n\t\t\t\t\tbackgroundVideo = slide.getAttribute( 'data-background-video' ),\n\t\t\t\t\tbackgroundVideoLoop = slide.hasAttribute( 'data-background-video-loop' ),\n\t\t\t\t\tbackgroundVideoMuted = slide.hasAttribute( 'data-background-video-muted' );\n\n\t\t\t\t// Images\n\t\t\t\tif( backgroundImage ) {\n\t\t\t\t\t// base64\n\t\t\t\t\tif( /^data:/.test( backgroundImage.trim() ) ) {\n\t\t\t\t\t\tbackgroundContent.style.backgroundImage = `url(${backgroundImage.trim()})`;\n\t\t\t\t\t}\n\t\t\t\t\t// URL(s)\n\t\t\t\t\telse {\n\t\t\t\t\t\tbackgroundContent.style.backgroundImage = backgroundImage.split( ',' ).map( background => {\n\t\t\t\t\t\t\treturn `url(${encodeURI(background.trim())})`;\n\t\t\t\t\t\t}).join( ',' );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\t// Videos\n\t\t\t\telse if ( backgroundVideo && !this.Reveal.isSpeakerNotes() ) {\n\t\t\t\t\tlet video = document.createElement( 'video' );\n\n\t\t\t\t\tif( backgroundVideoLoop ) {\n\t\t\t\t\t\tvideo.setAttribute( 'loop', '' );\n\t\t\t\t\t}\n\n\t\t\t\t\tif( backgroundVideoMuted ) {\n\t\t\t\t\t\tvideo.muted = true;\n\t\t\t\t\t}\n\n\t\t\t\t\t// Enable inline playback in mobile Safari\n\t\t\t\t\t//\n\t\t\t\t\t// Mute is required for video to play when using\n\t\t\t\t\t// swipe gestures to navigate since they don't\n\t\t\t\t\t// count as direct user actions :'(\n\t\t\t\t\tif( isMobile ) {\n\t\t\t\t\t\tvideo.muted = true;\n\t\t\t\t\t\tvideo.setAttribute( 'playsinline', '' );\n\t\t\t\t\t}\n\n\t\t\t\t\t// Support comma separated lists of video sources\n\t\t\t\t\tbackgroundVideo.split( ',' ).forEach( source => {\n\t\t\t\t\t\tlet type = getMimeTypeFromFile( source );\n\t\t\t\t\t\tif( type ) {\n\t\t\t\t\t\t\tvideo.innerHTML += ``;\n\t\t\t\t\t\t}\n\t\t\t\t\t\telse {\n\t\t\t\t\t\t\tvideo.innerHTML += ``;\n\t\t\t\t\t\t}\n\t\t\t\t\t} );\n\n\t\t\t\t\tbackgroundContent.appendChild( video );\n\t\t\t\t}\n\t\t\t\t// Iframes\n\t\t\t\telse if( backgroundIframe && options.excludeIframes !== true ) {\n\t\t\t\t\tlet iframe = document.createElement( 'iframe' );\n\t\t\t\t\tiframe.setAttribute( 'allowfullscreen', '' );\n\t\t\t\t\tiframe.setAttribute( 'mozallowfullscreen', '' );\n\t\t\t\t\tiframe.setAttribute( 'webkitallowfullscreen', '' );\n\t\t\t\t\tiframe.setAttribute( 'allow', 'autoplay' );\n\n\t\t\t\t\tiframe.setAttribute( 'data-src', backgroundIframe );\n\n\t\t\t\t\tiframe.style.width = '100%';\n\t\t\t\t\tiframe.style.height = '100%';\n\t\t\t\t\tiframe.style.maxHeight = '100%';\n\t\t\t\t\tiframe.style.maxWidth = '100%';\n\n\t\t\t\t\tbackgroundContent.appendChild( iframe );\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Start loading preloadable iframes\n\t\t\tlet backgroundIframeElement = backgroundContent.querySelector( 'iframe[data-src]' );\n\t\t\tif( backgroundIframeElement ) {\n\n\t\t\t\t// Check if this iframe is eligible to be preloaded\n\t\t\t\tif( this.shouldPreload( background ) && !/autoplay=(1|true|yes)/gi.test( backgroundIframe ) ) {\n\t\t\t\t\tif( backgroundIframeElement.getAttribute( 'src' ) !== backgroundIframe ) {\n\t\t\t\t\t\tbackgroundIframeElement.setAttribute( 'src', backgroundIframe );\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t}\n\n\t\t}\n\n\t\tthis.layout( slide );\n\n\t}\n\n\t/**\n\t * Applies JS-dependent layout helpers for the given slide,\n\t * if there are any.\n\t */\n\tlayout( slide ) {\n\n\t\t// Autosize text with the r-fit-text class based on the\n\t\t// size of its container. This needs to happen after the\n\t\t// slide is visible in order to measure the text.\n\t\tArray.from( slide.querySelectorAll( '.r-fit-text' ) ).forEach( element => {\n\t\t\tfitty( element, {\n\t\t\t\tminSize: 24,\n\t\t\t\tmaxSize: this.Reveal.getConfig().height * 0.8,\n\t\t\t\tobserveMutations: false,\n\t\t\t\tobserveWindow: false\n\t\t\t} );\n\t\t} );\n\n\t}\n\n\t/**\n\t * Unloads and hides the given slide. This is called when the\n\t * slide is moved outside of the configured view distance.\n\t *\n\t * @param {HTMLElement} slide\n\t */\n\tunload( slide ) {\n\n\t\t// Hide the slide element\n\t\tslide.style.display = 'none';\n\n\t\t// Hide the corresponding background element\n\t\tlet background = this.Reveal.getSlideBackground( slide );\n\t\tif( background ) {\n\t\t\tbackground.style.display = 'none';\n\n\t\t\t// Unload any background iframes\n\t\t\tqueryAll( background, 'iframe[src]' ).forEach( element => {\n\t\t\t\telement.removeAttribute( 'src' );\n\t\t\t} );\n\t\t}\n\n\t\t// Reset lazy-loaded media elements with src attributes\n\t\tqueryAll( slide, 'video[data-lazy-loaded][src], audio[data-lazy-loaded][src], iframe[data-lazy-loaded][src]' ).forEach( element => {\n\t\t\telement.setAttribute( 'data-src', element.getAttribute( 'src' ) );\n\t\t\telement.removeAttribute( 'src' );\n\t\t} );\n\n\t\t// Reset lazy-loaded media elements with children\n\t\tqueryAll( slide, 'video[data-lazy-loaded] source[src], audio source[src]' ).forEach( source => {\n\t\t\tsource.setAttribute( 'data-src', source.getAttribute( 'src' ) );\n\t\t\tsource.removeAttribute( 'src' );\n\t\t} );\n\n\t}\n\n\t/**\n\t * Enforces origin-specific format rules for embedded media.\n\t */\n\tformatEmbeddedContent() {\n\n\t\tlet _appendParamToIframeSource = ( sourceAttribute, sourceURL, param ) => {\n\t\t\tqueryAll( this.Reveal.getSlidesElement(), 'iframe['+ sourceAttribute +'*=\"'+ sourceURL +'\"]' ).forEach( el => {\n\t\t\t\tlet src = el.getAttribute( sourceAttribute );\n\t\t\t\tif( src && src.indexOf( param ) === -1 ) {\n\t\t\t\t\tel.setAttribute( sourceAttribute, src + ( !/\\?/.test( src ) ? '?' : '&' ) + param );\n\t\t\t\t}\n\t\t\t});\n\t\t};\n\n\t\t// YouTube frames must include \"?enablejsapi=1\"\n\t\t_appendParamToIframeSource( 'src', 'youtube.com/embed/', 'enablejsapi=1' );\n\t\t_appendParamToIframeSource( 'data-src', 'youtube.com/embed/', 'enablejsapi=1' );\n\n\t\t// Vimeo frames must include \"?api=1\"\n\t\t_appendParamToIframeSource( 'src', 'player.vimeo.com/', 'api=1' );\n\t\t_appendParamToIframeSource( 'data-src', 'player.vimeo.com/', 'api=1' );\n\n\t}\n\n\t/**\n\t * Start playback of any embedded content inside of\n\t * the given element.\n\t *\n\t * @param {HTMLElement} element\n\t */\n\tstartEmbeddedContent( element ) {\n\n\t\tif( element && !this.Reveal.isSpeakerNotes() ) {\n\n\t\t\t// Restart GIFs\n\t\t\tqueryAll( element, 'img[src$=\".gif\"]' ).forEach( el => {\n\t\t\t\t// Setting the same unchanged source like this was confirmed\n\t\t\t\t// to work in Chrome, FF & Safari\n\t\t\t\tel.setAttribute( 'src', el.getAttribute( 'src' ) );\n\t\t\t} );\n\n\t\t\t// HTML5 media elements\n\t\t\tqueryAll( element, 'video, audio' ).forEach( el => {\n\t\t\t\tif( closest( el, '.fragment' ) && !closest( el, '.fragment.visible' ) ) {\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\t// Prefer an explicit global autoplay setting\n\t\t\t\tlet autoplay = this.Reveal.getConfig().autoPlayMedia;\n\n\t\t\t\t// If no global setting is available, fall back on the element's\n\t\t\t\t// own autoplay setting\n\t\t\t\tif( typeof autoplay !== 'boolean' ) {\n\t\t\t\t\tautoplay = el.hasAttribute( 'data-autoplay' ) || !!closest( el, '.slide-background' );\n\t\t\t\t}\n\n\t\t\t\tif( autoplay && typeof el.play === 'function' ) {\n\n\t\t\t\t\t// If the media is ready, start playback\n\t\t\t\t\tif( el.readyState > 1 ) {\n\t\t\t\t\t\tthis.startEmbeddedMedia( { target: el } );\n\t\t\t\t\t}\n\t\t\t\t\t// Mobile devices never fire a loaded event so instead\n\t\t\t\t\t// of waiting, we initiate playback\n\t\t\t\t\telse if( isMobile ) {\n\t\t\t\t\t\tlet promise = el.play();\n\n\t\t\t\t\t\t// If autoplay does not work, ensure that the controls are visible so\n\t\t\t\t\t\t// that the viewer can start the media on their own\n\t\t\t\t\t\tif( promise && typeof promise.catch === 'function' && el.controls === false ) {\n\t\t\t\t\t\t\tpromise.catch( () => {\n\t\t\t\t\t\t\t\tel.controls = true;\n\n\t\t\t\t\t\t\t\t// Once the video does start playing, hide the controls again\n\t\t\t\t\t\t\t\tel.addEventListener( 'play', () => {\n\t\t\t\t\t\t\t\t\tel.controls = false;\n\t\t\t\t\t\t\t\t} );\n\t\t\t\t\t\t\t} );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\t// If the media isn't loaded, wait before playing\n\t\t\t\t\telse {\n\t\t\t\t\t\tel.removeEventListener( 'loadeddata', this.startEmbeddedMedia ); // remove first to avoid dupes\n\t\t\t\t\t\tel.addEventListener( 'loadeddata', this.startEmbeddedMedia );\n\t\t\t\t\t}\n\n\t\t\t\t}\n\t\t\t} );\n\n\t\t\t// Normal iframes\n\t\t\tqueryAll( element, 'iframe[src]' ).forEach( el => {\n\t\t\t\tif( closest( el, '.fragment' ) && !closest( el, '.fragment.visible' ) ) {\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\tthis.startEmbeddedIframe( { target: el } );\n\t\t\t} );\n\n\t\t\t// Lazy loading iframes\n\t\t\tqueryAll( element, 'iframe[data-src]' ).forEach( el => {\n\t\t\t\tif( closest( el, '.fragment' ) && !closest( el, '.fragment.visible' ) ) {\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\tif( el.getAttribute( 'src' ) !== el.getAttribute( 'data-src' ) ) {\n\t\t\t\t\tel.removeEventListener( 'load', this.startEmbeddedIframe ); // remove first to avoid dupes\n\t\t\t\t\tel.addEventListener( 'load', this.startEmbeddedIframe );\n\t\t\t\t\tel.setAttribute( 'src', el.getAttribute( 'data-src' ) );\n\t\t\t\t}\n\t\t\t} );\n\n\t\t}\n\n\t}\n\n\t/**\n\t * Starts playing an embedded video/audio element after\n\t * it has finished loading.\n\t *\n\t * @param {object} event\n\t */\n\tstartEmbeddedMedia( event ) {\n\n\t\tlet isAttachedToDOM = !!closest( event.target, 'html' ),\n\t\t\tisVisible \t\t= !!closest( event.target, '.present' );\n\n\t\tif( isAttachedToDOM && isVisible ) {\n\t\t\tevent.target.currentTime = 0;\n\t\t\tevent.target.play();\n\t\t}\n\n\t\tevent.target.removeEventListener( 'loadeddata', this.startEmbeddedMedia );\n\n\t}\n\n\t/**\n\t * \"Starts\" the content of an embedded iframe using the\n\t * postMessage API.\n\t *\n\t * @param {object} event\n\t */\n\tstartEmbeddedIframe( event ) {\n\n\t\tlet iframe = event.target;\n\n\t\tif( iframe && iframe.contentWindow ) {\n\n\t\t\tlet isAttachedToDOM = !!closest( event.target, 'html' ),\n\t\t\t\tisVisible \t\t= !!closest( event.target, '.present' );\n\n\t\t\tif( isAttachedToDOM && isVisible ) {\n\n\t\t\t\t// Prefer an explicit global autoplay setting\n\t\t\t\tlet autoplay = this.Reveal.getConfig().autoPlayMedia;\n\n\t\t\t\t// If no global setting is available, fall back on the element's\n\t\t\t\t// own autoplay setting\n\t\t\t\tif( typeof autoplay !== 'boolean' ) {\n\t\t\t\t\tautoplay = iframe.hasAttribute( 'data-autoplay' ) || !!closest( iframe, '.slide-background' );\n\t\t\t\t}\n\n\t\t\t\t// YouTube postMessage API\n\t\t\t\tif( /youtube\\.com\\/embed\\//.test( iframe.getAttribute( 'src' ) ) && autoplay ) {\n\t\t\t\t\tiframe.contentWindow.postMessage( '{\"event\":\"command\",\"func\":\"playVideo\",\"args\":\"\"}', '*' );\n\t\t\t\t}\n\t\t\t\t// Vimeo postMessage API\n\t\t\t\telse if( /player\\.vimeo\\.com\\//.test( iframe.getAttribute( 'src' ) ) && autoplay ) {\n\t\t\t\t\tiframe.contentWindow.postMessage( '{\"method\":\"play\"}', '*' );\n\t\t\t\t}\n\t\t\t\t// Generic postMessage API\n\t\t\t\telse {\n\t\t\t\t\tiframe.contentWindow.postMessage( 'slide:start', '*' );\n\t\t\t\t}\n\n\t\t\t}\n\n\t\t}\n\n\t}\n\n\t/**\n\t * Stop playback of any embedded content inside of\n\t * the targeted slide.\n\t *\n\t * @param {HTMLElement} element\n\t */\n\tstopEmbeddedContent( element, options = {} ) {\n\n\t\toptions = extend( {\n\t\t\t// Defaults\n\t\t\tunloadIframes: true\n\t\t}, options );\n\n\t\tif( element && element.parentNode ) {\n\t\t\t// HTML5 media elements\n\t\t\tqueryAll( element, 'video, audio' ).forEach( el => {\n\t\t\t\tif( !el.hasAttribute( 'data-ignore' ) && typeof el.pause === 'function' ) {\n\t\t\t\t\tel.setAttribute('data-paused-by-reveal', '');\n\t\t\t\t\tel.pause();\n\t\t\t\t}\n\t\t\t} );\n\n\t\t\t// Generic postMessage API for non-lazy loaded iframes\n\t\t\tqueryAll( element, 'iframe' ).forEach( el => {\n\t\t\t\tif( el.contentWindow ) el.contentWindow.postMessage( 'slide:stop', '*' );\n\t\t\t\tel.removeEventListener( 'load', this.startEmbeddedIframe );\n\t\t\t});\n\n\t\t\t// YouTube postMessage API\n\t\t\tqueryAll( element, 'iframe[src*=\"youtube.com/embed/\"]' ).forEach( el => {\n\t\t\t\tif( !el.hasAttribute( 'data-ignore' ) && el.contentWindow && typeof el.contentWindow.postMessage === 'function' ) {\n\t\t\t\t\tel.contentWindow.postMessage( '{\"event\":\"command\",\"func\":\"pauseVideo\",\"args\":\"\"}', '*' );\n\t\t\t\t}\n\t\t\t});\n\n\t\t\t// Vimeo postMessage API\n\t\t\tqueryAll( element, 'iframe[src*=\"player.vimeo.com/\"]' ).forEach( el => {\n\t\t\t\tif( !el.hasAttribute( 'data-ignore' ) && el.contentWindow && typeof el.contentWindow.postMessage === 'function' ) {\n\t\t\t\t\tel.contentWindow.postMessage( '{\"method\":\"pause\"}', '*' );\n\t\t\t\t}\n\t\t\t});\n\n\t\t\tif( options.unloadIframes === true ) {\n\t\t\t\t// Unload lazy-loaded iframes\n\t\t\t\tqueryAll( element, 'iframe[data-src]' ).forEach( el => {\n\t\t\t\t\t// Only removing the src doesn't actually unload the frame\n\t\t\t\t\t// in all browsers (Firefox) so we set it to blank first\n\t\t\t\t\tel.setAttribute( 'src', 'about:blank' );\n\t\t\t\t\tel.removeAttribute( 'src' );\n\t\t\t\t} );\n\t\t\t}\n\t\t}\n\n\t}\n\n}\n","/**\n * Handles the display of reveal.js' optional slide number.\n */\nexport default class SlideNumber {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t}\n\n\trender() {\n\n\t\tthis.element = document.createElement( 'div' );\n\t\tthis.element.className = 'slide-number';\n\t\tthis.Reveal.getRevealElement().appendChild( this.element );\n\n\t}\n\n\t/**\n\t * Called when the reveal.js config is updated.\n\t */\n\tconfigure( config, oldConfig ) {\n\n\t\tlet slideNumberDisplay = 'none';\n\t\tif( config.slideNumber && !this.Reveal.isPrintingPDF() ) {\n\t\t\tif( config.showSlideNumber === 'all' ) {\n\t\t\t\tslideNumberDisplay = 'block';\n\t\t\t}\n\t\t\telse if( config.showSlideNumber === 'speaker' && this.Reveal.isSpeakerNotes() ) {\n\t\t\t\tslideNumberDisplay = 'block';\n\t\t\t}\n\t\t}\n\n\t\tthis.element.style.display = slideNumberDisplay;\n\n\t}\n\n\t/**\n\t * Updates the slide number to match the current slide.\n\t */\n\tupdate() {\n\n\t\t// Update slide number if enabled\n\t\tif( this.Reveal.getConfig().slideNumber && this.element ) {\n\t\t\tthis.element.innerHTML = this.getSlideNumber();\n\t\t}\n\n\t}\n\n\t/**\n\t * Returns the HTML string corresponding to the current slide\n\t * number, including formatting.\n\t */\n\tgetSlideNumber( slide = this.Reveal.getCurrentSlide() ) {\n\n\t\tlet config = this.Reveal.getConfig();\n\t\tlet value;\n\t\tlet format = 'h.v';\n\n\t\tif ( typeof config.slideNumber === 'function' ) {\n\t\t\tvalue = config.slideNumber( slide );\n\t\t} else {\n\t\t\t// Check if a custom number format is available\n\t\t\tif( typeof config.slideNumber === 'string' ) {\n\t\t\t\tformat = config.slideNumber;\n\t\t\t}\n\n\t\t\t// If there are ONLY vertical slides in this deck, always use\n\t\t\t// a flattened slide number\n\t\t\tif( !/c/.test( format ) && this.Reveal.getHorizontalSlides().length === 1 ) {\n\t\t\t\tformat = 'c';\n\t\t\t}\n\n\t\t\t// Offset the current slide number by 1 to make it 1-indexed\n\t\t\tlet horizontalOffset = slide && slide.dataset.visibility === 'uncounted' ? 0 : 1;\n\n\t\t\tvalue = [];\n\t\t\tswitch( format ) {\n\t\t\t\tcase 'c':\n\t\t\t\t\tvalue.push( this.Reveal.getSlidePastCount( slide ) + horizontalOffset );\n\t\t\t\t\tbreak;\n\t\t\t\tcase 'c/t':\n\t\t\t\t\tvalue.push( this.Reveal.getSlidePastCount( slide ) + horizontalOffset, '/', this.Reveal.getTotalSlides() );\n\t\t\t\t\tbreak;\n\t\t\t\tdefault:\n\t\t\t\t\tlet indices = this.Reveal.getIndices( slide );\n\t\t\t\t\tvalue.push( indices.h + horizontalOffset );\n\t\t\t\t\tlet sep = format === 'h/v' ? '/' : '.';\n\t\t\t\t\tif( this.Reveal.isVerticalSlide( slide ) ) value.push( sep, indices.v + 1 );\n\t\t\t}\n\t\t}\n\n\t\tlet url = '#' + this.Reveal.location.getHash( slide );\n\t\treturn this.formatNumber( value[0], value[1], value[2], url );\n\n\t}\n\n\t/**\n\t * Applies HTML formatting to a slide number before it's\n\t * written to the DOM.\n\t *\n\t * @param {number} a Current slide\n\t * @param {string} delimiter Character to separate slide numbers\n\t * @param {(number|*)} b Total slides\n\t * @param {HTMLElement} [url='#'+locationHash()] The url to link to\n\t * @return {string} HTML string fragment\n\t */\n\tformatNumber( a, delimiter, b, url = '#' + this.Reveal.location.getHash() ) {\n\n\t\tif( typeof b === 'number' && !isNaN( b ) ) {\n\t\t\treturn `\n\t\t\t\t\t${a}\n\t\t\t\t\t${delimiter}\n\t\t\t\t\t${b}\n\t\t\t\t\t`;\n\t\t}\n\t\telse {\n\t\t\treturn `\n\t\t\t\t\t${a}\n\t\t\t\t\t`;\n\t\t}\n\n\t}\n\n\tdestroy() {\n\n\t\tthis.element.remove();\n\n\t}\n\n}","/**\n * Converts various color input formats to an {r:0,g:0,b:0} object.\n *\n * @param {string} color The string representation of a color\n * @example\n * colorToRgb('#000');\n * @example\n * colorToRgb('#000000');\n * @example\n * colorToRgb('rgb(0,0,0)');\n * @example\n * colorToRgb('rgba(0,0,0)');\n *\n * @return {{r: number, g: number, b: number, [a]: number}|null}\n */\nexport const colorToRgb = ( color ) => {\n\n\tlet hex3 = color.match( /^#([0-9a-f]{3})$/i );\n\tif( hex3 && hex3[1] ) {\n\t\thex3 = hex3[1];\n\t\treturn {\n\t\t\tr: parseInt( hex3.charAt( 0 ), 16 ) * 0x11,\n\t\t\tg: parseInt( hex3.charAt( 1 ), 16 ) * 0x11,\n\t\t\tb: parseInt( hex3.charAt( 2 ), 16 ) * 0x11\n\t\t};\n\t}\n\n\tlet hex6 = color.match( /^#([0-9a-f]{6})$/i );\n\tif( hex6 && hex6[1] ) {\n\t\thex6 = hex6[1];\n\t\treturn {\n\t\t\tr: parseInt( hex6.slice( 0, 2 ), 16 ),\n\t\t\tg: parseInt( hex6.slice( 2, 4 ), 16 ),\n\t\t\tb: parseInt( hex6.slice( 4, 6 ), 16 )\n\t\t};\n\t}\n\n\tlet rgb = color.match( /^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)$/i );\n\tif( rgb ) {\n\t\treturn {\n\t\t\tr: parseInt( rgb[1], 10 ),\n\t\t\tg: parseInt( rgb[2], 10 ),\n\t\t\tb: parseInt( rgb[3], 10 )\n\t\t};\n\t}\n\n\tlet rgba = color.match( /^rgba\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\,\\s*([\\d]+|[\\d]*.[\\d]+)\\s*\\)$/i );\n\tif( rgba ) {\n\t\treturn {\n\t\t\tr: parseInt( rgba[1], 10 ),\n\t\t\tg: parseInt( rgba[2], 10 ),\n\t\t\tb: parseInt( rgba[3], 10 ),\n\t\t\ta: parseFloat( rgba[4] )\n\t\t};\n\t}\n\n\treturn null;\n\n}\n\n/**\n * Calculates brightness on a scale of 0-255.\n *\n * @param {string} color See colorToRgb for supported formats.\n * @see {@link colorToRgb}\n */\nexport const colorBrightness = ( color ) => {\n\n\tif( typeof color === 'string' ) color = colorToRgb( color );\n\n\tif( color ) {\n\t\treturn ( color.r * 299 + color.g * 587 + color.b * 114 ) / 1000;\n\t}\n\n\treturn null;\n\n}","import { queryAll } from '../utils/util.js'\nimport { colorToRgb, colorBrightness } from '../utils/color.js'\n\n/**\n * Creates and updates slide backgrounds.\n */\nexport default class Backgrounds {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t}\n\n\trender() {\n\n\t\tthis.element = document.createElement( 'div' );\n\t\tthis.element.className = 'backgrounds';\n\t\tthis.Reveal.getRevealElement().appendChild( this.element );\n\n\t}\n\n\t/**\n\t * Creates the slide background elements and appends them\n\t * to the background container. One element is created per\n\t * slide no matter if the given slide has visible background.\n\t */\n\tcreate() {\n\n\t\t// Clear prior backgrounds\n\t\tthis.element.innerHTML = '';\n\t\tthis.element.classList.add( 'no-transition' );\n\n\t\t// Iterate over all horizontal slides\n\t\tthis.Reveal.getHorizontalSlides().forEach( slideh => {\n\n\t\t\tlet backgroundStack = this.createBackground( slideh, this.element );\n\n\t\t\t// Iterate over all vertical slides\n\t\t\tqueryAll( slideh, 'section' ).forEach( slidev => {\n\n\t\t\t\tthis.createBackground( slidev, backgroundStack );\n\n\t\t\t\tbackgroundStack.classList.add( 'stack' );\n\n\t\t\t} );\n\n\t\t} );\n\n\t\t// Add parallax background if specified\n\t\tif( this.Reveal.getConfig().parallaxBackgroundImage ) {\n\n\t\t\tthis.element.style.backgroundImage = 'url(\"' + this.Reveal.getConfig().parallaxBackgroundImage + '\")';\n\t\t\tthis.element.style.backgroundSize = this.Reveal.getConfig().parallaxBackgroundSize;\n\t\t\tthis.element.style.backgroundRepeat = this.Reveal.getConfig().parallaxBackgroundRepeat;\n\t\t\tthis.element.style.backgroundPosition = this.Reveal.getConfig().parallaxBackgroundPosition;\n\n\t\t\t// Make sure the below properties are set on the element - these properties are\n\t\t\t// needed for proper transitions to be set on the element via CSS. To remove\n\t\t\t// annoying background slide-in effect when the presentation starts, apply\n\t\t\t// these properties after short time delay\n\t\t\tsetTimeout( () => {\n\t\t\t\tthis.Reveal.getRevealElement().classList.add( 'has-parallax-background' );\n\t\t\t}, 1 );\n\n\t\t}\n\t\telse {\n\n\t\t\tthis.element.style.backgroundImage = '';\n\t\t\tthis.Reveal.getRevealElement().classList.remove( 'has-parallax-background' );\n\n\t\t}\n\n\t}\n\n\t/**\n\t * Creates a background for the given slide.\n\t *\n\t * @param {HTMLElement} slide\n\t * @param {HTMLElement} container The element that the background\n\t * should be appended to\n\t * @return {HTMLElement} New background div\n\t */\n\tcreateBackground( slide, container ) {\n\n\t\t// Main slide background element\n\t\tlet element = document.createElement( 'div' );\n\t\telement.className = 'slide-background ' + slide.className.replace( /present|past|future/, '' );\n\n\t\t// Inner background element that wraps images/videos/iframes\n\t\tlet contentElement = document.createElement( 'div' );\n\t\tcontentElement.className = 'slide-background-content';\n\n\t\telement.appendChild( contentElement );\n\t\tcontainer.appendChild( element );\n\n\t\tslide.slideBackgroundElement = element;\n\t\tslide.slideBackgroundContentElement = contentElement;\n\n\t\t// Syncs the background to reflect all current background settings\n\t\tthis.sync( slide );\n\n\t\treturn element;\n\n\t}\n\n\t/**\n\t * Renders all of the visual properties of a slide background\n\t * based on the various background attributes.\n\t *\n\t * @param {HTMLElement} slide\n\t */\n\tsync( slide ) {\n\n\t\tconst element = slide.slideBackgroundElement,\n\t\t\tcontentElement = slide.slideBackgroundContentElement;\n\n\t\tconst data = {\n\t\t\tbackground: slide.getAttribute( 'data-background' ),\n\t\t\tbackgroundSize: slide.getAttribute( 'data-background-size' ),\n\t\t\tbackgroundImage: slide.getAttribute( 'data-background-image' ),\n\t\t\tbackgroundVideo: slide.getAttribute( 'data-background-video' ),\n\t\t\tbackgroundIframe: slide.getAttribute( 'data-background-iframe' ),\n\t\t\tbackgroundColor: slide.getAttribute( 'data-background-color' ),\n\t\t\tbackgroundRepeat: slide.getAttribute( 'data-background-repeat' ),\n\t\t\tbackgroundPosition: slide.getAttribute( 'data-background-position' ),\n\t\t\tbackgroundTransition: slide.getAttribute( 'data-background-transition' ),\n\t\t\tbackgroundOpacity: slide.getAttribute( 'data-background-opacity' ),\n\t\t};\n\n\t\tconst dataPreload = slide.hasAttribute( 'data-preload' );\n\n\t\t// Reset the prior background state in case this is not the\n\t\t// initial sync\n\t\tslide.classList.remove( 'has-dark-background' );\n\t\tslide.classList.remove( 'has-light-background' );\n\n\t\telement.removeAttribute( 'data-loaded' );\n\t\telement.removeAttribute( 'data-background-hash' );\n\t\telement.removeAttribute( 'data-background-size' );\n\t\telement.removeAttribute( 'data-background-transition' );\n\t\telement.style.backgroundColor = '';\n\n\t\tcontentElement.style.backgroundSize = '';\n\t\tcontentElement.style.backgroundRepeat = '';\n\t\tcontentElement.style.backgroundPosition = '';\n\t\tcontentElement.style.backgroundImage = '';\n\t\tcontentElement.style.opacity = '';\n\t\tcontentElement.innerHTML = '';\n\n\t\tif( data.background ) {\n\t\t\t// Auto-wrap image urls in url(...)\n\t\t\tif( /^(http|file|\\/\\/)/gi.test( data.background ) || /\\.(svg|png|jpg|jpeg|gif|bmp)([?#\\s]|$)/gi.test( data.background ) ) {\n\t\t\t\tslide.setAttribute( 'data-background-image', data.background );\n\t\t\t}\n\t\t\telse {\n\t\t\t\telement.style.background = data.background;\n\t\t\t}\n\t\t}\n\n\t\t// Create a hash for this combination of background settings.\n\t\t// This is used to determine when two slide backgrounds are\n\t\t// the same.\n\t\tif( data.background || data.backgroundColor || data.backgroundImage || data.backgroundVideo || data.backgroundIframe ) {\n\t\t\telement.setAttribute( 'data-background-hash', data.background +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata.backgroundSize +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata.backgroundImage +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata.backgroundVideo +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata.backgroundIframe +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata.backgroundColor +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata.backgroundRepeat +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata.backgroundPosition +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata.backgroundTransition +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata.backgroundOpacity );\n\t\t}\n\n\t\t// Additional and optional background properties\n\t\tif( data.backgroundSize ) element.setAttribute( 'data-background-size', data.backgroundSize );\n\t\tif( data.backgroundColor ) element.style.backgroundColor = data.backgroundColor;\n\t\tif( data.backgroundTransition ) element.setAttribute( 'data-background-transition', data.backgroundTransition );\n\n\t\tif( dataPreload ) element.setAttribute( 'data-preload', '' );\n\n\t\t// Background image options are set on the content wrapper\n\t\tif( data.backgroundSize ) contentElement.style.backgroundSize = data.backgroundSize;\n\t\tif( data.backgroundRepeat ) contentElement.style.backgroundRepeat = data.backgroundRepeat;\n\t\tif( data.backgroundPosition ) contentElement.style.backgroundPosition = data.backgroundPosition;\n\t\tif( data.backgroundOpacity ) contentElement.style.opacity = data.backgroundOpacity;\n\n\t\t// If this slide has a background color, we add a class that\n\t\t// signals if it is light or dark. If the slide has no background\n\t\t// color, no class will be added\n\t\tlet contrastColor = data.backgroundColor;\n\n\t\t// If no bg color was found, or it cannot be converted by colorToRgb, check the computed background\n\t\tif( !contrastColor || !colorToRgb( contrastColor ) ) {\n\t\t\tlet computedBackgroundStyle = window.getComputedStyle( element );\n\t\t\tif( computedBackgroundStyle && computedBackgroundStyle.backgroundColor ) {\n\t\t\t\tcontrastColor = computedBackgroundStyle.backgroundColor;\n\t\t\t}\n\t\t}\n\n\t\tif( contrastColor ) {\n\t\t\tconst rgb = colorToRgb( contrastColor );\n\n\t\t\t// Ignore fully transparent backgrounds. Some browsers return\n\t\t\t// rgba(0,0,0,0) when reading the computed background color of\n\t\t\t// an element with no background\n\t\t\tif( rgb && rgb.a !== 0 ) {\n\t\t\t\tif( colorBrightness( contrastColor ) < 128 ) {\n\t\t\t\t\tslide.classList.add( 'has-dark-background' );\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tslide.classList.add( 'has-light-background' );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t}\n\n\t/**\n\t * Updates the background elements to reflect the current\n\t * slide.\n\t *\n\t * @param {boolean} includeAll If true, the backgrounds of\n\t * all vertical slides (not just the present) will be updated.\n\t */\n\tupdate( includeAll = false ) {\n\n\t\tlet currentSlide = this.Reveal.getCurrentSlide();\n\t\tlet indices = this.Reveal.getIndices();\n\n\t\tlet currentBackground = null;\n\n\t\t// Reverse past/future classes when in RTL mode\n\t\tlet horizontalPast = this.Reveal.getConfig().rtl ? 'future' : 'past',\n\t\t\thorizontalFuture = this.Reveal.getConfig().rtl ? 'past' : 'future';\n\n\t\t// Update the classes of all backgrounds to match the\n\t\t// states of their slides (past/present/future)\n\t\tArray.from( this.element.childNodes ).forEach( ( backgroundh, h ) => {\n\n\t\t\tbackgroundh.classList.remove( 'past', 'present', 'future' );\n\n\t\t\tif( h < indices.h ) {\n\t\t\t\tbackgroundh.classList.add( horizontalPast );\n\t\t\t}\n\t\t\telse if ( h > indices.h ) {\n\t\t\t\tbackgroundh.classList.add( horizontalFuture );\n\t\t\t}\n\t\t\telse {\n\t\t\t\tbackgroundh.classList.add( 'present' );\n\n\t\t\t\t// Store a reference to the current background element\n\t\t\t\tcurrentBackground = backgroundh;\n\t\t\t}\n\n\t\t\tif( includeAll || h === indices.h ) {\n\t\t\t\tqueryAll( backgroundh, '.slide-background' ).forEach( ( backgroundv, v ) => {\n\n\t\t\t\t\tbackgroundv.classList.remove( 'past', 'present', 'future' );\n\n\t\t\t\t\tif( v < indices.v ) {\n\t\t\t\t\t\tbackgroundv.classList.add( 'past' );\n\t\t\t\t\t}\n\t\t\t\t\telse if ( v > indices.v ) {\n\t\t\t\t\t\tbackgroundv.classList.add( 'future' );\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tbackgroundv.classList.add( 'present' );\n\n\t\t\t\t\t\t// Only if this is the present horizontal and vertical slide\n\t\t\t\t\t\tif( h === indices.h ) currentBackground = backgroundv;\n\t\t\t\t\t}\n\n\t\t\t\t} );\n\t\t\t}\n\n\t\t} );\n\n\t\t// Stop content inside of previous backgrounds\n\t\tif( this.previousBackground ) {\n\n\t\t\tthis.Reveal.slideContent.stopEmbeddedContent( this.previousBackground, { unloadIframes: !this.Reveal.slideContent.shouldPreload( this.previousBackground ) } );\n\n\t\t}\n\n\t\t// Start content in the current background\n\t\tif( currentBackground ) {\n\n\t\t\tthis.Reveal.slideContent.startEmbeddedContent( currentBackground );\n\n\t\t\tlet currentBackgroundContent = currentBackground.querySelector( '.slide-background-content' );\n\t\t\tif( currentBackgroundContent ) {\n\n\t\t\t\tlet backgroundImageURL = currentBackgroundContent.style.backgroundImage || '';\n\n\t\t\t\t// Restart GIFs (doesn't work in Firefox)\n\t\t\t\tif( /\\.gif/i.test( backgroundImageURL ) ) {\n\t\t\t\t\tcurrentBackgroundContent.style.backgroundImage = '';\n\t\t\t\t\twindow.getComputedStyle( currentBackgroundContent ).opacity;\n\t\t\t\t\tcurrentBackgroundContent.style.backgroundImage = backgroundImageURL;\n\t\t\t\t}\n\n\t\t\t}\n\n\t\t\t// Don't transition between identical backgrounds. This\n\t\t\t// prevents unwanted flicker.\n\t\t\tlet previousBackgroundHash = this.previousBackground ? this.previousBackground.getAttribute( 'data-background-hash' ) : null;\n\t\t\tlet currentBackgroundHash = currentBackground.getAttribute( 'data-background-hash' );\n\t\t\tif( currentBackgroundHash && currentBackgroundHash === previousBackgroundHash && currentBackground !== this.previousBackground ) {\n\t\t\t\tthis.element.classList.add( 'no-transition' );\n\t\t\t}\n\n\t\t\tthis.previousBackground = currentBackground;\n\n\t\t}\n\n\t\t// If there's a background brightness flag for this slide,\n\t\t// bubble it to the .reveal container\n\t\tif( currentSlide ) {\n\t\t\t[ 'has-light-background', 'has-dark-background' ].forEach( classToBubble => {\n\t\t\t\tif( currentSlide.classList.contains( classToBubble ) ) {\n\t\t\t\t\tthis.Reveal.getRevealElement().classList.add( classToBubble );\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tthis.Reveal.getRevealElement().classList.remove( classToBubble );\n\t\t\t\t}\n\t\t\t}, this );\n\t\t}\n\n\t\t// Allow the first background to apply without transition\n\t\tsetTimeout( () => {\n\t\t\tthis.element.classList.remove( 'no-transition' );\n\t\t}, 1 );\n\n\t}\n\n\t/**\n\t * Updates the position of the parallax background based\n\t * on the current slide index.\n\t */\n\tupdateParallax() {\n\n\t\tlet indices = this.Reveal.getIndices();\n\n\t\tif( this.Reveal.getConfig().parallaxBackgroundImage ) {\n\n\t\t\tlet horizontalSlides = this.Reveal.getHorizontalSlides(),\n\t\t\t\tverticalSlides = this.Reveal.getVerticalSlides();\n\n\t\t\tlet backgroundSize = this.element.style.backgroundSize.split( ' ' ),\n\t\t\t\tbackgroundWidth, backgroundHeight;\n\n\t\t\tif( backgroundSize.length === 1 ) {\n\t\t\t\tbackgroundWidth = backgroundHeight = parseInt( backgroundSize[0], 10 );\n\t\t\t}\n\t\t\telse {\n\t\t\t\tbackgroundWidth = parseInt( backgroundSize[0], 10 );\n\t\t\t\tbackgroundHeight = parseInt( backgroundSize[1], 10 );\n\t\t\t}\n\n\t\t\tlet slideWidth = this.element.offsetWidth,\n\t\t\t\thorizontalSlideCount = horizontalSlides.length,\n\t\t\t\thorizontalOffsetMultiplier,\n\t\t\t\thorizontalOffset;\n\n\t\t\tif( typeof this.Reveal.getConfig().parallaxBackgroundHorizontal === 'number' ) {\n\t\t\t\thorizontalOffsetMultiplier = this.Reveal.getConfig().parallaxBackgroundHorizontal;\n\t\t\t}\n\t\t\telse {\n\t\t\t\thorizontalOffsetMultiplier = horizontalSlideCount > 1 ? ( backgroundWidth - slideWidth ) / ( horizontalSlideCount-1 ) : 0;\n\t\t\t}\n\n\t\t\thorizontalOffset = horizontalOffsetMultiplier * indices.h * -1;\n\n\t\t\tlet slideHeight = this.element.offsetHeight,\n\t\t\t\tverticalSlideCount = verticalSlides.length,\n\t\t\t\tverticalOffsetMultiplier,\n\t\t\t\tverticalOffset;\n\n\t\t\tif( typeof this.Reveal.getConfig().parallaxBackgroundVertical === 'number' ) {\n\t\t\t\tverticalOffsetMultiplier = this.Reveal.getConfig().parallaxBackgroundVertical;\n\t\t\t}\n\t\t\telse {\n\t\t\t\tverticalOffsetMultiplier = ( backgroundHeight - slideHeight ) / ( verticalSlideCount-1 );\n\t\t\t}\n\n\t\t\tverticalOffset = verticalSlideCount > 0 ? verticalOffsetMultiplier * indices.v : 0;\n\n\t\t\tthis.element.style.backgroundPosition = horizontalOffset + 'px ' + -verticalOffset + 'px';\n\n\t\t}\n\n\t}\n\n\tdestroy() {\n\n\t\tthis.element.remove();\n\n\t}\n\n}\n","\nexport const SLIDES_SELECTOR = '.slides section';\nexport const HORIZONTAL_SLIDES_SELECTOR = '.slides>section';\nexport const VERTICAL_SLIDES_SELECTOR = '.slides>section.present>section';\n\n// Methods that may not be invoked via the postMessage API\nexport const POST_MESSAGE_METHOD_BLACKLIST = /registerPlugin|registerKeyboardShortcut|addKeyBinding|addEventListener/;\n\n// Regex for retrieving the fragment style from a class attribute\nexport const FRAGMENT_STYLE_REGEX = /fade-(down|up|right|left|out|in-then-out|in-then-semi-out)|semi-fade-out|current-visible|shrink|grow/;","import { queryAll, extend, createStyleSheet, matches, closest } from '../utils/util.js'\nimport { FRAGMENT_STYLE_REGEX } from '../utils/constants.js'\n\n// Counter used to generate unique IDs for auto-animated elements\nlet autoAnimateCounter = 0;\n\n/**\n * Automatically animates matching elements across\n * slides with the [data-auto-animate] attribute.\n */\nexport default class AutoAnimate {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t}\n\n\t/**\n\t * Runs an auto-animation between the given slides.\n\t *\n\t * @param {HTMLElement} fromSlide\n\t * @param {HTMLElement} toSlide\n\t */\n\trun( fromSlide, toSlide ) {\n\n\t\t// Clean up after prior animations\n\t\tthis.reset();\n\n\t\tlet allSlides = this.Reveal.getSlides();\n\t\tlet toSlideIndex = allSlides.indexOf( toSlide );\n\t\tlet fromSlideIndex = allSlides.indexOf( fromSlide );\n\n\t\t// Ensure that both slides are auto-animate targets with the same data-auto-animate-id value\n\t\t// (including null if absent on both) and that data-auto-animate-restart isn't set on the\n\t\t// physically latter slide (independent of slide direction)\n\t\tif( fromSlide.hasAttribute( 'data-auto-animate' ) && toSlide.hasAttribute( 'data-auto-animate' )\n\t\t\t\t&& fromSlide.getAttribute( 'data-auto-animate-id' ) === toSlide.getAttribute( 'data-auto-animate-id' ) \n\t\t\t\t&& !( toSlideIndex > fromSlideIndex ? toSlide : fromSlide ).hasAttribute( 'data-auto-animate-restart' ) ) {\n\n\t\t\t// Create a new auto-animate sheet\n\t\t\tthis.autoAnimateStyleSheet = this.autoAnimateStyleSheet || createStyleSheet();\n\n\t\t\tlet animationOptions = this.getAutoAnimateOptions( toSlide );\n\n\t\t\t// Set our starting state\n\t\t\tfromSlide.dataset.autoAnimate = 'pending';\n\t\t\ttoSlide.dataset.autoAnimate = 'pending';\n\n\t\t\t// Flag the navigation direction, needed for fragment buildup\n\t\t\tanimationOptions.slideDirection = toSlideIndex > fromSlideIndex ? 'forward' : 'backward';\n\n\t\t\t// Inject our auto-animate styles for this transition\n\t\t\tlet css = this.getAutoAnimatableElements( fromSlide, toSlide ).map( elements => {\n\t\t\t\treturn this.autoAnimateElements( elements.from, elements.to, elements.options || {}, animationOptions, autoAnimateCounter++ );\n\t\t\t} );\n\n\t\t\t// Animate unmatched elements, if enabled\n\t\t\tif( toSlide.dataset.autoAnimateUnmatched !== 'false' && this.Reveal.getConfig().autoAnimateUnmatched === true ) {\n\n\t\t\t\t// Our default timings for unmatched elements\n\t\t\t\tlet defaultUnmatchedDuration = animationOptions.duration * 0.8,\n\t\t\t\t\tdefaultUnmatchedDelay = animationOptions.duration * 0.2;\n\n\t\t\t\tthis.getUnmatchedAutoAnimateElements( toSlide ).forEach( unmatchedElement => {\n\n\t\t\t\t\tlet unmatchedOptions = this.getAutoAnimateOptions( unmatchedElement, animationOptions );\n\t\t\t\t\tlet id = 'unmatched';\n\n\t\t\t\t\t// If there is a duration or delay set specifically for this\n\t\t\t\t\t// element our unmatched elements should adhere to those\n\t\t\t\t\tif( unmatchedOptions.duration !== animationOptions.duration || unmatchedOptions.delay !== animationOptions.delay ) {\n\t\t\t\t\t\tid = 'unmatched-' + autoAnimateCounter++;\n\t\t\t\t\t\tcss.push( `[data-auto-animate=\"running\"] [data-auto-animate-target=\"${id}\"] { transition: opacity ${unmatchedOptions.duration}s ease ${unmatchedOptions.delay}s; }` );\n\t\t\t\t\t}\n\n\t\t\t\t\tunmatchedElement.dataset.autoAnimateTarget = id;\n\n\t\t\t\t}, this );\n\n\t\t\t\t// Our default transition for unmatched elements\n\t\t\t\tcss.push( `[data-auto-animate=\"running\"] [data-auto-animate-target=\"unmatched\"] { transition: opacity ${defaultUnmatchedDuration}s ease ${defaultUnmatchedDelay}s; }` );\n\n\t\t\t}\n\n\t\t\t// Setting the whole chunk of CSS at once is the most\n\t\t\t// efficient way to do this. Using sheet.insertRule\n\t\t\t// is multiple factors slower.\n\t\t\tthis.autoAnimateStyleSheet.innerHTML = css.join( '' );\n\n\t\t\t// Start the animation next cycle\n\t\t\trequestAnimationFrame( () => {\n\t\t\t\tif( this.autoAnimateStyleSheet ) {\n\t\t\t\t\t// This forces our newly injected styles to be applied in Firefox\n\t\t\t\t\tgetComputedStyle( this.autoAnimateStyleSheet ).fontWeight;\n\n\t\t\t\t\ttoSlide.dataset.autoAnimate = 'running';\n\t\t\t\t}\n\t\t\t} );\n\n\t\t\tthis.Reveal.dispatchEvent({\n\t\t\t\ttype: 'autoanimate',\n\t\t\t\tdata: {\n\t\t\t\t\tfromSlide,\n\t\t\t\t\ttoSlide,\n\t\t\t\t\tsheet: this.autoAnimateStyleSheet\n\t\t\t\t}\n\t\t\t});\n\n\t\t}\n\n\t}\n\n\t/**\n\t * Rolls back all changes that we've made to the DOM so\n\t * that as part of animating.\n\t */\n\treset() {\n\n\t\t// Reset slides\n\t\tqueryAll( this.Reveal.getRevealElement(), '[data-auto-animate]:not([data-auto-animate=\"\"])' ).forEach( element => {\n\t\t\telement.dataset.autoAnimate = '';\n\t\t} );\n\n\t\t// Reset elements\n\t\tqueryAll( this.Reveal.getRevealElement(), '[data-auto-animate-target]' ).forEach( element => {\n\t\t\tdelete element.dataset.autoAnimateTarget;\n\t\t} );\n\n\t\t// Remove the animation sheet\n\t\tif( this.autoAnimateStyleSheet && this.autoAnimateStyleSheet.parentNode ) {\n\t\t\tthis.autoAnimateStyleSheet.parentNode.removeChild( this.autoAnimateStyleSheet );\n\t\t\tthis.autoAnimateStyleSheet = null;\n\t\t}\n\n\t}\n\n\t/**\n\t * Creates a FLIP animation where the `to` element starts out\n\t * in the `from` element position and animates to its original\n\t * state.\n\t *\n\t * @param {HTMLElement} from\n\t * @param {HTMLElement} to\n\t * @param {Object} elementOptions Options for this element pair\n\t * @param {Object} animationOptions Options set at the slide level\n\t * @param {String} id Unique ID that we can use to identify this\n\t * auto-animate element in the DOM\n\t */\n\tautoAnimateElements( from, to, elementOptions, animationOptions, id ) {\n\n\t\t// 'from' elements are given a data-auto-animate-target with no value,\n\t\t// 'to' elements are are given a data-auto-animate-target with an ID\n\t\tfrom.dataset.autoAnimateTarget = '';\n\t\tto.dataset.autoAnimateTarget = id;\n\n\t\t// Each element may override any of the auto-animate options\n\t\t// like transition easing, duration and delay via data-attributes\n\t\tlet options = this.getAutoAnimateOptions( to, animationOptions );\n\n\t\t// If we're using a custom element matcher the element options\n\t\t// may contain additional transition overrides\n\t\tif( typeof elementOptions.delay !== 'undefined' ) options.delay = elementOptions.delay;\n\t\tif( typeof elementOptions.duration !== 'undefined' ) options.duration = elementOptions.duration;\n\t\tif( typeof elementOptions.easing !== 'undefined' ) options.easing = elementOptions.easing;\n\n\t\tlet fromProps = this.getAutoAnimatableProperties( 'from', from, elementOptions ),\n\t\t\ttoProps = this.getAutoAnimatableProperties( 'to', to, elementOptions );\n\n\t\t// Maintain fragment visibility for matching elements when\n\t\t// we're navigating forwards, this way the viewer won't need\n\t\t// to step through the same fragments twice\n\t\tif( to.classList.contains( 'fragment' ) ) {\n\n\t\t\t// Don't auto-animate the opacity of fragments to avoid\n\t\t\t// conflicts with fragment animations\n\t\t\tdelete toProps.styles['opacity'];\n\n\t\t\tif( from.classList.contains( 'fragment' ) ) {\n\n\t\t\t\tlet fromFragmentStyle = ( from.className.match( FRAGMENT_STYLE_REGEX ) || [''] )[0];\n\t\t\t\tlet toFragmentStyle = ( to.className.match( FRAGMENT_STYLE_REGEX ) || [''] )[0];\n\n\t\t\t\t// Only skip the fragment if the fragment animation style\n\t\t\t\t// remains unchanged\n\t\t\t\tif( fromFragmentStyle === toFragmentStyle && animationOptions.slideDirection === 'forward' ) {\n\t\t\t\t\tto.classList.add( 'visible', 'disabled' );\n\t\t\t\t}\n\n\t\t\t}\n\n\t\t}\n\n\t\t// If translation and/or scaling are enabled, css transform\n\t\t// the 'to' element so that it matches the position and size\n\t\t// of the 'from' element\n\t\tif( elementOptions.translate !== false || elementOptions.scale !== false ) {\n\n\t\t\tlet presentationScale = this.Reveal.getScale();\n\n\t\t\tlet delta = {\n\t\t\t\tx: ( fromProps.x - toProps.x ) / presentationScale,\n\t\t\t\ty: ( fromProps.y - toProps.y ) / presentationScale,\n\t\t\t\tscaleX: fromProps.width / toProps.width,\n\t\t\t\tscaleY: fromProps.height / toProps.height\n\t\t\t};\n\n\t\t\t// Limit decimal points to avoid 0.0001px blur and stutter\n\t\t\tdelta.x = Math.round( delta.x * 1000 ) / 1000;\n\t\t\tdelta.y = Math.round( delta.y * 1000 ) / 1000;\n\t\t\tdelta.scaleX = Math.round( delta.scaleX * 1000 ) / 1000;\n\t\t\tdelta.scaleX = Math.round( delta.scaleX * 1000 ) / 1000;\n\n\t\t\tlet translate = elementOptions.translate !== false && ( delta.x !== 0 || delta.y !== 0 ),\n\t\t\t\tscale = elementOptions.scale !== false && ( delta.scaleX !== 0 || delta.scaleY !== 0 );\n\n\t\t\t// No need to transform if nothing's changed\n\t\t\tif( translate || scale ) {\n\n\t\t\t\tlet transform = [];\n\n\t\t\t\tif( translate ) transform.push( `translate(${delta.x}px, ${delta.y}px)` );\n\t\t\t\tif( scale ) transform.push( `scale(${delta.scaleX}, ${delta.scaleY})` );\n\n\t\t\t\tfromProps.styles['transform'] = transform.join( ' ' );\n\t\t\t\tfromProps.styles['transform-origin'] = 'top left';\n\n\t\t\t\ttoProps.styles['transform'] = 'none';\n\n\t\t\t}\n\n\t\t}\n\n\t\t// Delete all unchanged 'to' styles\n\t\tfor( let propertyName in toProps.styles ) {\n\t\t\tconst toValue = toProps.styles[propertyName];\n\t\t\tconst fromValue = fromProps.styles[propertyName];\n\n\t\t\tif( toValue === fromValue ) {\n\t\t\t\tdelete toProps.styles[propertyName];\n\t\t\t}\n\t\t\telse {\n\t\t\t\t// If these property values were set via a custom matcher providing\n\t\t\t\t// an explicit 'from' and/or 'to' value, we always inject those values.\n\t\t\t\tif( toValue.explicitValue === true ) {\n\t\t\t\t\ttoProps.styles[propertyName] = toValue.value;\n\t\t\t\t}\n\n\t\t\t\tif( fromValue.explicitValue === true ) {\n\t\t\t\t\tfromProps.styles[propertyName] = fromValue.value;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tlet css = '';\n\n\t\tlet toStyleProperties = Object.keys( toProps.styles );\n\n\t\t// Only create animate this element IF at least one style\n\t\t// property has changed\n\t\tif( toStyleProperties.length > 0 ) {\n\n\t\t\t// Instantly move to the 'from' state\n\t\t\tfromProps.styles['transition'] = 'none';\n\n\t\t\t// Animate towards the 'to' state\n\t\t\ttoProps.styles['transition'] = `all ${options.duration}s ${options.easing} ${options.delay}s`;\n\t\t\ttoProps.styles['transition-property'] = toStyleProperties.join( ', ' );\n\t\t\ttoProps.styles['will-change'] = toStyleProperties.join( ', ' );\n\n\t\t\t// Build up our custom CSS. We need to override inline styles\n\t\t\t// so we need to make our styles vErY IMPORTANT!1!!\n\t\t\tlet fromCSS = Object.keys( fromProps.styles ).map( propertyName => {\n\t\t\t\treturn propertyName + ': ' + fromProps.styles[propertyName] + ' !important;';\n\t\t\t} ).join( '' );\n\n\t\t\tlet toCSS = Object.keys( toProps.styles ).map( propertyName => {\n\t\t\t\treturn propertyName + ': ' + toProps.styles[propertyName] + ' !important;';\n\t\t\t} ).join( '' );\n\n\t\t\tcss = \t'[data-auto-animate-target=\"'+ id +'\"] {'+ fromCSS +'}' +\n\t\t\t\t\t'[data-auto-animate=\"running\"] [data-auto-animate-target=\"'+ id +'\"] {'+ toCSS +'}';\n\n\t\t}\n\n\t\treturn css;\n\n\t}\n\n\t/**\n\t * Returns the auto-animate options for the given element.\n\t *\n\t * @param {HTMLElement} element Element to pick up options\n\t * from, either a slide or an animation target\n\t * @param {Object} [inheritedOptions] Optional set of existing\n\t * options\n\t */\n\tgetAutoAnimateOptions( element, inheritedOptions ) {\n\n\t\tlet options = {\n\t\t\teasing: this.Reveal.getConfig().autoAnimateEasing,\n\t\t\tduration: this.Reveal.getConfig().autoAnimateDuration,\n\t\t\tdelay: 0\n\t\t};\n\n\t\toptions = extend( options, inheritedOptions );\n\n\t\t// Inherit options from parent elements\n\t\tif( element.parentNode ) {\n\t\t\tlet autoAnimatedParent = closest( element.parentNode, '[data-auto-animate-target]' );\n\t\t\tif( autoAnimatedParent ) {\n\t\t\t\toptions = this.getAutoAnimateOptions( autoAnimatedParent, options );\n\t\t\t}\n\t\t}\n\n\t\tif( element.dataset.autoAnimateEasing ) {\n\t\t\toptions.easing = element.dataset.autoAnimateEasing;\n\t\t}\n\n\t\tif( element.dataset.autoAnimateDuration ) {\n\t\t\toptions.duration = parseFloat( element.dataset.autoAnimateDuration );\n\t\t}\n\n\t\tif( element.dataset.autoAnimateDelay ) {\n\t\t\toptions.delay = parseFloat( element.dataset.autoAnimateDelay );\n\t\t}\n\n\t\treturn options;\n\n\t}\n\n\t/**\n\t * Returns an object containing all of the properties\n\t * that can be auto-animated for the given element and\n\t * their current computed values.\n\t *\n\t * @param {String} direction 'from' or 'to'\n\t */\n\tgetAutoAnimatableProperties( direction, element, elementOptions ) {\n\n\t\tlet config = this.Reveal.getConfig();\n\n\t\tlet properties = { styles: [] };\n\n\t\t// Position and size\n\t\tif( elementOptions.translate !== false || elementOptions.scale !== false ) {\n\t\t\tlet bounds;\n\n\t\t\t// Custom auto-animate may optionally return a custom tailored\n\t\t\t// measurement function\n\t\t\tif( typeof elementOptions.measure === 'function' ) {\n\t\t\t\tbounds = elementOptions.measure( element );\n\t\t\t}\n\t\t\telse {\n\t\t\t\tif( config.center ) {\n\t\t\t\t\t// More precise, but breaks when used in combination\n\t\t\t\t\t// with zoom for scaling the deck ¯\\_(ツ)_/¯\n\t\t\t\t\tbounds = element.getBoundingClientRect();\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tlet scale = this.Reveal.getScale();\n\t\t\t\t\tbounds = {\n\t\t\t\t\t\tx: element.offsetLeft * scale,\n\t\t\t\t\t\ty: element.offsetTop * scale,\n\t\t\t\t\t\twidth: element.offsetWidth * scale,\n\t\t\t\t\t\theight: element.offsetHeight * scale\n\t\t\t\t\t};\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tproperties.x = bounds.x;\n\t\t\tproperties.y = bounds.y;\n\t\t\tproperties.width = bounds.width;\n\t\t\tproperties.height = bounds.height;\n\t\t}\n\n\t\tconst computedStyles = getComputedStyle( element );\n\n\t\t// CSS styles\n\t\t( elementOptions.styles || config.autoAnimateStyles ).forEach( style => {\n\t\t\tlet value;\n\n\t\t\t// `style` is either the property name directly, or an object\n\t\t\t// definition of a style property\n\t\t\tif( typeof style === 'string' ) style = { property: style };\n\n\t\t\tif( typeof style.from !== 'undefined' && direction === 'from' ) {\n\t\t\t\tvalue = { value: style.from, explicitValue: true };\n\t\t\t}\n\t\t\telse if( typeof style.to !== 'undefined' && direction === 'to' ) {\n\t\t\t\tvalue = { value: style.to, explicitValue: true };\n\t\t\t}\n\t\t\telse {\n\t\t\t\tvalue = computedStyles[style.property];\n\t\t\t}\n\n\t\t\tif( value !== '' ) {\n\t\t\t\tproperties.styles[style.property] = value;\n\t\t\t}\n\t\t} );\n\n\t\treturn properties;\n\n\t}\n\n\t/**\n\t * Get a list of all element pairs that we can animate\n\t * between the given slides.\n\t *\n\t * @param {HTMLElement} fromSlide\n\t * @param {HTMLElement} toSlide\n\t *\n\t * @return {Array} Each value is an array where [0] is\n\t * the element we're animating from and [1] is the\n\t * element we're animating to\n\t */\n\tgetAutoAnimatableElements( fromSlide, toSlide ) {\n\n\t\tlet matcher = typeof this.Reveal.getConfig().autoAnimateMatcher === 'function' ? this.Reveal.getConfig().autoAnimateMatcher : this.getAutoAnimatePairs;\n\n\t\tlet pairs = matcher.call( this, fromSlide, toSlide );\n\n\t\tlet reserved = [];\n\n\t\t// Remove duplicate pairs\n\t\treturn pairs.filter( ( pair, index ) => {\n\t\t\tif( reserved.indexOf( pair.to ) === -1 ) {\n\t\t\t\treserved.push( pair.to );\n\t\t\t\treturn true;\n\t\t\t}\n\t\t} );\n\n\t}\n\n\t/**\n\t * Identifies matching elements between slides.\n\t *\n\t * You can specify a custom matcher function by using\n\t * the `autoAnimateMatcher` config option.\n\t */\n\tgetAutoAnimatePairs( fromSlide, toSlide ) {\n\n\t\tlet pairs = [];\n\n\t\tconst codeNodes = 'pre';\n\t\tconst textNodes = 'h1, h2, h3, h4, h5, h6, p, li';\n\t\tconst mediaNodes = 'img, video, iframe';\n\n\t\t// Eplicit matches via data-id\n\t\tthis.findAutoAnimateMatches( pairs, fromSlide, toSlide, '[data-id]', node => {\n\t\t\treturn node.nodeName + ':::' + node.getAttribute( 'data-id' );\n\t\t} );\n\n\t\t// Text\n\t\tthis.findAutoAnimateMatches( pairs, fromSlide, toSlide, textNodes, node => {\n\t\t\treturn node.nodeName + ':::' + node.innerText;\n\t\t} );\n\n\t\t// Media\n\t\tthis.findAutoAnimateMatches( pairs, fromSlide, toSlide, mediaNodes, node => {\n\t\t\treturn node.nodeName + ':::' + ( node.getAttribute( 'src' ) || node.getAttribute( 'data-src' ) );\n\t\t} );\n\n\t\t// Code\n\t\tthis.findAutoAnimateMatches( pairs, fromSlide, toSlide, codeNodes, node => {\n\t\t\treturn node.nodeName + ':::' + node.innerText;\n\t\t} );\n\n\t\tpairs.forEach( pair => {\n\n\t\t\t// Disable scale transformations on text nodes, we transition\n\t\t\t// each individual text property instead\n\t\t\tif( matches( pair.from, textNodes ) ) {\n\t\t\t\tpair.options = { scale: false };\n\t\t\t}\n\t\t\t// Animate individual lines of code\n\t\t\telse if( matches( pair.from, codeNodes ) ) {\n\n\t\t\t\t// Transition the code block's width and height instead of scaling\n\t\t\t\t// to prevent its content from being squished\n\t\t\t\tpair.options = { scale: false, styles: [ 'width', 'height' ] };\n\n\t\t\t\t// Lines of code\n\t\t\t\tthis.findAutoAnimateMatches( pairs, pair.from, pair.to, '.hljs .hljs-ln-code', node => {\n\t\t\t\t\treturn node.textContent;\n\t\t\t\t}, {\n\t\t\t\t\tscale: false,\n\t\t\t\t\tstyles: [],\n\t\t\t\t\tmeasure: this.getLocalBoundingBox.bind( this )\n\t\t\t\t} );\n\n\t\t\t\t// Line numbers\n\t\t\t\tthis.findAutoAnimateMatches( pairs, pair.from, pair.to, '.hljs .hljs-ln-line[data-line-number]', node => {\n\t\t\t\t\treturn node.getAttribute( 'data-line-number' );\n\t\t\t\t}, {\n\t\t\t\t\tscale: false,\n\t\t\t\t\tstyles: [ 'width' ],\n\t\t\t\t\tmeasure: this.getLocalBoundingBox.bind( this )\n\t\t\t\t} );\n\n\t\t\t}\n\n\t\t}, this );\n\n\t\treturn pairs;\n\n\t}\n\n\t/**\n\t * Helper method which returns a bounding box based on\n\t * the given elements offset coordinates.\n\t *\n\t * @param {HTMLElement} element\n\t * @return {Object} x, y, width, height\n\t */\n\tgetLocalBoundingBox( element ) {\n\n\t\tconst presentationScale = this.Reveal.getScale();\n\n\t\treturn {\n\t\t\tx: Math.round( ( element.offsetLeft * presentationScale ) * 100 ) / 100,\n\t\t\ty: Math.round( ( element.offsetTop * presentationScale ) * 100 ) / 100,\n\t\t\twidth: Math.round( ( element.offsetWidth * presentationScale ) * 100 ) / 100,\n\t\t\theight: Math.round( ( element.offsetHeight * presentationScale ) * 100 ) / 100\n\t\t};\n\n\t}\n\n\t/**\n\t * Finds matching elements between two slides.\n\t *\n\t * @param {Array} pairs \tList of pairs to push matches to\n\t * @param {HTMLElement} fromScope Scope within the from element exists\n\t * @param {HTMLElement} toScope Scope within the to element exists\n\t * @param {String} selector CSS selector of the element to match\n\t * @param {Function} serializer A function that accepts an element and returns\n\t * a stringified ID based on its contents\n\t * @param {Object} animationOptions Optional config options for this pair\n\t */\n\tfindAutoAnimateMatches( pairs, fromScope, toScope, selector, serializer, animationOptions ) {\n\n\t\tlet fromMatches = {};\n\t\tlet toMatches = {};\n\n\t\t[].slice.call( fromScope.querySelectorAll( selector ) ).forEach( ( element, i ) => {\n\t\t\tconst key = serializer( element );\n\t\t\tif( typeof key === 'string' && key.length ) {\n\t\t\t\tfromMatches[key] = fromMatches[key] || [];\n\t\t\t\tfromMatches[key].push( element );\n\t\t\t}\n\t\t} );\n\n\t\t[].slice.call( toScope.querySelectorAll( selector ) ).forEach( ( element, i ) => {\n\t\t\tconst key = serializer( element );\n\t\t\ttoMatches[key] = toMatches[key] || [];\n\t\t\ttoMatches[key].push( element );\n\n\t\t\tlet fromElement;\n\n\t\t\t// Retrieve the 'from' element\n\t\t\tif( fromMatches[key] ) {\n\t\t\t\tconst pimaryIndex = toMatches[key].length - 1;\n\t\t\t\tconst secondaryIndex = fromMatches[key].length - 1;\n\n\t\t\t\t// If there are multiple identical from elements, retrieve\n\t\t\t\t// the one at the same index as our to-element.\n\t\t\t\tif( fromMatches[key][ pimaryIndex ] ) {\n\t\t\t\t\tfromElement = fromMatches[key][ pimaryIndex ];\n\t\t\t\t\tfromMatches[key][ pimaryIndex ] = null;\n\t\t\t\t}\n\t\t\t\t// If there are no matching from-elements at the same index,\n\t\t\t\t// use the last one.\n\t\t\t\telse if( fromMatches[key][ secondaryIndex ] ) {\n\t\t\t\t\tfromElement = fromMatches[key][ secondaryIndex ];\n\t\t\t\t\tfromMatches[key][ secondaryIndex ] = null;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// If we've got a matching pair, push it to the list of pairs\n\t\t\tif( fromElement ) {\n\t\t\t\tpairs.push({\n\t\t\t\t\tfrom: fromElement,\n\t\t\t\t\tto: element,\n\t\t\t\t\toptions: animationOptions\n\t\t\t\t});\n\t\t\t}\n\t\t} );\n\n\t}\n\n\t/**\n\t * Returns a all elements within the given scope that should\n\t * be considered unmatched in an auto-animate transition. If\n\t * fading of unmatched elements is turned on, these elements\n\t * will fade when going between auto-animate slides.\n\t *\n\t * Note that parents of auto-animate targets are NOT considerd\n\t * unmatched since fading them would break the auto-animation.\n\t *\n\t * @param {HTMLElement} rootElement\n\t * @return {Array}\n\t */\n\tgetUnmatchedAutoAnimateElements( rootElement ) {\n\n\t\treturn [].slice.call( rootElement.children ).reduce( ( result, element ) => {\n\n\t\t\tconst containsAnimatedElements = element.querySelector( '[data-auto-animate-target]' );\n\n\t\t\t// The element is unmatched if\n\t\t\t// - It is not an auto-animate target\n\t\t\t// - It does not contain any auto-animate targets\n\t\t\tif( !element.hasAttribute( 'data-auto-animate-target' ) && !containsAnimatedElements ) {\n\t\t\t\tresult.push( element );\n\t\t\t}\n\n\t\t\tif( element.querySelector( '[data-auto-animate-target]' ) ) {\n\t\t\t\tresult = result.concat( this.getUnmatchedAutoAnimateElements( element ) );\n\t\t\t}\n\n\t\t\treturn result;\n\n\t\t}, [] );\n\n\t}\n\n}\n","import { extend, queryAll } from '../utils/util.js'\n\n/**\n * Handles sorting and navigation of slide fragments.\n * Fragments are elements within a slide that are\n * revealed/animated incrementally.\n */\nexport default class Fragments {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t}\n\n\t/**\n\t * Called when the reveal.js config is updated.\n\t */\n\tconfigure( config, oldConfig ) {\n\n\t\tif( config.fragments === false ) {\n\t\t\tthis.disable();\n\t\t}\n\t\telse if( oldConfig.fragments === false ) {\n\t\t\tthis.enable();\n\t\t}\n\n\t}\n\n\t/**\n\t * If fragments are disabled in the deck, they should all be\n\t * visible rather than stepped through.\n\t */\n\tdisable() {\n\n\t\tqueryAll( this.Reveal.getSlidesElement(), '.fragment' ).forEach( element => {\n\t\t\telement.classList.add( 'visible' );\n\t\t\telement.classList.remove( 'current-fragment' );\n\t\t} );\n\n\t}\n\n\t/**\n\t * Reverse of #disable(). Only called if fragments have\n\t * previously been disabled.\n\t */\n\tenable() {\n\n\t\tqueryAll( this.Reveal.getSlidesElement(), '.fragment' ).forEach( element => {\n\t\t\telement.classList.remove( 'visible' );\n\t\t\telement.classList.remove( 'current-fragment' );\n\t\t} );\n\n\t}\n\n\t/**\n\t * Returns an object describing the available fragment\n\t * directions.\n\t *\n\t * @return {{prev: boolean, next: boolean}}\n\t */\n\tavailableRoutes() {\n\n\t\tlet currentSlide = this.Reveal.getCurrentSlide();\n\t\tif( currentSlide && this.Reveal.getConfig().fragments ) {\n\t\t\tlet fragments = currentSlide.querySelectorAll( '.fragment:not(.disabled)' );\n\t\t\tlet hiddenFragments = currentSlide.querySelectorAll( '.fragment:not(.disabled):not(.visible)' );\n\n\t\t\treturn {\n\t\t\t\tprev: fragments.length - hiddenFragments.length > 0,\n\t\t\t\tnext: !!hiddenFragments.length\n\t\t\t};\n\t\t}\n\t\telse {\n\t\t\treturn { prev: false, next: false };\n\t\t}\n\n\t}\n\n\t/**\n\t * Return a sorted fragments list, ordered by an increasing\n\t * \"data-fragment-index\" attribute.\n\t *\n\t * Fragments will be revealed in the order that they are returned by\n\t * this function, so you can use the index attributes to control the\n\t * order of fragment appearance.\n\t *\n\t * To maintain a sensible default fragment order, fragments are presumed\n\t * to be passed in document order. This function adds a \"fragment-index\"\n\t * attribute to each node if such an attribute is not already present,\n\t * and sets that attribute to an integer value which is the position of\n\t * the fragment within the fragments list.\n\t *\n\t * @param {object[]|*} fragments\n\t * @param {boolean} grouped If true the returned array will contain\n\t * nested arrays for all fragments with the same index\n\t * @return {object[]} sorted Sorted array of fragments\n\t */\n\tsort( fragments, grouped = false ) {\n\n\t\tfragments = Array.from( fragments );\n\n\t\tlet ordered = [],\n\t\t\tunordered = [],\n\t\t\tsorted = [];\n\n\t\t// Group ordered and unordered elements\n\t\tfragments.forEach( fragment => {\n\t\t\tif( fragment.hasAttribute( 'data-fragment-index' ) ) {\n\t\t\t\tlet index = parseInt( fragment.getAttribute( 'data-fragment-index' ), 10 );\n\n\t\t\t\tif( !ordered[index] ) {\n\t\t\t\t\tordered[index] = [];\n\t\t\t\t}\n\n\t\t\t\tordered[index].push( fragment );\n\t\t\t}\n\t\t\telse {\n\t\t\t\tunordered.push( [ fragment ] );\n\t\t\t}\n\t\t} );\n\n\t\t// Append fragments without explicit indices in their\n\t\t// DOM order\n\t\tordered = ordered.concat( unordered );\n\n\t\t// Manually count the index up per group to ensure there\n\t\t// are no gaps\n\t\tlet index = 0;\n\n\t\t// Push all fragments in their sorted order to an array,\n\t\t// this flattens the groups\n\t\tordered.forEach( group => {\n\t\t\tgroup.forEach( fragment => {\n\t\t\t\tsorted.push( fragment );\n\t\t\t\tfragment.setAttribute( 'data-fragment-index', index );\n\t\t\t} );\n\n\t\t\tindex ++;\n\t\t} );\n\n\t\treturn grouped === true ? ordered : sorted;\n\n\t}\n\n\t/**\n\t * Sorts and formats all of fragments in the\n\t * presentation.\n\t */\n\tsortAll() {\n\n\t\tthis.Reveal.getHorizontalSlides().forEach( horizontalSlide => {\n\n\t\t\tlet verticalSlides = queryAll( horizontalSlide, 'section' );\n\t\t\tverticalSlides.forEach( ( verticalSlide, y ) => {\n\n\t\t\t\tthis.sort( verticalSlide.querySelectorAll( '.fragment' ) );\n\n\t\t\t}, this );\n\n\t\t\tif( verticalSlides.length === 0 ) this.sort( horizontalSlide.querySelectorAll( '.fragment' ) );\n\n\t\t} );\n\n\t}\n\n\t/**\n\t * Refreshes the fragments on the current slide so that they\n\t * have the appropriate classes (.visible + .current-fragment).\n\t *\n\t * @param {number} [index] The index of the current fragment\n\t * @param {array} [fragments] Array containing all fragments\n\t * in the current slide\n\t *\n\t * @return {{shown: array, hidden: array}}\n\t */\n\tupdate( index, fragments ) {\n\n\t\tlet changedFragments = {\n\t\t\tshown: [],\n\t\t\thidden: []\n\t\t};\n\n\t\tlet currentSlide = this.Reveal.getCurrentSlide();\n\t\tif( currentSlide && this.Reveal.getConfig().fragments ) {\n\n\t\t\tfragments = fragments || this.sort( currentSlide.querySelectorAll( '.fragment' ) );\n\n\t\t\tif( fragments.length ) {\n\n\t\t\t\tlet maxIndex = 0;\n\n\t\t\t\tif( typeof index !== 'number' ) {\n\t\t\t\t\tlet currentFragment = this.sort( currentSlide.querySelectorAll( '.fragment.visible' ) ).pop();\n\t\t\t\t\tif( currentFragment ) {\n\t\t\t\t\t\tindex = parseInt( currentFragment.getAttribute( 'data-fragment-index' ) || 0, 10 );\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\tArray.from( fragments ).forEach( ( el, i ) => {\n\n\t\t\t\t\tif( el.hasAttribute( 'data-fragment-index' ) ) {\n\t\t\t\t\t\ti = parseInt( el.getAttribute( 'data-fragment-index' ), 10 );\n\t\t\t\t\t}\n\n\t\t\t\t\tmaxIndex = Math.max( maxIndex, i );\n\n\t\t\t\t\t// Visible fragments\n\t\t\t\t\tif( i <= index ) {\n\t\t\t\t\t\tlet wasVisible = el.classList.contains( 'visible' )\n\t\t\t\t\t\tel.classList.add( 'visible' );\n\t\t\t\t\t\tel.classList.remove( 'current-fragment' );\n\n\t\t\t\t\t\tif( i === index ) {\n\t\t\t\t\t\t\t// Announce the fragments one by one to the Screen Reader\n\t\t\t\t\t\t\tthis.Reveal.announceStatus( this.Reveal.getStatusText( el ) );\n\n\t\t\t\t\t\t\tel.classList.add( 'current-fragment' );\n\t\t\t\t\t\t\tthis.Reveal.slideContent.startEmbeddedContent( el );\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\tif( !wasVisible ) {\n\t\t\t\t\t\t\tchangedFragments.shown.push( el )\n\t\t\t\t\t\t\tthis.Reveal.dispatchEvent({\n\t\t\t\t\t\t\t\ttarget: el,\n\t\t\t\t\t\t\t\ttype: 'visible',\n\t\t\t\t\t\t\t\tbubbles: false\n\t\t\t\t\t\t\t});\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\t// Hidden fragments\n\t\t\t\t\telse {\n\t\t\t\t\t\tlet wasVisible = el.classList.contains( 'visible' )\n\t\t\t\t\t\tel.classList.remove( 'visible' );\n\t\t\t\t\t\tel.classList.remove( 'current-fragment' );\n\n\t\t\t\t\t\tif( wasVisible ) {\n\t\t\t\t\t\t\tthis.Reveal.slideContent.stopEmbeddedContent( el );\n\t\t\t\t\t\t\tchangedFragments.hidden.push( el );\n\t\t\t\t\t\t\tthis.Reveal.dispatchEvent({\n\t\t\t\t\t\t\t\ttarget: el,\n\t\t\t\t\t\t\t\ttype: 'hidden',\n\t\t\t\t\t\t\t\tbubbles: false\n\t\t\t\t\t\t\t});\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t} );\n\n\t\t\t\t// Write the current fragment index to the slide
    .\n\t\t\t\t// This can be used by end users to apply styles based on\n\t\t\t\t// the current fragment index.\n\t\t\t\tindex = typeof index === 'number' ? index : -1;\n\t\t\t\tindex = Math.max( Math.min( index, maxIndex ), -1 );\n\t\t\t\tcurrentSlide.setAttribute( 'data-fragment', index );\n\n\t\t\t}\n\n\t\t}\n\n\t\treturn changedFragments;\n\n\t}\n\n\t/**\n\t * Formats the fragments on the given slide so that they have\n\t * valid indices. Call this if fragments are changed in the DOM\n\t * after reveal.js has already initialized.\n\t *\n\t * @param {HTMLElement} slide\n\t * @return {Array} a list of the HTML fragments that were synced\n\t */\n\tsync( slide = this.Reveal.getCurrentSlide() ) {\n\n\t\treturn this.sort( slide.querySelectorAll( '.fragment' ) );\n\n\t}\n\n\t/**\n\t * Navigate to the specified slide fragment.\n\t *\n\t * @param {?number} index The index of the fragment that\n\t * should be shown, -1 means all are invisible\n\t * @param {number} offset Integer offset to apply to the\n\t * fragment index\n\t *\n\t * @return {boolean} true if a change was made in any\n\t * fragments visibility as part of this call\n\t */\n\tgoto( index, offset = 0 ) {\n\n\t\tlet currentSlide = this.Reveal.getCurrentSlide();\n\t\tif( currentSlide && this.Reveal.getConfig().fragments ) {\n\n\t\t\tlet fragments = this.sort( currentSlide.querySelectorAll( '.fragment:not(.disabled)' ) );\n\t\t\tif( fragments.length ) {\n\n\t\t\t\t// If no index is specified, find the current\n\t\t\t\tif( typeof index !== 'number' ) {\n\t\t\t\t\tlet lastVisibleFragment = this.sort( currentSlide.querySelectorAll( '.fragment:not(.disabled).visible' ) ).pop();\n\n\t\t\t\t\tif( lastVisibleFragment ) {\n\t\t\t\t\t\tindex = parseInt( lastVisibleFragment.getAttribute( 'data-fragment-index' ) || 0, 10 );\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tindex = -1;\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// Apply the offset if there is one\n\t\t\t\tindex += offset;\n\n\t\t\t\tlet changedFragments = this.update( index, fragments );\n\n\t\t\t\tif( changedFragments.hidden.length ) {\n\t\t\t\t\tthis.Reveal.dispatchEvent({\n\t\t\t\t\t\ttype: 'fragmenthidden',\n\t\t\t\t\t\tdata: {\n\t\t\t\t\t\t\tfragment: changedFragments.hidden[0],\n\t\t\t\t\t\t\tfragments: changedFragments.hidden\n\t\t\t\t\t\t}\n\t\t\t\t\t});\n\t\t\t\t}\n\n\t\t\t\tif( changedFragments.shown.length ) {\n\t\t\t\t\tthis.Reveal.dispatchEvent({\n\t\t\t\t\t\ttype: 'fragmentshown',\n\t\t\t\t\t\tdata: {\n\t\t\t\t\t\t\tfragment: changedFragments.shown[0],\n\t\t\t\t\t\t\tfragments: changedFragments.shown\n\t\t\t\t\t\t}\n\t\t\t\t\t});\n\t\t\t\t}\n\n\t\t\t\tthis.Reveal.controls.update();\n\t\t\t\tthis.Reveal.progress.update();\n\n\t\t\t\tif( this.Reveal.getConfig().fragmentInURL ) {\n\t\t\t\t\tthis.Reveal.location.writeURL();\n\t\t\t\t}\n\n\t\t\t\treturn !!( changedFragments.shown.length || changedFragments.hidden.length );\n\n\t\t\t}\n\n\t\t}\n\n\t\treturn false;\n\n\t}\n\n\t/**\n\t * Navigate to the next slide fragment.\n\t *\n\t * @return {boolean} true if there was a next fragment,\n\t * false otherwise\n\t */\n\tnext() {\n\n\t\treturn this.goto( null, 1 );\n\n\t}\n\n\t/**\n\t * Navigate to the previous slide fragment.\n\t *\n\t * @return {boolean} true if there was a previous fragment,\n\t * false otherwise\n\t */\n\tprev() {\n\n\t\treturn this.goto( null, -1 );\n\n\t}\n\n}","import { SLIDES_SELECTOR } from '../utils/constants.js'\nimport { extend, queryAll, transformElement } from '../utils/util.js'\n\n/**\n * Handles all logic related to the overview mode\n * (birds-eye view of all slides).\n */\nexport default class Overview {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t\tthis.active = false;\n\n\t\tthis.onSlideClicked = this.onSlideClicked.bind( this );\n\n\t}\n\n\t/**\n\t * Displays the overview of slides (quick nav) by scaling\n\t * down and arranging all slide elements.\n\t */\n\tactivate() {\n\n\t\t// Only proceed if enabled in config\n\t\tif( this.Reveal.getConfig().overview && !this.isActive() ) {\n\n\t\t\tthis.active = true;\n\n\t\t\tthis.Reveal.getRevealElement().classList.add( 'overview' );\n\n\t\t\t// Don't auto-slide while in overview mode\n\t\t\tthis.Reveal.cancelAutoSlide();\n\n\t\t\t// Move the backgrounds element into the slide container to\n\t\t\t// that the same scaling is applied\n\t\t\tthis.Reveal.getSlidesElement().appendChild( this.Reveal.getBackgroundsElement() );\n\n\t\t\t// Clicking on an overview slide navigates to it\n\t\t\tqueryAll( this.Reveal.getRevealElement(), SLIDES_SELECTOR ).forEach( slide => {\n\t\t\t\tif( !slide.classList.contains( 'stack' ) ) {\n\t\t\t\t\tslide.addEventListener( 'click', this.onSlideClicked, true );\n\t\t\t\t}\n\t\t\t} );\n\n\t\t\t// Calculate slide sizes\n\t\t\tconst margin = 70;\n\t\t\tconst slideSize = this.Reveal.getComputedSlideSize();\n\t\t\tthis.overviewSlideWidth = slideSize.width + margin;\n\t\t\tthis.overviewSlideHeight = slideSize.height + margin;\n\n\t\t\t// Reverse in RTL mode\n\t\t\tif( this.Reveal.getConfig().rtl ) {\n\t\t\t\tthis.overviewSlideWidth = -this.overviewSlideWidth;\n\t\t\t}\n\n\t\t\tthis.Reveal.updateSlidesVisibility();\n\n\t\t\tthis.layout();\n\t\t\tthis.update();\n\n\t\t\tthis.Reveal.layout();\n\n\t\t\tconst indices = this.Reveal.getIndices();\n\n\t\t\t// Notify observers of the overview showing\n\t\t\tthis.Reveal.dispatchEvent({\n\t\t\t\ttype: 'overviewshown',\n\t\t\t\tdata: {\n\t\t\t\t\t'indexh': indices.h,\n\t\t\t\t\t'indexv': indices.v,\n\t\t\t\t\t'currentSlide': this.Reveal.getCurrentSlide()\n\t\t\t\t}\n\t\t\t});\n\n\t\t}\n\n\t}\n\n\t/**\n\t * Uses CSS transforms to position all slides in a grid for\n\t * display inside of the overview mode.\n\t */\n\tlayout() {\n\n\t\t// Layout slides\n\t\tthis.Reveal.getHorizontalSlides().forEach( ( hslide, h ) => {\n\t\t\thslide.setAttribute( 'data-index-h', h );\n\t\t\ttransformElement( hslide, 'translate3d(' + ( h * this.overviewSlideWidth ) + 'px, 0, 0)' );\n\n\t\t\tif( hslide.classList.contains( 'stack' ) ) {\n\n\t\t\t\tqueryAll( hslide, 'section' ).forEach( ( vslide, v ) => {\n\t\t\t\t\tvslide.setAttribute( 'data-index-h', h );\n\t\t\t\t\tvslide.setAttribute( 'data-index-v', v );\n\n\t\t\t\t\ttransformElement( vslide, 'translate3d(0, ' + ( v * this.overviewSlideHeight ) + 'px, 0)' );\n\t\t\t\t} );\n\n\t\t\t}\n\t\t} );\n\n\t\t// Layout slide backgrounds\n\t\tArray.from( this.Reveal.getBackgroundsElement().childNodes ).forEach( ( hbackground, h ) => {\n\t\t\ttransformElement( hbackground, 'translate3d(' + ( h * this.overviewSlideWidth ) + 'px, 0, 0)' );\n\n\t\t\tqueryAll( hbackground, '.slide-background' ).forEach( ( vbackground, v ) => {\n\t\t\t\ttransformElement( vbackground, 'translate3d(0, ' + ( v * this.overviewSlideHeight ) + 'px, 0)' );\n\t\t\t} );\n\t\t} );\n\n\t}\n\n\t/**\n\t * Moves the overview viewport to the current slides.\n\t * Called each time the current slide changes.\n\t */\n\tupdate() {\n\n\t\tconst vmin = Math.min( window.innerWidth, window.innerHeight );\n\t\tconst scale = Math.max( vmin / 5, 150 ) / vmin;\n\t\tconst indices = this.Reveal.getIndices();\n\n\t\tthis.Reveal.transformSlides( {\n\t\t\toverview: [\n\t\t\t\t'scale('+ scale +')',\n\t\t\t\t'translateX('+ ( -indices.h * this.overviewSlideWidth ) +'px)',\n\t\t\t\t'translateY('+ ( -indices.v * this.overviewSlideHeight ) +'px)'\n\t\t\t].join( ' ' )\n\t\t} );\n\n\t}\n\n\t/**\n\t * Exits the slide overview and enters the currently\n\t * active slide.\n\t */\n\tdeactivate() {\n\n\t\t// Only proceed if enabled in config\n\t\tif( this.Reveal.getConfig().overview ) {\n\n\t\t\tthis.active = false;\n\n\t\t\tthis.Reveal.getRevealElement().classList.remove( 'overview' );\n\n\t\t\t// Temporarily add a class so that transitions can do different things\n\t\t\t// depending on whether they are exiting/entering overview, or just\n\t\t\t// moving from slide to slide\n\t\t\tthis.Reveal.getRevealElement().classList.add( 'overview-deactivating' );\n\n\t\t\tsetTimeout( () => {\n\t\t\t\tthis.Reveal.getRevealElement().classList.remove( 'overview-deactivating' );\n\t\t\t}, 1 );\n\n\t\t\t// Move the background element back out\n\t\t\tthis.Reveal.getRevealElement().appendChild( this.Reveal.getBackgroundsElement() );\n\n\t\t\t// Clean up changes made to slides\n\t\t\tqueryAll( this.Reveal.getRevealElement(), SLIDES_SELECTOR ).forEach( slide => {\n\t\t\t\ttransformElement( slide, '' );\n\n\t\t\t\tslide.removeEventListener( 'click', this.onSlideClicked, true );\n\t\t\t} );\n\n\t\t\t// Clean up changes made to backgrounds\n\t\t\tqueryAll( this.Reveal.getBackgroundsElement(), '.slide-background' ).forEach( background => {\n\t\t\t\ttransformElement( background, '' );\n\t\t\t} );\n\n\t\t\tthis.Reveal.transformSlides( { overview: '' } );\n\n\t\t\tconst indices = this.Reveal.getIndices();\n\n\t\t\tthis.Reveal.slide( indices.h, indices.v );\n\t\t\tthis.Reveal.layout();\n\t\t\tthis.Reveal.cueAutoSlide();\n\n\t\t\t// Notify observers of the overview hiding\n\t\t\tthis.Reveal.dispatchEvent({\n\t\t\t\ttype: 'overviewhidden',\n\t\t\t\tdata: {\n\t\t\t\t\t'indexh': indices.h,\n\t\t\t\t\t'indexv': indices.v,\n\t\t\t\t\t'currentSlide': this.Reveal.getCurrentSlide()\n\t\t\t\t}\n\t\t\t});\n\n\t\t}\n\t}\n\n\t/**\n\t * Toggles the slide overview mode on and off.\n\t *\n\t * @param {Boolean} [override] Flag which overrides the\n\t * toggle logic and forcibly sets the desired state. True means\n\t * overview is open, false means it's closed.\n\t */\n\ttoggle( override ) {\n\n\t\tif( typeof override === 'boolean' ) {\n\t\t\toverride ? this.activate() : this.deactivate();\n\t\t}\n\t\telse {\n\t\t\tthis.isActive() ? this.deactivate() : this.activate();\n\t\t}\n\n\t}\n\n\t/**\n\t * Checks if the overview is currently active.\n\t *\n\t * @return {Boolean} true if the overview is active,\n\t * false otherwise\n\t */\n\tisActive() {\n\n\t\treturn this.active;\n\n\t}\n\n\t/**\n\t * Invoked when a slide is and we're in the overview.\n\t *\n\t * @param {object} event\n\t */\n\tonSlideClicked( event ) {\n\n\t\tif( this.isActive() ) {\n\t\t\tevent.preventDefault();\n\n\t\t\tlet element = event.target;\n\n\t\t\twhile( element && !element.nodeName.match( /section/gi ) ) {\n\t\t\t\telement = element.parentNode;\n\t\t\t}\n\n\t\t\tif( element && !element.classList.contains( 'disabled' ) ) {\n\n\t\t\t\tthis.deactivate();\n\n\t\t\t\tif( element.nodeName.match( /section/gi ) ) {\n\t\t\t\t\tlet h = parseInt( element.getAttribute( 'data-index-h' ), 10 ),\n\t\t\t\t\t\tv = parseInt( element.getAttribute( 'data-index-v' ), 10 );\n\n\t\t\t\t\tthis.Reveal.slide( h, v );\n\t\t\t\t}\n\n\t\t\t}\n\t\t}\n\n\t}\n\n}","import { enterFullscreen } from '../utils/util.js'\n\n/**\n * Handles all reveal.js keyboard interactions.\n */\nexport default class Keyboard {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t\t// A key:value map of keyboard keys and descriptions of\n\t\t// the actions they trigger\n\t\tthis.shortcuts = {};\n\n\t\t// Holds custom key code mappings\n\t\tthis.bindings = {};\n\n\t\tthis.onDocumentKeyDown = this.onDocumentKeyDown.bind( this );\n\t\tthis.onDocumentKeyPress = this.onDocumentKeyPress.bind( this );\n\n\t}\n\n\t/**\n\t * Called when the reveal.js config is updated.\n\t */\n\tconfigure( config, oldConfig ) {\n\n\t\tif( config.navigationMode === 'linear' ) {\n\t\t\tthis.shortcuts['→ , ↓ , SPACE , N , L , J'] = 'Next slide';\n\t\t\tthis.shortcuts['← , ↑ , P , H , K'] = 'Previous slide';\n\t\t}\n\t\telse {\n\t\t\tthis.shortcuts['N , SPACE'] = 'Next slide';\n\t\t\tthis.shortcuts['P , Shift SPACE'] = 'Previous slide';\n\t\t\tthis.shortcuts['← , H'] = 'Navigate left';\n\t\t\tthis.shortcuts['→ , L'] = 'Navigate right';\n\t\t\tthis.shortcuts['↑ , K'] = 'Navigate up';\n\t\t\tthis.shortcuts['↓ , J'] = 'Navigate down';\n\t\t}\n\n\t\tthis.shortcuts['Alt + ←/↑/→/↓'] = 'Navigate without fragments';\n\t\tthis.shortcuts['Shift + ←/↑/→/↓'] = 'Jump to first/last slide';\n\t\tthis.shortcuts['B , .'] = 'Pause';\n\t\tthis.shortcuts['F'] = 'Fullscreen';\n\t\tthis.shortcuts['ESC, O'] = 'Slide overview';\n\n\t}\n\n\t/**\n\t * Starts listening for keyboard events.\n\t */\n\tbind() {\n\n\t\tdocument.addEventListener( 'keydown', this.onDocumentKeyDown, false );\n\t\tdocument.addEventListener( 'keypress', this.onDocumentKeyPress, false );\n\n\t}\n\n\t/**\n\t * Stops listening for keyboard events.\n\t */\n\tunbind() {\n\n\t\tdocument.removeEventListener( 'keydown', this.onDocumentKeyDown, false );\n\t\tdocument.removeEventListener( 'keypress', this.onDocumentKeyPress, false );\n\n\t}\n\n\t/**\n\t * Add a custom key binding with optional description to\n\t * be added to the help screen.\n\t */\n\taddKeyBinding( binding, callback ) {\n\n\t\tif( typeof binding === 'object' && binding.keyCode ) {\n\t\t\tthis.bindings[binding.keyCode] = {\n\t\t\t\tcallback: callback,\n\t\t\t\tkey: binding.key,\n\t\t\t\tdescription: binding.description\n\t\t\t};\n\t\t}\n\t\telse {\n\t\t\tthis.bindings[binding] = {\n\t\t\t\tcallback: callback,\n\t\t\t\tkey: null,\n\t\t\t\tdescription: null\n\t\t\t};\n\t\t}\n\n\t}\n\n\t/**\n\t * Removes the specified custom key binding.\n\t */\n\tremoveKeyBinding( keyCode ) {\n\n\t\tdelete this.bindings[keyCode];\n\n\t}\n\n\t/**\n\t * Programmatically triggers a keyboard event\n\t *\n\t * @param {int} keyCode\n\t */\n\ttriggerKey( keyCode ) {\n\n\t\tthis.onDocumentKeyDown( { keyCode } );\n\n\t}\n\n\t/**\n\t * Registers a new shortcut to include in the help overlay\n\t *\n\t * @param {String} key\n\t * @param {String} value\n\t */\n\tregisterKeyboardShortcut( key, value ) {\n\n\t\tthis.shortcuts[key] = value;\n\n\t}\n\n\tgetShortcuts() {\n\n\t\treturn this.shortcuts;\n\n\t}\n\n\tgetBindings() {\n\n\t\treturn this.bindings;\n\n\t}\n\n\t/**\n\t * Handler for the document level 'keypress' event.\n\t *\n\t * @param {object} event\n\t */\n\tonDocumentKeyPress( event ) {\n\n\t\t// Check if the pressed key is question mark\n\t\tif( event.shiftKey && event.charCode === 63 ) {\n\t\t\tthis.Reveal.toggleHelp();\n\t\t}\n\n\t}\n\n\t/**\n\t * Handler for the document level 'keydown' event.\n\t *\n\t * @param {object} event\n\t */\n\tonDocumentKeyDown( event ) {\n\n\t\tlet config = this.Reveal.getConfig();\n\n\t\t// If there's a condition specified and it returns false,\n\t\t// ignore this event\n\t\tif( typeof config.keyboardCondition === 'function' && config.keyboardCondition(event) === false ) {\n\t\t\treturn true;\n\t\t}\n\n\t\t// If keyboardCondition is set, only capture keyboard events\n\t\t// for embedded decks when they are focused\n\t\tif( config.keyboardCondition === 'focused' && !this.Reveal.isFocused() ) {\n\t\t\treturn true;\n\t\t}\n\n\t\t// Shorthand\n\t\tlet keyCode = event.keyCode;\n\n\t\t// Remember if auto-sliding was paused so we can toggle it\n\t\tlet autoSlideWasPaused = !this.Reveal.isAutoSliding();\n\n\t\tthis.Reveal.onUserInput( event );\n\n\t\t// Is there a focused element that could be using the keyboard?\n\t\tlet activeElementIsCE = document.activeElement && document.activeElement.isContentEditable === true;\n\t\tlet activeElementIsInput = document.activeElement && document.activeElement.tagName && /input|textarea/i.test( document.activeElement.tagName );\n\t\tlet activeElementIsNotes = document.activeElement && document.activeElement.className && /speaker-notes/i.test( document.activeElement.className);\n\n\t\t// Whitelist certain modifiers for slide navigation shortcuts\n\t\tlet isNavigationKey = [32, 37, 38, 39, 40, 78, 80].indexOf( event.keyCode ) !== -1;\n\n\t\t// Prevent all other events when a modifier is pressed\n\t\tlet unusedModifier = \t!( isNavigationKey && event.shiftKey || event.altKey ) &&\n\t\t\t\t\t\t\t\t( event.shiftKey || event.altKey || event.ctrlKey || event.metaKey );\n\n\t\t// Disregard the event if there's a focused element or a\n\t\t// keyboard modifier key is present\n\t\tif( activeElementIsCE || activeElementIsInput || activeElementIsNotes || unusedModifier ) return;\n\n\t\t// While paused only allow resume keyboard events; 'b', 'v', '.'\n\t\tlet resumeKeyCodes = [66,86,190,191];\n\t\tlet key;\n\n\t\t// Custom key bindings for togglePause should be able to resume\n\t\tif( typeof config.keyboard === 'object' ) {\n\t\t\tfor( key in config.keyboard ) {\n\t\t\t\tif( config.keyboard[key] === 'togglePause' ) {\n\t\t\t\t\tresumeKeyCodes.push( parseInt( key, 10 ) );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tif( this.Reveal.isPaused() && resumeKeyCodes.indexOf( keyCode ) === -1 ) {\n\t\t\treturn false;\n\t\t}\n\n\t\t// Use linear navigation if we're configured to OR if\n\t\t// the presentation is one-dimensional\n\t\tlet useLinearMode = config.navigationMode === 'linear' || !this.Reveal.hasHorizontalSlides() || !this.Reveal.hasVerticalSlides();\n\n\t\tlet triggered = false;\n\n\t\t// 1. User defined key bindings\n\t\tif( typeof config.keyboard === 'object' ) {\n\n\t\t\tfor( key in config.keyboard ) {\n\n\t\t\t\t// Check if this binding matches the pressed key\n\t\t\t\tif( parseInt( key, 10 ) === keyCode ) {\n\n\t\t\t\t\tlet value = config.keyboard[ key ];\n\n\t\t\t\t\t// Callback function\n\t\t\t\t\tif( typeof value === 'function' ) {\n\t\t\t\t\t\tvalue.apply( null, [ event ] );\n\t\t\t\t\t}\n\t\t\t\t\t// String shortcuts to reveal.js API\n\t\t\t\t\telse if( typeof value === 'string' && typeof this.Reveal[ value ] === 'function' ) {\n\t\t\t\t\t\tthis.Reveal[ value ].call();\n\t\t\t\t\t}\n\n\t\t\t\t\ttriggered = true;\n\n\t\t\t\t}\n\n\t\t\t}\n\n\t\t}\n\n\t\t// 2. Registered custom key bindings\n\t\tif( triggered === false ) {\n\n\t\t\tfor( key in this.bindings ) {\n\n\t\t\t\t// Check if this binding matches the pressed key\n\t\t\t\tif( parseInt( key, 10 ) === keyCode ) {\n\n\t\t\t\t\tlet action = this.bindings[ key ].callback;\n\n\t\t\t\t\t// Callback function\n\t\t\t\t\tif( typeof action === 'function' ) {\n\t\t\t\t\t\taction.apply( null, [ event ] );\n\t\t\t\t\t}\n\t\t\t\t\t// String shortcuts to reveal.js API\n\t\t\t\t\telse if( typeof action === 'string' && typeof this.Reveal[ action ] === 'function' ) {\n\t\t\t\t\t\tthis.Reveal[ action ].call();\n\t\t\t\t\t}\n\n\t\t\t\t\ttriggered = true;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// 3. System defined key bindings\n\t\tif( triggered === false ) {\n\n\t\t\t// Assume true and try to prove false\n\t\t\ttriggered = true;\n\n\t\t\t// P, PAGE UP\n\t\t\tif( keyCode === 80 || keyCode === 33 ) {\n\t\t\t\tthis.Reveal.prev({skipFragments: event.altKey});\n\t\t\t}\n\t\t\t// N, PAGE DOWN\n\t\t\telse if( keyCode === 78 || keyCode === 34 ) {\n\t\t\t\tthis.Reveal.next({skipFragments: event.altKey});\n\t\t\t}\n\t\t\t// H, LEFT\n\t\t\telse if( keyCode === 72 || keyCode === 37 ) {\n\t\t\t\tif( event.shiftKey ) {\n\t\t\t\t\tthis.Reveal.slide( 0 );\n\t\t\t\t}\n\t\t\t\telse if( !this.Reveal.overview.isActive() && useLinearMode ) {\n\t\t\t\t\tthis.Reveal.prev({skipFragments: event.altKey});\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tthis.Reveal.left({skipFragments: event.altKey});\n\t\t\t\t}\n\t\t\t}\n\t\t\t// L, RIGHT\n\t\t\telse if( keyCode === 76 || keyCode === 39 ) {\n\t\t\t\tif( event.shiftKey ) {\n\t\t\t\t\tthis.Reveal.slide( this.Reveal.getHorizontalSlides().length - 1 );\n\t\t\t\t}\n\t\t\t\telse if( !this.Reveal.overview.isActive() && useLinearMode ) {\n\t\t\t\t\tthis.Reveal.next({skipFragments: event.altKey});\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tthis.Reveal.right({skipFragments: event.altKey});\n\t\t\t\t}\n\t\t\t}\n\t\t\t// K, UP\n\t\t\telse if( keyCode === 75 || keyCode === 38 ) {\n\t\t\t\tif( event.shiftKey ) {\n\t\t\t\t\tthis.Reveal.slide( undefined, 0 );\n\t\t\t\t}\n\t\t\t\telse if( !this.Reveal.overview.isActive() && useLinearMode ) {\n\t\t\t\t\tthis.Reveal.prev({skipFragments: event.altKey});\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tthis.Reveal.up({skipFragments: event.altKey});\n\t\t\t\t}\n\t\t\t}\n\t\t\t// J, DOWN\n\t\t\telse if( keyCode === 74 || keyCode === 40 ) {\n\t\t\t\tif( event.shiftKey ) {\n\t\t\t\t\tthis.Reveal.slide( undefined, Number.MAX_VALUE );\n\t\t\t\t}\n\t\t\t\telse if( !this.Reveal.overview.isActive() && useLinearMode ) {\n\t\t\t\t\tthis.Reveal.next({skipFragments: event.altKey});\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tthis.Reveal.down({skipFragments: event.altKey});\n\t\t\t\t}\n\t\t\t}\n\t\t\t// HOME\n\t\t\telse if( keyCode === 36 ) {\n\t\t\t\tthis.Reveal.slide( 0 );\n\t\t\t}\n\t\t\t// END\n\t\t\telse if( keyCode === 35 ) {\n\t\t\t\tthis.Reveal.slide( this.Reveal.getHorizontalSlides().length - 1 );\n\t\t\t}\n\t\t\t// SPACE\n\t\t\telse if( keyCode === 32 ) {\n\t\t\t\tif( this.Reveal.overview.isActive() ) {\n\t\t\t\t\tthis.Reveal.overview.deactivate();\n\t\t\t\t}\n\t\t\t\tif( event.shiftKey ) {\n\t\t\t\t\tthis.Reveal.prev({skipFragments: event.altKey});\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tthis.Reveal.next({skipFragments: event.altKey});\n\t\t\t\t}\n\t\t\t}\n\t\t\t// TWO-SPOT, SEMICOLON, B, V, PERIOD, LOGITECH PRESENTER TOOLS \"BLACK SCREEN\" BUTTON\n\t\t\telse if( keyCode === 58 || keyCode === 59 || keyCode === 66 || keyCode === 86 || keyCode === 190 || keyCode === 191 ) {\n\t\t\t\tthis.Reveal.togglePause();\n\t\t\t}\n\t\t\t// F\n\t\t\telse if( keyCode === 70 ) {\n\t\t\t\tenterFullscreen( config.embedded ? this.Reveal.getViewportElement() : document.documentElement );\n\t\t\t}\n\t\t\t// A\n\t\t\telse if( keyCode === 65 ) {\n\t\t\t\tif ( config.autoSlideStoppable ) {\n\t\t\t\t\tthis.Reveal.toggleAutoSlide( autoSlideWasPaused );\n\t\t\t\t}\n\t\t\t}\n\t\t\telse {\n\t\t\t\ttriggered = false;\n\t\t\t}\n\n\t\t}\n\n\t\t// If the input resulted in a triggered action we should prevent\n\t\t// the browsers default behavior\n\t\tif( triggered ) {\n\t\t\tevent.preventDefault && event.preventDefault();\n\t\t}\n\t\t// ESC or O key\n\t\telse if( keyCode === 27 || keyCode === 79 ) {\n\t\t\tif( this.Reveal.closeOverlay() === false ) {\n\t\t\t\tthis.Reveal.overview.toggle();\n\t\t\t}\n\n\t\t\tevent.preventDefault && event.preventDefault();\n\t\t}\n\n\t\t// If auto-sliding is enabled we need to cue up\n\t\t// another timeout\n\t\tthis.Reveal.cueAutoSlide();\n\n\t}\n\n}","/**\n * Reads and writes the URL based on reveal.js' current state.\n */\nexport default class Location {\n\n\t// The minimum number of milliseconds that must pass between\n\t// calls to history.replaceState\n\tMAX_REPLACE_STATE_FREQUENCY = 1000\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t\t// Delays updates to the URL due to a Chrome thumbnailer bug\n\t\tthis.writeURLTimeout = 0;\n\n\t\tthis.replaceStateTimestamp = 0;\n\n\t\tthis.onWindowHashChange = this.onWindowHashChange.bind( this );\n\n\t}\n\n\tbind() {\n\n\t\twindow.addEventListener( 'hashchange', this.onWindowHashChange, false );\n\n\t}\n\n\tunbind() {\n\n\t\twindow.removeEventListener( 'hashchange', this.onWindowHashChange, false );\n\n\t}\n\n\t/**\n\t * Returns the slide indices for the given hash link.\n\t *\n\t * @param {string} [hash] the hash string that we want to\n\t * find the indices for\n\t *\n\t * @returns slide indices or null\n\t */\n\tgetIndicesFromHash( hash=window.location.hash ) {\n\n\t\t// Attempt to parse the hash as either an index or name\n\t\tlet name = hash.replace( /^#\\/?/, '' );\n\t\tlet bits = name.split( '/' );\n\n\t\t// If the first bit is not fully numeric and there is a name we\n\t\t// can assume that this is a named link\n\t\tif( !/^[0-9]*$/.test( bits[0] ) && name.length ) {\n\t\t\tlet element;\n\n\t\t\tlet f;\n\n\t\t\t// Parse named links with fragments (#/named-link/2)\n\t\t\tif( /\\/[-\\d]+$/g.test( name ) ) {\n\t\t\t\tf = parseInt( name.split( '/' ).pop(), 10 );\n\t\t\t\tf = isNaN(f) ? undefined : f;\n\t\t\t\tname = name.split( '/' ).shift();\n\t\t\t}\n\n\t\t\t// Ensure the named link is a valid HTML ID attribute\n\t\t\ttry {\n\t\t\t\telement = document.getElementById( decodeURIComponent( name ) );\n\t\t\t}\n\t\t\tcatch ( error ) { }\n\n\t\t\tif( element ) {\n\t\t\t\treturn { ...this.Reveal.getIndices( element ), f };\n\t\t\t}\n\t\t}\n\t\telse {\n\t\t\tconst config = this.Reveal.getConfig();\n\t\t\tlet hashIndexBase = config.hashOneBasedIndex ? 1 : 0;\n\n\t\t\t// Read the index components of the hash\n\t\t\tlet h = ( parseInt( bits[0], 10 ) - hashIndexBase ) || 0,\n\t\t\t\tv = ( parseInt( bits[1], 10 ) - hashIndexBase ) || 0,\n\t\t\t\tf;\n\n\t\t\tif( config.fragmentInURL ) {\n\t\t\t\tf = parseInt( bits[2], 10 );\n\t\t\t\tif( isNaN( f ) ) {\n\t\t\t\t\tf = undefined;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\treturn { h, v, f };\n\t\t}\n\n\t\t// The hash couldn't be parsed or no matching named link was found\n\t\treturn null\n\n\t}\n\n\t/**\n\t * Reads the current URL (hash) and navigates accordingly.\n\t */\n\treadURL() {\n\n\t\tconst currentIndices = this.Reveal.getIndices();\n\t\tconst newIndices = this.getIndicesFromHash();\n\n\t\tif( newIndices ) {\n\t\t\tif( ( newIndices.h !== currentIndices.h || newIndices.v !== currentIndices.v || newIndices.f !== undefined ) ) {\n\t\t\t\t\tthis.Reveal.slide( newIndices.h, newIndices.v, newIndices.f );\n\t\t\t}\n\t\t}\n\t\t// If no new indices are available, we're trying to navigate to\n\t\t// a slide hash that does not exist\n\t\telse {\n\t\t\tthis.Reveal.slide( currentIndices.h || 0, currentIndices.v || 0 );\n\t\t}\n\n\t}\n\n\t/**\n\t * Updates the page URL (hash) to reflect the current\n\t * state.\n\t *\n\t * @param {number} delay The time in ms to wait before\n\t * writing the hash\n\t */\n\twriteURL( delay ) {\n\n\t\tlet config = this.Reveal.getConfig();\n\t\tlet currentSlide = this.Reveal.getCurrentSlide();\n\n\t\t// Make sure there's never more than one timeout running\n\t\tclearTimeout( this.writeURLTimeout );\n\n\t\t// If a delay is specified, timeout this call\n\t\tif( typeof delay === 'number' ) {\n\t\t\tthis.writeURLTimeout = setTimeout( this.writeURL, delay );\n\t\t}\n\t\telse if( currentSlide ) {\n\n\t\t\tlet hash = this.getHash();\n\n\t\t\t// If we're configured to push to history OR the history\n\t\t\t// API is not avaialble.\n\t\t\tif( config.history ) {\n\t\t\t\twindow.location.hash = hash;\n\t\t\t}\n\t\t\t// If we're configured to reflect the current slide in the\n\t\t\t// URL without pushing to history.\n\t\t\telse if( config.hash ) {\n\t\t\t\t// If the hash is empty, don't add it to the URL\n\t\t\t\tif( hash === '/' ) {\n\t\t\t\t\tthis.debouncedReplaceState( window.location.pathname + window.location.search );\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tthis.debouncedReplaceState( '#' + hash );\n\t\t\t\t}\n\t\t\t}\n\t\t\t// UPDATE: The below nuking of all hash changes breaks\n\t\t\t// anchors on pages where reveal.js is running. Removed\n\t\t\t// in 4.0. Why was it here in the first place? ¯\\_(ツ)_/¯\n\t\t\t//\n\t\t\t// If history and hash are both disabled, a hash may still\n\t\t\t// be added to the URL by clicking on a href with a hash\n\t\t\t// target. Counter this by always removing the hash.\n\t\t\t// else {\n\t\t\t// \twindow.history.replaceState( null, null, window.location.pathname + window.location.search );\n\t\t\t// }\n\n\t\t}\n\n\t}\n\n\treplaceState( url ) {\n\n\t\twindow.history.replaceState( null, null, url );\n\t\tthis.replaceStateTimestamp = Date.now();\n\n\t}\n\n\tdebouncedReplaceState( url ) {\n\n\t\tclearTimeout( this.replaceStateTimeout );\n\n\t\tif( Date.now() - this.replaceStateTimestamp > this.MAX_REPLACE_STATE_FREQUENCY ) {\n\t\t\tthis.replaceState( url );\n\t\t}\n\t\telse {\n\t\t\tthis.replaceStateTimeout = setTimeout( () => this.replaceState( url ), this.MAX_REPLACE_STATE_FREQUENCY );\n\t\t}\n\n\t}\n\n\t/**\n\t * Return a hash URL that will resolve to the given slide location.\n\t *\n\t * @param {HTMLElement} [slide=currentSlide] The slide to link to\n\t */\n\tgetHash( slide ) {\n\n\t\tlet url = '/';\n\n\t\t// Attempt to create a named link based on the slide's ID\n\t\tlet s = slide || this.Reveal.getCurrentSlide();\n\t\tlet id = s ? s.getAttribute( 'id' ) : null;\n\t\tif( id ) {\n\t\t\tid = encodeURIComponent( id );\n\t\t}\n\n\t\tlet index = this.Reveal.getIndices( slide );\n\t\tif( !this.Reveal.getConfig().fragmentInURL ) {\n\t\t\tindex.f = undefined;\n\t\t}\n\n\t\t// If the current slide has an ID, use that as a named link,\n\t\t// but we don't support named links with a fragment index\n\t\tif( typeof id === 'string' && id.length ) {\n\t\t\turl = '/' + id;\n\n\t\t\t// If there is also a fragment, append that at the end\n\t\t\t// of the named link, like: #/named-link/2\n\t\t\tif( index.f >= 0 ) url += '/' + index.f;\n\t\t}\n\t\t// Otherwise use the /h/v index\n\t\telse {\n\t\t\tlet hashIndexBase = this.Reveal.getConfig().hashOneBasedIndex ? 1 : 0;\n\t\t\tif( index.h > 0 || index.v > 0 || index.f >= 0 ) url += index.h + hashIndexBase;\n\t\t\tif( index.v > 0 || index.f >= 0 ) url += '/' + (index.v + hashIndexBase );\n\t\t\tif( index.f >= 0 ) url += '/' + index.f;\n\t\t}\n\n\t\treturn url;\n\n\t}\n\n\t/**\n\t * Handler for the window level 'hashchange' event.\n\t *\n\t * @param {object} [event]\n\t */\n\tonWindowHashChange( event ) {\n\n\t\tthis.readURL();\n\n\t}\n\n}","import { queryAll } from '../utils/util.js'\nimport { isAndroid } from '../utils/device.js'\n\n/**\n * Manages our presentation controls. This includes both\n * the built-in control arrows as well as event monitoring\n * of any elements within the presentation with either of the\n * following helper classes:\n * - .navigate-up\n * - .navigate-right\n * - .navigate-down\n * - .navigate-left\n * - .navigate-next\n * - .navigate-prev\n */\nexport default class Controls {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t\tthis.onNavigateLeftClicked = this.onNavigateLeftClicked.bind( this );\n\t\tthis.onNavigateRightClicked = this.onNavigateRightClicked.bind( this );\n\t\tthis.onNavigateUpClicked = this.onNavigateUpClicked.bind( this );\n\t\tthis.onNavigateDownClicked = this.onNavigateDownClicked.bind( this );\n\t\tthis.onNavigatePrevClicked = this.onNavigatePrevClicked.bind( this );\n\t\tthis.onNavigateNextClicked = this.onNavigateNextClicked.bind( this );\n\n\t}\n\n\trender() {\n\n\t\tconst rtl = this.Reveal.getConfig().rtl;\n\t\tconst revealElement = this.Reveal.getRevealElement();\n\n\t\tthis.element = document.createElement( 'aside' );\n\t\tthis.element.className = 'controls';\n\t\tthis.element.innerHTML =\n\t\t\t`\n\t\t\t\n\t\t\t\n\t\t\t`;\n\n\t\tthis.Reveal.getRevealElement().appendChild( this.element );\n\n\t\t// There can be multiple instances of controls throughout the page\n\t\tthis.controlsLeft = queryAll( revealElement, '.navigate-left' );\n\t\tthis.controlsRight = queryAll( revealElement, '.navigate-right' );\n\t\tthis.controlsUp = queryAll( revealElement, '.navigate-up' );\n\t\tthis.controlsDown = queryAll( revealElement, '.navigate-down' );\n\t\tthis.controlsPrev = queryAll( revealElement, '.navigate-prev' );\n\t\tthis.controlsNext = queryAll( revealElement, '.navigate-next' );\n\n\t\t// The left, right and down arrows in the standard reveal.js controls\n\t\tthis.controlsRightArrow = this.element.querySelector( '.navigate-right' );\n\t\tthis.controlsLeftArrow = this.element.querySelector( '.navigate-left' );\n\t\tthis.controlsDownArrow = this.element.querySelector( '.navigate-down' );\n\n\t}\n\n\t/**\n\t * Called when the reveal.js config is updated.\n\t */\n\tconfigure( config, oldConfig ) {\n\n\t\tthis.element.style.display = config.controls ? 'block' : 'none';\n\n\t\tthis.element.setAttribute( 'data-controls-layout', config.controlsLayout );\n\t\tthis.element.setAttribute( 'data-controls-back-arrows', config.controlsBackArrows );\n\n\t}\n\n\tbind() {\n\n\t\t// Listen to both touch and click events, in case the device\n\t\t// supports both\n\t\tlet pointerEvents = [ 'touchstart', 'click' ];\n\n\t\t// Only support touch for Android, fixes double navigations in\n\t\t// stock browser\n\t\tif( isAndroid ) {\n\t\t\tpointerEvents = [ 'touchstart' ];\n\t\t}\n\n\t\tpointerEvents.forEach( eventName => {\n\t\t\tthis.controlsLeft.forEach( el => el.addEventListener( eventName, this.onNavigateLeftClicked, false ) );\n\t\t\tthis.controlsRight.forEach( el => el.addEventListener( eventName, this.onNavigateRightClicked, false ) );\n\t\t\tthis.controlsUp.forEach( el => el.addEventListener( eventName, this.onNavigateUpClicked, false ) );\n\t\t\tthis.controlsDown.forEach( el => el.addEventListener( eventName, this.onNavigateDownClicked, false ) );\n\t\t\tthis.controlsPrev.forEach( el => el.addEventListener( eventName, this.onNavigatePrevClicked, false ) );\n\t\t\tthis.controlsNext.forEach( el => el.addEventListener( eventName, this.onNavigateNextClicked, false ) );\n\t\t} );\n\n\t}\n\n\tunbind() {\n\n\t\t[ 'touchstart', 'click' ].forEach( eventName => {\n\t\t\tthis.controlsLeft.forEach( el => el.removeEventListener( eventName, this.onNavigateLeftClicked, false ) );\n\t\t\tthis.controlsRight.forEach( el => el.removeEventListener( eventName, this.onNavigateRightClicked, false ) );\n\t\t\tthis.controlsUp.forEach( el => el.removeEventListener( eventName, this.onNavigateUpClicked, false ) );\n\t\t\tthis.controlsDown.forEach( el => el.removeEventListener( eventName, this.onNavigateDownClicked, false ) );\n\t\t\tthis.controlsPrev.forEach( el => el.removeEventListener( eventName, this.onNavigatePrevClicked, false ) );\n\t\t\tthis.controlsNext.forEach( el => el.removeEventListener( eventName, this.onNavigateNextClicked, false ) );\n\t\t} );\n\n\t}\n\n\t/**\n\t * Updates the state of all control/navigation arrows.\n\t */\n\tupdate() {\n\n\t\tlet routes = this.Reveal.availableRoutes();\n\n\t\t// Remove the 'enabled' class from all directions\n\t\t[...this.controlsLeft, ...this.controlsRight, ...this.controlsUp, ...this.controlsDown, ...this.controlsPrev, ...this.controlsNext].forEach( node => {\n\t\t\tnode.classList.remove( 'enabled', 'fragmented' );\n\n\t\t\t// Set 'disabled' attribute on all directions\n\t\t\tnode.setAttribute( 'disabled', 'disabled' );\n\t\t} );\n\n\t\t// Add the 'enabled' class to the available routes; remove 'disabled' attribute to enable buttons\n\t\tif( routes.left ) this.controlsLeft.forEach( el => { el.classList.add( 'enabled' ); el.removeAttribute( 'disabled' ); } );\n\t\tif( routes.right ) this.controlsRight.forEach( el => { el.classList.add( 'enabled' ); el.removeAttribute( 'disabled' ); } );\n\t\tif( routes.up ) this.controlsUp.forEach( el => { el.classList.add( 'enabled' ); el.removeAttribute( 'disabled' ); } );\n\t\tif( routes.down ) this.controlsDown.forEach( el => { el.classList.add( 'enabled' ); el.removeAttribute( 'disabled' ); } );\n\n\t\t// Prev/next buttons\n\t\tif( routes.left || routes.up ) this.controlsPrev.forEach( el => { el.classList.add( 'enabled' ); el.removeAttribute( 'disabled' ); } );\n\t\tif( routes.right || routes.down ) this.controlsNext.forEach( el => { el.classList.add( 'enabled' ); el.removeAttribute( 'disabled' ); } );\n\n\t\t// Highlight fragment directions\n\t\tlet currentSlide = this.Reveal.getCurrentSlide();\n\t\tif( currentSlide ) {\n\n\t\t\tlet fragmentsRoutes = this.Reveal.fragments.availableRoutes();\n\n\t\t\t// Always apply fragment decorator to prev/next buttons\n\t\t\tif( fragmentsRoutes.prev ) this.controlsPrev.forEach( el => { el.classList.add( 'fragmented', 'enabled' ); el.removeAttribute( 'disabled' ); } );\n\t\t\tif( fragmentsRoutes.next ) this.controlsNext.forEach( el => { el.classList.add( 'fragmented', 'enabled' ); el.removeAttribute( 'disabled' ); } );\n\n\t\t\t// Apply fragment decorators to directional buttons based on\n\t\t\t// what slide axis they are in\n\t\t\tif( this.Reveal.isVerticalSlide( currentSlide ) ) {\n\t\t\t\tif( fragmentsRoutes.prev ) this.controlsUp.forEach( el => { el.classList.add( 'fragmented', 'enabled' ); el.removeAttribute( 'disabled' ); } );\n\t\t\t\tif( fragmentsRoutes.next ) this.controlsDown.forEach( el => { el.classList.add( 'fragmented', 'enabled' ); el.removeAttribute( 'disabled' ); } );\n\t\t\t}\n\t\t\telse {\n\t\t\t\tif( fragmentsRoutes.prev ) this.controlsLeft.forEach( el => { el.classList.add( 'fragmented', 'enabled' ); el.removeAttribute( 'disabled' ); } );\n\t\t\t\tif( fragmentsRoutes.next ) this.controlsRight.forEach( el => { el.classList.add( 'fragmented', 'enabled' ); el.removeAttribute( 'disabled' ); } );\n\t\t\t}\n\n\t\t}\n\n\t\tif( this.Reveal.getConfig().controlsTutorial ) {\n\n\t\t\tlet indices = this.Reveal.getIndices();\n\n\t\t\t// Highlight control arrows with an animation to ensure\n\t\t\t// that the viewer knows how to navigate\n\t\t\tif( !this.Reveal.hasNavigatedVertically() && routes.down ) {\n\t\t\t\tthis.controlsDownArrow.classList.add( 'highlight' );\n\t\t\t}\n\t\t\telse {\n\t\t\t\tthis.controlsDownArrow.classList.remove( 'highlight' );\n\n\t\t\t\tif( this.Reveal.getConfig().rtl ) {\n\n\t\t\t\t\tif( !this.Reveal.hasNavigatedHorizontally() && routes.left && indices.v === 0 ) {\n\t\t\t\t\t\tthis.controlsLeftArrow.classList.add( 'highlight' );\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tthis.controlsLeftArrow.classList.remove( 'highlight' );\n\t\t\t\t\t}\n\n\t\t\t\t} else {\n\n\t\t\t\t\tif( !this.Reveal.hasNavigatedHorizontally() && routes.right && indices.v === 0 ) {\n\t\t\t\t\t\tthis.controlsRightArrow.classList.add( 'highlight' );\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tthis.controlsRightArrow.classList.remove( 'highlight' );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\tdestroy() {\n\n\t\tthis.unbind();\n\t\tthis.element.remove();\n\n\t}\n\n\t/**\n\t * Event handlers for navigation control buttons.\n\t */\n\tonNavigateLeftClicked( event ) {\n\n\t\tevent.preventDefault();\n\t\tthis.Reveal.onUserInput();\n\n\t\tif( this.Reveal.getConfig().navigationMode === 'linear' ) {\n\t\t\tthis.Reveal.prev();\n\t\t}\n\t\telse {\n\t\t\tthis.Reveal.left();\n\t\t}\n\n\t}\n\n\tonNavigateRightClicked( event ) {\n\n\t\tevent.preventDefault();\n\t\tthis.Reveal.onUserInput();\n\n\t\tif( this.Reveal.getConfig().navigationMode === 'linear' ) {\n\t\t\tthis.Reveal.next();\n\t\t}\n\t\telse {\n\t\t\tthis.Reveal.right();\n\t\t}\n\n\t}\n\n\tonNavigateUpClicked( event ) {\n\n\t\tevent.preventDefault();\n\t\tthis.Reveal.onUserInput();\n\n\t\tthis.Reveal.up();\n\n\t}\n\n\tonNavigateDownClicked( event ) {\n\n\t\tevent.preventDefault();\n\t\tthis.Reveal.onUserInput();\n\n\t\tthis.Reveal.down();\n\n\t}\n\n\tonNavigatePrevClicked( event ) {\n\n\t\tevent.preventDefault();\n\t\tthis.Reveal.onUserInput();\n\n\t\tthis.Reveal.prev();\n\n\t}\n\n\tonNavigateNextClicked( event ) {\n\n\t\tevent.preventDefault();\n\t\tthis.Reveal.onUserInput();\n\n\t\tthis.Reveal.next();\n\n\t}\n\n\n}","/**\n * Creates a visual progress bar for the presentation.\n */\nexport default class Progress {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t\tthis.onProgressClicked = this.onProgressClicked.bind( this );\n\n\t}\n\n\trender() {\n\n\t\tthis.element = document.createElement( 'div' );\n\t\tthis.element.className = 'progress';\n\t\tthis.Reveal.getRevealElement().appendChild( this.element );\n\n\t\tthis.bar = document.createElement( 'span' );\n\t\tthis.element.appendChild( this.bar );\n\n\t}\n\n\t/**\n\t * Called when the reveal.js config is updated.\n\t */\n\tconfigure( config, oldConfig ) {\n\n\t\tthis.element.style.display = config.progress ? 'block' : 'none';\n\n\t}\n\n\tbind() {\n\n\t\tif( this.Reveal.getConfig().progress && this.element ) {\n\t\t\tthis.element.addEventListener( 'click', this.onProgressClicked, false );\n\t\t}\n\n\t}\n\n\tunbind() {\n\n\t\tif ( this.Reveal.getConfig().progress && this.element ) {\n\t\t\tthis.element.removeEventListener( 'click', this.onProgressClicked, false );\n\t\t}\n\n\t}\n\n\t/**\n\t * Updates the progress bar to reflect the current slide.\n\t */\n\tupdate() {\n\n\t\t// Update progress if enabled\n\t\tif( this.Reveal.getConfig().progress && this.bar ) {\n\n\t\t\tlet scale = this.Reveal.getProgress();\n\n\t\t\t// Don't fill the progress bar if there's only one slide\n\t\t\tif( this.Reveal.getTotalSlides() < 2 ) {\n\t\t\t\tscale = 0;\n\t\t\t}\n\n\t\t\tthis.bar.style.transform = 'scaleX('+ scale +')';\n\n\t\t}\n\n\t}\n\n\tgetMaxWidth() {\n\n\t\treturn this.Reveal.getRevealElement().offsetWidth;\n\n\t}\n\n\t/**\n\t * Clicking on the progress bar results in a navigation to the\n\t * closest approximate horizontal slide using this equation:\n\t *\n\t * ( clickX / presentationWidth ) * numberOfSlides\n\t *\n\t * @param {object} event\n\t */\n\tonProgressClicked( event ) {\n\n\t\tthis.Reveal.onUserInput( event );\n\n\t\tevent.preventDefault();\n\n\t\tlet slides = this.Reveal.getSlides();\n\t\tlet slidesTotal = slides.length;\n\t\tlet slideIndex = Math.floor( ( event.clientX / this.getMaxWidth() ) * slidesTotal );\n\n\t\tif( this.Reveal.getConfig().rtl ) {\n\t\t\tslideIndex = slidesTotal - slideIndex;\n\t\t}\n\n\t\tlet targetIndices = this.Reveal.getIndices(slides[slideIndex]);\n\t\tthis.Reveal.slide( targetIndices.h, targetIndices.v );\n\n\t}\n\n\tdestroy() {\n\n\t\tthis.element.remove();\n\n\t}\n\n}","/**\n * Handles hiding of the pointer/cursor when inactive.\n */\nexport default class Pointer {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t\t// Throttles mouse wheel navigation\n\t\tthis.lastMouseWheelStep = 0;\n\n\t\t// Is the mouse pointer currently hidden from view\n\t\tthis.cursorHidden = false;\n\n\t\t// Timeout used to determine when the cursor is inactive\n\t\tthis.cursorInactiveTimeout = 0;\n\n\t\tthis.onDocumentCursorActive = this.onDocumentCursorActive.bind( this );\n\t\tthis.onDocumentMouseScroll = this.onDocumentMouseScroll.bind( this );\n\n\t}\n\n\t/**\n\t * Called when the reveal.js config is updated.\n\t */\n\tconfigure( config, oldConfig ) {\n\n\t\tif( config.mouseWheel ) {\n\t\t\tdocument.addEventListener( 'DOMMouseScroll', this.onDocumentMouseScroll, false ); // FF\n\t\t\tdocument.addEventListener( 'mousewheel', this.onDocumentMouseScroll, false );\n\t\t}\n\t\telse {\n\t\t\tdocument.removeEventListener( 'DOMMouseScroll', this.onDocumentMouseScroll, false ); // FF\n\t\t\tdocument.removeEventListener( 'mousewheel', this.onDocumentMouseScroll, false );\n\t\t}\n\n\t\t// Auto-hide the mouse pointer when its inactive\n\t\tif( config.hideInactiveCursor ) {\n\t\t\tdocument.addEventListener( 'mousemove', this.onDocumentCursorActive, false );\n\t\t\tdocument.addEventListener( 'mousedown', this.onDocumentCursorActive, false );\n\t\t}\n\t\telse {\n\t\t\tthis.showCursor();\n\n\t\t\tdocument.removeEventListener( 'mousemove', this.onDocumentCursorActive, false );\n\t\t\tdocument.removeEventListener( 'mousedown', this.onDocumentCursorActive, false );\n\t\t}\n\n\t}\n\n\t/**\n\t * Shows the mouse pointer after it has been hidden with\n\t * #hideCursor.\n\t */\n\tshowCursor() {\n\n\t\tif( this.cursorHidden ) {\n\t\t\tthis.cursorHidden = false;\n\t\t\tthis.Reveal.getRevealElement().style.cursor = '';\n\t\t}\n\n\t}\n\n\t/**\n\t * Hides the mouse pointer when it's on top of the .reveal\n\t * container.\n\t */\n\thideCursor() {\n\n\t\tif( this.cursorHidden === false ) {\n\t\t\tthis.cursorHidden = true;\n\t\t\tthis.Reveal.getRevealElement().style.cursor = 'none';\n\t\t}\n\n\t}\n\n\tdestroy() {\n\n\t\tthis.showCursor();\n\n\t\tdocument.removeEventListener( 'DOMMouseScroll', this.onDocumentMouseScroll, false );\n\t\tdocument.removeEventListener( 'mousewheel', this.onDocumentMouseScroll, false );\n\t\tdocument.removeEventListener( 'mousemove', this.onDocumentCursorActive, false );\n\t\tdocument.removeEventListener( 'mousedown', this.onDocumentCursorActive, false );\n\n\t}\n\n\t/**\n\t * Called whenever there is mouse input at the document level\n\t * to determine if the cursor is active or not.\n\t *\n\t * @param {object} event\n\t */\n\tonDocumentCursorActive( event ) {\n\n\t\tthis.showCursor();\n\n\t\tclearTimeout( this.cursorInactiveTimeout );\n\n\t\tthis.cursorInactiveTimeout = setTimeout( this.hideCursor.bind( this ), this.Reveal.getConfig().hideCursorTime );\n\n\t}\n\n\t/**\n\t * Handles mouse wheel scrolling, throttled to avoid skipping\n\t * multiple slides.\n\t *\n\t * @param {object} event\n\t */\n\tonDocumentMouseScroll( event ) {\n\n\t\tif( Date.now() - this.lastMouseWheelStep > 1000 ) {\n\n\t\t\tthis.lastMouseWheelStep = Date.now();\n\n\t\t\tlet delta = event.detail || -event.wheelDelta;\n\t\t\tif( delta > 0 ) {\n\t\t\t\tthis.Reveal.next();\n\t\t\t}\n\t\t\telse if( delta < 0 ) {\n\t\t\t\tthis.Reveal.prev();\n\t\t\t}\n\n\t\t}\n\n\t}\n\n}","/**\n * Loads a JavaScript file from the given URL and executes it.\n *\n * @param {string} url Address of the .js file to load\n * @param {function} callback Method to invoke when the script\n * has loaded and executed\n */\nexport const loadScript = ( url, callback ) => {\n\n\tconst script = document.createElement( 'script' );\n\tscript.type = 'text/javascript';\n\tscript.async = false;\n\tscript.defer = false;\n\tscript.src = url;\n\n\tif( typeof callback === 'function' ) {\n\n\t\t// Success callback\n\t\tscript.onload = script.onreadystatechange = event => {\n\t\t\tif( event.type === 'load' || /loaded|complete/.test( script.readyState ) ) {\n\n\t\t\t\t// Kill event listeners\n\t\t\t\tscript.onload = script.onreadystatechange = script.onerror = null;\n\n\t\t\t\tcallback();\n\n\t\t\t}\n\t\t};\n\n\t\t// Error callback\n\t\tscript.onerror = err => {\n\n\t\t\t// Kill event listeners\n\t\t\tscript.onload = script.onreadystatechange = script.onerror = null;\n\n\t\t\tcallback( new Error( 'Failed loading script: ' + script.src + '\\n' + err ) );\n\n\t\t};\n\n\t}\n\n\t// Append the script at the end of \n\tconst head = document.querySelector( 'head' );\n\thead.insertBefore( script, head.lastChild );\n\n}","import { loadScript } from '../utils/loader.js'\n\n/**\n * Manages loading and registering of reveal.js plugins.\n */\nexport default class Plugins {\n\n\tconstructor( reveal ) {\n\n\t\tthis.Reveal = reveal;\n\n\t\t// Flags our current state (idle -> loading -> loaded)\n\t\tthis.state = 'idle';\n\n\t\t// An id:instance map of currently registed plugins\n\t\tthis.registeredPlugins = {};\n\n\t\tthis.asyncDependencies = [];\n\n\t}\n\n\t/**\n\t * Loads reveal.js dependencies, registers and\n\t * initializes plugins.\n\t *\n\t * Plugins are direct references to a reveal.js plugin\n\t * object that we register and initialize after any\n\t * synchronous dependencies have loaded.\n\t *\n\t * Dependencies are defined via the 'dependencies' config\n\t * option and will be loaded prior to starting reveal.js.\n\t * Some dependencies may have an 'async' flag, if so they\n\t * will load after reveal.js has been started up.\n\t */\n\tload( plugins, dependencies ) {\n\n\t\tthis.state = 'loading';\n\n\t\tplugins.forEach( this.registerPlugin.bind( this ) );\n\n\t\treturn new Promise( resolve => {\n\n\t\t\tlet scripts = [],\n\t\t\t\tscriptsToLoad = 0;\n\n\t\t\tdependencies.forEach( s => {\n\t\t\t\t// Load if there's no condition or the condition is truthy\n\t\t\t\tif( !s.condition || s.condition() ) {\n\t\t\t\t\tif( s.async ) {\n\t\t\t\t\t\tthis.asyncDependencies.push( s );\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tscripts.push( s );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t} );\n\n\t\t\tif( scripts.length ) {\n\t\t\t\tscriptsToLoad = scripts.length;\n\n\t\t\t\tconst scriptLoadedCallback = (s) => {\n\t\t\t\t\tif( s && typeof s.callback === 'function' ) s.callback();\n\n\t\t\t\t\tif( --scriptsToLoad === 0 ) {\n\t\t\t\t\t\tthis.initPlugins().then( resolve );\n\t\t\t\t\t}\n\t\t\t\t};\n\n\t\t\t\t// Load synchronous scripts\n\t\t\t\tscripts.forEach( s => {\n\t\t\t\t\tif( typeof s.id === 'string' ) {\n\t\t\t\t\t\tthis.registerPlugin( s );\n\t\t\t\t\t\tscriptLoadedCallback( s );\n\t\t\t\t\t}\n\t\t\t\t\telse if( typeof s.src === 'string' ) {\n\t\t\t\t\t\tloadScript( s.src, () => scriptLoadedCallback(s) );\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tconsole.warn( 'Unrecognized plugin format', s );\n\t\t\t\t\t\tscriptLoadedCallback();\n\t\t\t\t\t}\n\t\t\t\t} );\n\t\t\t}\n\t\t\telse {\n\t\t\t\tthis.initPlugins().then( resolve );\n\t\t\t}\n\n\t\t} );\n\n\t}\n\n\t/**\n\t * Initializes our plugins and waits for them to be ready\n\t * before proceeding.\n\t */\n\tinitPlugins() {\n\n\t\treturn new Promise( resolve => {\n\n\t\t\tlet pluginValues = Object.values( this.registeredPlugins );\n\t\t\tlet pluginsToInitialize = pluginValues.length;\n\n\t\t\t// If there are no plugins, skip this step\n\t\t\tif( pluginsToInitialize === 0 ) {\n\t\t\t\tthis.loadAsync().then( resolve );\n\t\t\t}\n\t\t\t// ... otherwise initialize plugins\n\t\t\telse {\n\n\t\t\t\tlet initNextPlugin;\n\n\t\t\t\tlet afterPlugInitialized = () => {\n\t\t\t\t\tif( --pluginsToInitialize === 0 ) {\n\t\t\t\t\t\tthis.loadAsync().then( resolve );\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tinitNextPlugin();\n\t\t\t\t\t}\n\t\t\t\t};\n\n\t\t\t\tlet i = 0;\n\n\t\t\t\t// Initialize plugins serially\n\t\t\t\tinitNextPlugin = () => {\n\n\t\t\t\t\tlet plugin = pluginValues[i++];\n\n\t\t\t\t\t// If the plugin has an 'init' method, invoke it\n\t\t\t\t\tif( typeof plugin.init === 'function' ) {\n\t\t\t\t\t\tlet promise = plugin.init( this.Reveal );\n\n\t\t\t\t\t\t// If the plugin returned a Promise, wait for it\n\t\t\t\t\t\tif( promise && typeof promise.then === 'function' ) {\n\t\t\t\t\t\t\tpromise.then( afterPlugInitialized );\n\t\t\t\t\t\t}\n\t\t\t\t\t\telse {\n\t\t\t\t\t\t\tafterPlugInitialized();\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tafterPlugInitialized();\n\t\t\t\t\t}\n\n\t\t\t\t}\n\n\t\t\t\tinitNextPlugin();\n\n\t\t\t}\n\n\t\t} )\n\n\t}\n\n\t/**\n\t * Loads all async reveal.js dependencies.\n\t */\n\tloadAsync() {\n\n\t\tthis.state = 'loaded';\n\n\t\tif( this.asyncDependencies.length ) {\n\t\t\tthis.asyncDependencies.forEach( s => {\n\t\t\t\tloadScript( s.src, s.callback );\n\t\t\t} );\n\t\t}\n\n\t\treturn Promise.resolve();\n\n\t}\n\n\t/**\n\t * Registers a new plugin with this reveal.js instance.\n\t *\n\t * reveal.js waits for all regisered plugins to initialize\n\t * before considering itself ready, as long as the plugin\n\t * is registered before calling `Reveal.initialize()`.\n\t */\n\tregisterPlugin( plugin ) {\n\n\t\t// Backwards compatibility to make reveal.js ~3.9.0\n\t\t// plugins work with reveal.js 4.0.0\n\t\tif( arguments.length === 2 && typeof arguments[0] === 'string' ) {\n\t\t\tplugin = arguments[1];\n\t\t\tplugin.id = arguments[0];\n\t\t}\n\t\t// Plugin can optionally be a function which we call\n\t\t// to create an instance of the plugin\n\t\telse if( typeof plugin === 'function' ) {\n\t\t\tplugin = plugin();\n\t\t}\n\n\t\tlet id = plugin.id;\n\n\t\tif( typeof id !== 'string' ) {\n\t\t\tconsole.warn( 'Unrecognized plugin format; can\\'t find plugin.id', plugin );\n\t\t}\n\t\telse if( this.registeredPlugins[id] === undefined ) {\n\t\t\tthis.registeredPlugins[id] = plugin;\n\n\t\t\t// If a plugin is registered after reveal.js is loaded,\n\t\t\t// initialize it right away\n\t\t\tif( this.state === 'loaded' && typeof plugin.init === 'function' ) {\n\t\t\t\tplugin.init( this.Reveal );\n\t\t\t}\n\t\t}\n\t\telse {\n\t\t\tconsole.warn( 'reveal.js: \"'+ id +'\" plugin has already been registered' );\n\t\t}\n\n\t}\n\n\t/**\n\t * Checks if a specific plugin has been registered.\n\t *\n\t * @param {String} id Unique plugin identifier\n\t */\n\thasPlugin( id ) {\n\n\t\treturn !!this.registeredPlugins[id];\n\n\t}\n\n\t/**\n\t * Returns the specific plugin instance, if a plugin\n\t * with the given ID has been registered.\n\t *\n\t * @param {String} id Unique plugin identifier\n\t */\n\tgetPlugin( id ) {\n\n\t\treturn this.registeredPlugins[id];\n\n\t}\n\n\tgetRegisteredPlugins() {\n\n\t\treturn this.registeredPlugins;\n\n\t}\n\n\tdestroy() {\n\n\t\tObject.values( this.registeredPlugins ).forEach( plugin => {\n\t\t\tif( typeof plugin.destroy === 'function' ) {\n\t\t\t\tplugin.destroy();\n\t\t\t}\n\t\t} );\n\n\t\tthis.registeredPlugins = {};\n\t\tthis.asyncDependencies = [];\n\n\t}\n\n}\n","import { SLIDES_SELECTOR } from '../utils/constants.js'\nimport { queryAll, createStyleSheet } from '../utils/util.js'\n\n/**\n * Setups up our presentation for printing/exporting to PDF.\n */\nexport default class Print {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t}\n\n\t/**\n\t * Configures the presentation for printing to a static\n\t * PDF.\n\t */\n\tasync setupPDF() {\n\n\t\tconst config = this.Reveal.getConfig();\n\t\tconst slides = queryAll( this.Reveal.getRevealElement(), SLIDES_SELECTOR )\n\n\t\t// Compute slide numbers now, before we start duplicating slides\n\t\tconst doingSlideNumbers = config.slideNumber && /all|print/i.test( config.showSlideNumber );\n\n\t\tconst slideSize = this.Reveal.getComputedSlideSize( window.innerWidth, window.innerHeight );\n\n\t\t// Dimensions of the PDF pages\n\t\tconst pageWidth = Math.floor( slideSize.width * ( 1 + config.margin ) ),\n\t\t\tpageHeight = Math.floor( slideSize.height * ( 1 + config.margin ) );\n\n\t\t// Dimensions of slides within the pages\n\t\tconst slideWidth = slideSize.width,\n\t\t\tslideHeight = slideSize.height;\n\n\t\tawait new Promise( requestAnimationFrame );\n\n\t\t// Let the browser know what page size we want to print\n\t\tcreateStyleSheet( '@page{size:'+ pageWidth +'px '+ pageHeight +'px; margin: 0px;}' );\n\n\t\t// Limit the size of certain elements to the dimensions of the slide\n\t\tcreateStyleSheet( '.reveal section>img, .reveal section>video, .reveal section>iframe{max-width: '+ slideWidth +'px; max-height:'+ slideHeight +'px}' );\n\n\t\tdocument.documentElement.classList.add( 'print-pdf' );\n\t\tdocument.body.style.width = pageWidth + 'px';\n\t\tdocument.body.style.height = pageHeight + 'px';\n\n\t\tconst viewportElement = document.querySelector( '.reveal-viewport' );\n\t\tlet presentationBackground;\n\t\tif( viewportElement ) {\n\t\t\tconst viewportStyles = window.getComputedStyle( viewportElement );\n\t\t\tif( viewportStyles && viewportStyles.background ) {\n\t\t\t\tpresentationBackground = viewportStyles.background;\n\t\t\t}\n\t\t}\n\n\t\t// Make sure stretch elements fit on slide\n\t\tawait new Promise( requestAnimationFrame );\n\t\tthis.Reveal.layoutSlideContents( slideWidth, slideHeight );\n\n\t\t// Batch scrollHeight access to prevent layout thrashing\n\t\tawait new Promise( requestAnimationFrame );\n\n\t\tconst slideScrollHeights = slides.map( slide => slide.scrollHeight );\n\n\t\tconst pages = [];\n\t\tconst pageContainer = slides[0].parentNode;\n\n\t\t// Slide and slide background layout\n\t\tslides.forEach( function( slide, index ) {\n\n\t\t\t// Vertical stacks are not centred since their section\n\t\t\t// children will be\n\t\t\tif( slide.classList.contains( 'stack' ) === false ) {\n\t\t\t\t// Center the slide inside of the page, giving the slide some margin\n\t\t\t\tlet left = ( pageWidth - slideWidth ) / 2;\n\t\t\t\tlet top = ( pageHeight - slideHeight ) / 2;\n\n\t\t\t\tconst contentHeight = slideScrollHeights[ index ];\n\t\t\t\tlet numberOfPages = Math.max( Math.ceil( contentHeight / pageHeight ), 1 );\n\n\t\t\t\t// Adhere to configured pages per slide limit\n\t\t\t\tnumberOfPages = Math.min( numberOfPages, config.pdfMaxPagesPerSlide );\n\n\t\t\t\t// Center slides vertically\n\t\t\t\tif( numberOfPages === 1 && config.center || slide.classList.contains( 'center' ) ) {\n\t\t\t\t\ttop = Math.max( ( pageHeight - contentHeight ) / 2, 0 );\n\t\t\t\t}\n\n\t\t\t\t// Wrap the slide in a page element and hide its overflow\n\t\t\t\t// so that no page ever flows onto another\n\t\t\t\tconst page = document.createElement( 'div' );\n\t\t\t\tpages.push( page );\n\n\t\t\t\tpage.className = 'pdf-page';\n\t\t\t\tpage.style.height = ( ( pageHeight + config.pdfPageHeightOffset ) * numberOfPages ) + 'px';\n\n\t\t\t\t// Copy the presentation-wide background to each individual\n\t\t\t\t// page when printing\n\t\t\t\tif( presentationBackground ) {\n\t\t\t\t\tpage.style.background = presentationBackground;\n\t\t\t\t}\n\n\t\t\t\tpage.appendChild( slide );\n\n\t\t\t\t// Position the slide inside of the page\n\t\t\t\tslide.style.left = left + 'px';\n\t\t\t\tslide.style.top = top + 'px';\n\t\t\t\tslide.style.width = slideWidth + 'px';\n\n\t\t\t\t// Re-run the slide layout so that r-fit-text is applied based on\n\t\t\t\t// the printed slide size\n\t\t\t\tthis.Reveal.slideContent.layout( slide )\n\n\t\t\t\tif( slide.slideBackgroundElement ) {\n\t\t\t\t\tpage.insertBefore( slide.slideBackgroundElement, slide );\n\t\t\t\t}\n\n\t\t\t\t// Inject notes if `showNotes` is enabled\n\t\t\t\tif( config.showNotes ) {\n\n\t\t\t\t\t// Are there notes for this slide?\n\t\t\t\t\tconst notes = this.Reveal.getSlideNotes( slide );\n\t\t\t\t\tif( notes ) {\n\n\t\t\t\t\t\tconst notesSpacing = 8;\n\t\t\t\t\t\tconst notesLayout = typeof config.showNotes === 'string' ? config.showNotes : 'inline';\n\t\t\t\t\t\tconst notesElement = document.createElement( 'div' );\n\t\t\t\t\t\tnotesElement.classList.add( 'speaker-notes' );\n\t\t\t\t\t\tnotesElement.classList.add( 'speaker-notes-pdf' );\n\t\t\t\t\t\tnotesElement.setAttribute( 'data-layout', notesLayout );\n\t\t\t\t\t\tnotesElement.innerHTML = notes;\n\n\t\t\t\t\t\tif( notesLayout === 'separate-page' ) {\n\t\t\t\t\t\t\tpages.push( notesElement );\n\t\t\t\t\t\t}\n\t\t\t\t\t\telse {\n\t\t\t\t\t\t\tnotesElement.style.left = notesSpacing + 'px';\n\t\t\t\t\t\t\tnotesElement.style.bottom = notesSpacing + 'px';\n\t\t\t\t\t\t\tnotesElement.style.width = ( pageWidth - notesSpacing*2 ) + 'px';\n\t\t\t\t\t\t\tpage.appendChild( notesElement );\n\t\t\t\t\t\t}\n\n\t\t\t\t\t}\n\n\t\t\t\t}\n\n\t\t\t\t// Inject slide numbers if `slideNumbers` are enabled\n\t\t\t\tif( doingSlideNumbers ) {\n\t\t\t\t\tconst slideNumber = index + 1;\n\t\t\t\t\tconst numberElement = document.createElement( 'div' );\n\t\t\t\t\tnumberElement.classList.add( 'slide-number' );\n\t\t\t\t\tnumberElement.classList.add( 'slide-number-pdf' );\n\t\t\t\t\tnumberElement.innerHTML = slideNumber;\n\t\t\t\t\tpage.appendChild( numberElement );\n\t\t\t\t}\n\n\t\t\t\t// Copy page and show fragments one after another\n\t\t\t\tif( config.pdfSeparateFragments ) {\n\n\t\t\t\t\t// Each fragment 'group' is an array containing one or more\n\t\t\t\t\t// fragments. Multiple fragments that appear at the same time\n\t\t\t\t\t// are part of the same group.\n\t\t\t\t\tconst fragmentGroups = this.Reveal.fragments.sort( page.querySelectorAll( '.fragment' ), true );\n\n\t\t\t\t\tlet previousFragmentStep;\n\n\t\t\t\t\tfragmentGroups.forEach( function( fragments ) {\n\n\t\t\t\t\t\t// Remove 'current-fragment' from the previous group\n\t\t\t\t\t\tif( previousFragmentStep ) {\n\t\t\t\t\t\t\tpreviousFragmentStep.forEach( function( fragment ) {\n\t\t\t\t\t\t\t\tfragment.classList.remove( 'current-fragment' );\n\t\t\t\t\t\t\t} );\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\t// Show the fragments for the current index\n\t\t\t\t\t\tfragments.forEach( function( fragment ) {\n\t\t\t\t\t\t\tfragment.classList.add( 'visible', 'current-fragment' );\n\t\t\t\t\t\t}, this );\n\n\t\t\t\t\t\t// Create a separate page for the current fragment state\n\t\t\t\t\t\tconst clonedPage = page.cloneNode( true );\n\t\t\t\t\t\tpages.push( clonedPage );\n\n\t\t\t\t\t\tpreviousFragmentStep = fragments;\n\n\t\t\t\t\t}, this );\n\n\t\t\t\t\t// Reset the first/original page so that all fragments are hidden\n\t\t\t\t\tfragmentGroups.forEach( function( fragments ) {\n\t\t\t\t\t\tfragments.forEach( function( fragment ) {\n\t\t\t\t\t\t\tfragment.classList.remove( 'visible', 'current-fragment' );\n\t\t\t\t\t\t} );\n\t\t\t\t\t} );\n\n\t\t\t\t}\n\t\t\t\t// Show all fragments\n\t\t\t\telse {\n\t\t\t\t\tqueryAll( page, '.fragment:not(.fade-out)' ).forEach( function( fragment ) {\n\t\t\t\t\t\tfragment.classList.add( 'visible' );\n\t\t\t\t\t} );\n\t\t\t\t}\n\n\t\t\t}\n\n\t\t}, this );\n\n\t\tawait new Promise( requestAnimationFrame );\n\n\t\tpages.forEach( page => pageContainer.appendChild( page ) );\n\n\t\t// Notify subscribers that the PDF layout is good to go\n\t\tthis.Reveal.dispatchEvent({ type: 'pdf-ready' });\n\n\t}\n\n\t/**\n\t * Checks if this instance is being used to print a PDF.\n\t */\n\tisPrintingPDF() {\n\n\t\treturn ( /print-pdf/gi ).test( window.location.search );\n\n\t}\n\n}\n","import { isAndroid } from '../utils/device.js'\nimport { matches } from '../utils/util.js'\n\nconst SWIPE_THRESHOLD = 40;\n\n/**\n * Controls all touch interactions and navigations for\n * a presentation.\n */\nexport default class Touch {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t\t// Holds information about the currently ongoing touch interaction\n\t\tthis.touchStartX = 0;\n\t\tthis.touchStartY = 0;\n\t\tthis.touchStartCount = 0;\n\t\tthis.touchCaptured = false;\n\n\t\tthis.onPointerDown = this.onPointerDown.bind( this );\n\t\tthis.onPointerMove = this.onPointerMove.bind( this );\n\t\tthis.onPointerUp = this.onPointerUp.bind( this );\n\t\tthis.onTouchStart = this.onTouchStart.bind( this );\n\t\tthis.onTouchMove = this.onTouchMove.bind( this );\n\t\tthis.onTouchEnd = this.onTouchEnd.bind( this );\n\n\t}\n\n\t/**\n\t *\n\t */\n\tbind() {\n\n\t\tlet revealElement = this.Reveal.getRevealElement();\n\n\t\tif( 'onpointerdown' in window ) {\n\t\t\t// Use W3C pointer events\n\t\t\trevealElement.addEventListener( 'pointerdown', this.onPointerDown, false );\n\t\t\trevealElement.addEventListener( 'pointermove', this.onPointerMove, false );\n\t\t\trevealElement.addEventListener( 'pointerup', this.onPointerUp, false );\n\t\t}\n\t\telse if( window.navigator.msPointerEnabled ) {\n\t\t\t// IE 10 uses prefixed version of pointer events\n\t\t\trevealElement.addEventListener( 'MSPointerDown', this.onPointerDown, false );\n\t\t\trevealElement.addEventListener( 'MSPointerMove', this.onPointerMove, false );\n\t\t\trevealElement.addEventListener( 'MSPointerUp', this.onPointerUp, false );\n\t\t}\n\t\telse {\n\t\t\t// Fall back to touch events\n\t\t\trevealElement.addEventListener( 'touchstart', this.onTouchStart, false );\n\t\t\trevealElement.addEventListener( 'touchmove', this.onTouchMove, false );\n\t\t\trevealElement.addEventListener( 'touchend', this.onTouchEnd, false );\n\t\t}\n\n\t}\n\n\t/**\n\t *\n\t */\n\tunbind() {\n\n\t\tlet revealElement = this.Reveal.getRevealElement();\n\n\t\trevealElement.removeEventListener( 'pointerdown', this.onPointerDown, false );\n\t\trevealElement.removeEventListener( 'pointermove', this.onPointerMove, false );\n\t\trevealElement.removeEventListener( 'pointerup', this.onPointerUp, false );\n\n\t\trevealElement.removeEventListener( 'MSPointerDown', this.onPointerDown, false );\n\t\trevealElement.removeEventListener( 'MSPointerMove', this.onPointerMove, false );\n\t\trevealElement.removeEventListener( 'MSPointerUp', this.onPointerUp, false );\n\n\t\trevealElement.removeEventListener( 'touchstart', this.onTouchStart, false );\n\t\trevealElement.removeEventListener( 'touchmove', this.onTouchMove, false );\n\t\trevealElement.removeEventListener( 'touchend', this.onTouchEnd, false );\n\n\t}\n\n\t/**\n\t * Checks if the target element prevents the triggering of\n\t * swipe navigation.\n\t */\n\tisSwipePrevented( target ) {\n\n\t\t// Prevent accidental swipes when scrubbing timelines\n\t\tif( matches( target, 'video, audio' ) ) return true;\n\n\t\twhile( target && typeof target.hasAttribute === 'function' ) {\n\t\t\tif( target.hasAttribute( 'data-prevent-swipe' ) ) return true;\n\t\t\ttarget = target.parentNode;\n\t\t}\n\n\t\treturn false;\n\n\t}\n\n\t/**\n\t * Handler for the 'touchstart' event, enables support for\n\t * swipe and pinch gestures.\n\t *\n\t * @param {object} event\n\t */\n\tonTouchStart( event ) {\n\n\t\tif( this.isSwipePrevented( event.target ) ) return true;\n\n\t\tthis.touchStartX = event.touches[0].clientX;\n\t\tthis.touchStartY = event.touches[0].clientY;\n\t\tthis.touchStartCount = event.touches.length;\n\n\t}\n\n\t/**\n\t * Handler for the 'touchmove' event.\n\t *\n\t * @param {object} event\n\t */\n\tonTouchMove( event ) {\n\n\t\tif( this.isSwipePrevented( event.target ) ) return true;\n\n\t\tlet config = this.Reveal.getConfig();\n\n\t\t// Each touch should only trigger one action\n\t\tif( !this.touchCaptured ) {\n\t\t\tthis.Reveal.onUserInput( event );\n\n\t\t\tlet currentX = event.touches[0].clientX;\n\t\t\tlet currentY = event.touches[0].clientY;\n\n\t\t\t// There was only one touch point, look for a swipe\n\t\t\tif( event.touches.length === 1 && this.touchStartCount !== 2 ) {\n\n\t\t\t\tlet availableRoutes = this.Reveal.availableRoutes({ includeFragments: true });\n\n\t\t\t\tlet deltaX = currentX - this.touchStartX,\n\t\t\t\t\tdeltaY = currentY - this.touchStartY;\n\n\t\t\t\tif( deltaX > SWIPE_THRESHOLD && Math.abs( deltaX ) > Math.abs( deltaY ) ) {\n\t\t\t\t\tthis.touchCaptured = true;\n\t\t\t\t\tif( config.navigationMode === 'linear' ) {\n\t\t\t\t\t\tif( config.rtl ) {\n\t\t\t\t\t\t\tthis.Reveal.next();\n\t\t\t\t\t\t}\n\t\t\t\t\t\telse {\n\t\t\t\t\t\t\tthis.Reveal.prev();\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tthis.Reveal.left();\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\telse if( deltaX < -SWIPE_THRESHOLD && Math.abs( deltaX ) > Math.abs( deltaY ) ) {\n\t\t\t\t\tthis.touchCaptured = true;\n\t\t\t\t\tif( config.navigationMode === 'linear' ) {\n\t\t\t\t\t\tif( config.rtl ) {\n\t\t\t\t\t\t\tthis.Reveal.prev();\n\t\t\t\t\t\t}\n\t\t\t\t\t\telse {\n\t\t\t\t\t\t\tthis.Reveal.next();\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tthis.Reveal.right();\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\telse if( deltaY > SWIPE_THRESHOLD && availableRoutes.up ) {\n\t\t\t\t\tthis.touchCaptured = true;\n\t\t\t\t\tif( config.navigationMode === 'linear' ) {\n\t\t\t\t\t\tthis.Reveal.prev();\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tthis.Reveal.up();\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\telse if( deltaY < -SWIPE_THRESHOLD && availableRoutes.down ) {\n\t\t\t\t\tthis.touchCaptured = true;\n\t\t\t\t\tif( config.navigationMode === 'linear' ) {\n\t\t\t\t\t\tthis.Reveal.next();\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tthis.Reveal.down();\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// If we're embedded, only block touch events if they have\n\t\t\t\t// triggered an action\n\t\t\t\tif( config.embedded ) {\n\t\t\t\t\tif( this.touchCaptured || this.Reveal.isVerticalSlide() ) {\n\t\t\t\t\t\tevent.preventDefault();\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\t// Not embedded? Block them all to avoid needless tossing\n\t\t\t\t// around of the viewport in iOS\n\t\t\t\telse {\n\t\t\t\t\tevent.preventDefault();\n\t\t\t\t}\n\n\t\t\t}\n\t\t}\n\t\t// There's a bug with swiping on some Android devices unless\n\t\t// the default action is always prevented\n\t\telse if( isAndroid ) {\n\t\t\tevent.preventDefault();\n\t\t}\n\n\t}\n\n\t/**\n\t * Handler for the 'touchend' event.\n\t *\n\t * @param {object} event\n\t */\n\tonTouchEnd( event ) {\n\n\t\tthis.touchCaptured = false;\n\n\t}\n\n\t/**\n\t * Convert pointer down to touch start.\n\t *\n\t * @param {object} event\n\t */\n\tonPointerDown( event ) {\n\n\t\tif( event.pointerType === event.MSPOINTER_TYPE_TOUCH || event.pointerType === \"touch\" ) {\n\t\t\tevent.touches = [{ clientX: event.clientX, clientY: event.clientY }];\n\t\t\tthis.onTouchStart( event );\n\t\t}\n\n\t}\n\n\t/**\n\t * Convert pointer move to touch move.\n\t *\n\t * @param {object} event\n\t */\n\tonPointerMove( event ) {\n\n\t\tif( event.pointerType === event.MSPOINTER_TYPE_TOUCH || event.pointerType === \"touch\" ) {\n\t\t\tevent.touches = [{ clientX: event.clientX, clientY: event.clientY }];\n\t\t\tthis.onTouchMove( event );\n\t\t}\n\n\t}\n\n\t/**\n\t * Convert pointer up to touch end.\n\t *\n\t * @param {object} event\n\t */\n\tonPointerUp( event ) {\n\n\t\tif( event.pointerType === event.MSPOINTER_TYPE_TOUCH || event.pointerType === \"touch\" ) {\n\t\t\tevent.touches = [{ clientX: event.clientX, clientY: event.clientY }];\n\t\t\tthis.onTouchEnd( event );\n\t\t}\n\n\t}\n\n}","import { closest } from '../utils/util.js'\n\n/**\n * Manages focus when a presentation is embedded. This\n * helps us only capture keyboard from the presentation\n * a user is currently interacting with in a page where\n * multiple presentations are embedded.\n */\n\nconst STATE_FOCUS = 'focus';\nconst STATE_BLUR = 'blur';\n\nexport default class Focus {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t\tthis.onRevealPointerDown = this.onRevealPointerDown.bind( this );\n\t\tthis.onDocumentPointerDown = this.onDocumentPointerDown.bind( this );\n\n\t}\n\n\t/**\n\t * Called when the reveal.js config is updated.\n\t */\n\tconfigure( config, oldConfig ) {\n\n\t\tif( config.embedded ) {\n\t\t\tthis.blur();\n\t\t}\n\t\telse {\n\t\t\tthis.focus();\n\t\t\tthis.unbind();\n\t\t}\n\n\t}\n\n\tbind() {\n\n\t\tif( this.Reveal.getConfig().embedded ) {\n\t\t\tthis.Reveal.getRevealElement().addEventListener( 'pointerdown', this.onRevealPointerDown, false );\n\t\t}\n\n\t}\n\n\tunbind() {\n\n\t\tthis.Reveal.getRevealElement().removeEventListener( 'pointerdown', this.onRevealPointerDown, false );\n\t\tdocument.removeEventListener( 'pointerdown', this.onDocumentPointerDown, false );\n\n\t}\n\n\tfocus() {\n\n\t\tif( this.state !== STATE_FOCUS ) {\n\t\t\tthis.Reveal.getRevealElement().classList.add( 'focused' );\n\t\t\tdocument.addEventListener( 'pointerdown', this.onDocumentPointerDown, false );\n\t\t}\n\n\t\tthis.state = STATE_FOCUS;\n\n\t}\n\n\tblur() {\n\n\t\tif( this.state !== STATE_BLUR ) {\n\t\t\tthis.Reveal.getRevealElement().classList.remove( 'focused' );\n\t\t\tdocument.removeEventListener( 'pointerdown', this.onDocumentPointerDown, false );\n\t\t}\n\n\t\tthis.state = STATE_BLUR;\n\n\t}\n\n\tisFocused() {\n\n\t\treturn this.state === STATE_FOCUS;\n\n\t}\n\n\tdestroy() {\n\n\t\tthis.Reveal.getRevealElement().classList.remove( 'focused' );\n\n\t}\n\n\tonRevealPointerDown( event ) {\n\n\t\tthis.focus();\n\n\t}\n\n\tonDocumentPointerDown( event ) {\n\n\t\tlet revealElement = closest( event.target, '.reveal' );\n\t\tif( !revealElement || revealElement !== this.Reveal.getRevealElement() ) {\n\t\t\tthis.blur();\n\t\t}\n\n\t}\n\n}","/**\n * Handles the showing and \n */\nexport default class Notes {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t}\n\n\trender() {\n\n\t\tthis.element = document.createElement( 'div' );\n\t\tthis.element.className = 'speaker-notes';\n\t\tthis.element.setAttribute( 'data-prevent-swipe', '' );\n\t\tthis.element.setAttribute( 'tabindex', '0' );\n\t\tthis.Reveal.getRevealElement().appendChild( this.element );\n\n\t}\n\n\t/**\n\t * Called when the reveal.js config is updated.\n\t */\n\tconfigure( config, oldConfig ) {\n\n\t\tif( config.showNotes ) {\n\t\t\tthis.element.setAttribute( 'data-layout', typeof config.showNotes === 'string' ? config.showNotes : 'inline' );\n\t\t}\n\n\t}\n\n\t/**\n\t * Pick up notes from the current slide and display them\n\t * to the viewer.\n\t *\n\t * @see {@link config.showNotes}\n\t */\n\tupdate() {\n\n\t\tif( this.Reveal.getConfig().showNotes && this.element && this.Reveal.getCurrentSlide() && !this.Reveal.print.isPrintingPDF() ) {\n\n\t\t\tthis.element.innerHTML = this.getSlideNotes() || 'No notes on this slide.';\n\n\t\t}\n\n\t}\n\n\t/**\n\t * Updates the visibility of the speaker notes sidebar that\n\t * is used to share annotated slides. The notes sidebar is\n\t * only visible if showNotes is true and there are notes on\n\t * one or more slides in the deck.\n\t */\n\tupdateVisibility() {\n\n\t\tif( this.Reveal.getConfig().showNotes && this.hasNotes() && !this.Reveal.print.isPrintingPDF() ) {\n\t\t\tthis.Reveal.getRevealElement().classList.add( 'show-notes' );\n\t\t}\n\t\telse {\n\t\t\tthis.Reveal.getRevealElement().classList.remove( 'show-notes' );\n\t\t}\n\n\t}\n\n\t/**\n\t * Checks if there are speaker notes for ANY slide in the\n\t * presentation.\n\t */\n\thasNotes() {\n\n\t\treturn this.Reveal.getSlidesElement().querySelectorAll( '[data-notes], aside.notes' ).length > 0;\n\n\t}\n\n\t/**\n\t * Checks if this presentation is running inside of the\n\t * speaker notes window.\n\t *\n\t * @return {boolean}\n\t */\n\tisSpeakerNotesWindow() {\n\n\t\treturn !!window.location.search.match( /receiver/gi );\n\n\t}\n\n\t/**\n\t * Retrieves the speaker notes from a slide. Notes can be\n\t * defined in two ways:\n\t * 1. As a data-notes attribute on the slide
    \n\t * 2. As an

    Predictions (with interaction included), plotted

    -
    plot_cap(model = prepost.1, condition = c("before", "drug"))
    +
    plot_predictions(model = prepost.1, condition = c("before", "drug"))
    -

    Lines almost parallel, but not quite.

    +

    Lines almost parallel, but not quite.