From 76e16242e61ef3340a96301a7314cd55fb1db24a Mon Sep 17 00:00:00 2001 From: Elizabeth Humphries Date: Thu, 26 Sep 2024 12:29:55 -0400 Subject: [PATCH 01/26] remove tb as an object name --- modules/Subsetting_Data_in_R/Subsetting_Data_in_R.Rmd | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/modules/Subsetting_Data_in_R/Subsetting_Data_in_R.Rmd b/modules/Subsetting_Data_in_R/Subsetting_Data_in_R.Rmd index 95461c60..29cfed73 100644 --- a/modules/Subsetting_Data_in_R/Subsetting_Data_in_R.Rmd +++ b/modules/Subsetting_Data_in_R/Subsetting_Data_in_R.Rmd @@ -695,7 +695,7 @@ head(er_30_Den) ## Using the `pipe` (comes with `dplyr`): -The pipe `%>%` makes this much more readable. It reads left side "pipes" into right side. RStudio `CMD/Ctrl + Shift + M` shortcut. Pipe `tb` into `filter`, then pipe that into `select`: +The pipe `%>%` makes this much more readable. It reads left side "pipes" into right side. RStudio `CMD/Ctrl + Shift + M` shortcut. Pipe `er_30` into `filter`, then pipe that into `select`: ```{r} @@ -839,9 +839,9 @@ For example, let say we just wanted `year` to be before `rate``. head(er_30, 1) -tb_carb <- relocate(er_30, year, .before = rate) +er_year_fix <- relocate(er_30, year, .before = rate) -head(tb_carb, 1) +head(er_year_fix, 1) ``` From e3d149400d7355d8fe7f8aff9a2ee7f78ae5508f Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Fri, 27 Sep 2024 15:13:30 +0000 Subject: [PATCH 02/26] Render pdfs --- modules/Intro/Intro.pdf | Bin 7930308 -> 7930429 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/modules/Intro/Intro.pdf b/modules/Intro/Intro.pdf index c503b96af2a8e83ec1974106614e80af08f1d0b5..c7e0bd3f4e3daacae8217edf5decc5443cae8781 100644 GIT binary patch delta 2145 zcmZWq2~ZVR7BzfG0-A{dS;80npkE}$|6gyvyAg0fKpapBvM2(g$TDmrqPFW7$(5Uk z1Gu#jP+W*{MI=Iqfg);*WRMWINZf*8WK1z}5GBYq{VSb;9O z?W)=j+f@ZI@9`8zF|;s=5O?MEu+ax+#LtY&TBeye(~~ANOGNrlNj3t$2514O0<;9Q z0(>3t4Zt@6tpRNShXalPd<*bzfNuki1bhe37SIlG6reqz1E3?I6X3gm&VXvb(SR<1 zV*tkjY5-jU-2le{jtA5NP5^WVoCxRv_#WUS!1nPSPa4O(5!0CW90DS>x0{Q{^0|o%j0t^J44fuD!Ai!Y2Ie>Ek=K+QQ zegGH>I3F+!FdQ%fFcNS9;6lJCz(s)3fH8ovfQtd+0G9wR1&jw=2ABYt2$%$z4EPVg z6u?x#G{AJg48VT^E(gp6TmhH`xDs#`;Oes|{o-#W2s&PrzY5BlE1wUL=Gk*Pfz@!L zK}T_-$ZJ@U*0F;8TX%#`UR;x9Bgf8?((M@?qpXS=j_^7Y5Hu`Dq)>o5e)#0>{-E;-rgVO>7 z?Ha->?RCxhxAs(Q@h!~1K295Tc#+SghMp3aF{;v6}jrNYM`>gZ} zV=aPHN}EpZ-nwz^?C89cn?`#N+g$%cq=U0lkz1cdlI`yP#4AR(vwIp&=P&$qVY9<% zo6K74>7{#bIl8?6vn6|A*N58<#1+iUU|Y7=&(ph?`5v)4M`GXHOP+-VX7(Pt{HRNP zJjgv;owMp2Xlmc^aB%UX?spo?^*J9+-Zykwv!MjM)K*6|&Wn=oui#E9rMT$D8YOI>I0iIDjRj184-wO2PBzMkF7mEF6Z zANja6stEIAAA7T1-W#-j{RJr@{<&uieKkHQwVD&d$3^@%byd8J+Z#>`pHR-(mdT7u z>CkV^(+7(()7IU+CVlEp9L0=%85EN+b?LrJ&t)SLN*r%}qSgj4|9a=x;;2UtpH@mS zUwbUw_}$wjb1S=y%jb^l=-|@Ij*hK3slnun#^&&~No$**r+oX?0NE7Ramlmp&V8@$ zl5~fM2R$8iJFA{OxKm}>>3OO)x2h%eSo96wPjWiD52|e=){-6Dc2>BD7VUUGEp<*s zXLw9@arlLab3!(@W_-WBD=qMfE778u~!OK42WFaD+!i7p^5yoR}xjUL6G$?6u?miIpu})kxEeN zdtOLEmI4tdIiOFeV>n)x`%J+sk^B0TRs@cf$Mq`=#Sl5SUtt)9DK`O*7vvvJKp?a{ z)Gx(aiG&E8ymY{%BBH!?K(XW$=C+C9MNw7_Dm_ueK+97H6-t>wP8>A##E9~tK}9W4 zocwf9%Cb<}E*z5VBg`M1@vomrD4sPd775L9W+RG(p(ulyp>m7U z=9`0JSi_$oe`QcCBfdO{FE3K8NX&z218*=lq%-hkj~xeE zNuOR@$>zy>6AUN^c{8pvcjlh?eZO?lk#mAeMC|mP0=hR(f^}tLO?IT(SRC2 zE#MfyM*znHJ__g!r~~u?90%wN=m+QzI392U;6%X300RI60Urkp0-OXm8E^{VRKQ@s zX@DVsp@4e8>3}l;p8$LkFbr@eU^rj|U?gA^U^L(?z!*RSU@Ra7NC0U-29O2h0C_+G zPz00!;{ay^#sfYDm;g8j@M*wAKqKH>z$CzVfb#*90aE}?fT@5B0Mh`?fC~W^0j2{k z23!KT6fgsD8Q?R3nSfb<&jKz7%m&N>TmhI1m*O+36dl!i_J2p988uA`~(9n z>eZGQ7~($Yej|e@H)*R@S=ngQDS0V!(L~-rGwPY1@T`IG6h*PhN~3I0#yK*Cz=Go9 z0;?sjw5+7uvObKab0WidN{KMZyj>t%LV~{3R$|FttCMT4Ka*WFm5dwdh_r2rno$>? 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How many data points are from days where the maximum ground temperature got above 0 degrees Celsius? What percent/proportion do these represent? Use: +13\. How many data points are from days where the maximum ground temperature got above or equal to 0 degrees Celsius? What percent/proportion do these represent? Use: - `filter()` and `nrow()` - `group_by()` and `summarize()` or From 6449205bf348d2ea6fdd29027a3b3774d07b8439 Mon Sep 17 00:00:00 2001 From: carriewright11 Date: Sun, 29 Sep 2024 21:10:17 -0400 Subject: [PATCH 05/26] updating rstudio lecture --- modules/RStudio/RStudio.Rmd | 56 ++++++++++++++++++++----------------- 1 file changed, 30 insertions(+), 26 deletions(-) diff --git a/modules/RStudio/RStudio.Rmd b/modules/RStudio/RStudio.Rmd index 688d66dd..a314bdd0 100644 --- a/modules/RStudio/RStudio.Rmd +++ b/modules/RStudio/RStudio.Rmd @@ -316,19 +316,6 @@ You can run all chunks above a specific chunk using this button: knitr::include_graphics("images/previous_chunks.png") ``` -## Chunk settings - -```{r echo=FALSE, fig.align='center', out.width="80%"} -knitr::include_graphics("images/chunk_settings.png") -``` - -## Chunk settings - -You can specify if a chunk will be seen in the report or not. - -```{r echo=FALSE, fig.align='center', out.width="80%"} -knitr::include_graphics("images/chunk_settings2.png") -``` ## Errors @@ -339,19 +326,6 @@ Note that sometimes the error occurs earlier than RStudio thinks. knitr::include_graphics("images/errors.png") ``` -## Rainbow Parentheses - -Tools --> Global Options --> Code --> Display --> Use rainbow parentheses - -Press enter to save this setting and get out of this menu. - -
-
- -```{r echo=FALSE, fig.align='center', out.width="80%"} -knitr::include_graphics("https://posit.co/wp-content/themes/Posit/public/markdown-blogs/rstudio-1-4-preview-rainbow-parentheses/colorfulCode.png") -``` - ## Useful R Studio Shortcuts @@ -442,7 +416,37 @@ Image by Global Options --> Code --> Display --> Use rainbow parentheses + +This can help you see your code more easily. + +Press enter to save this setting and get out of this menu. + +
+
+ +```{r echo=FALSE, fig.align='center', out.width="80%"} +knitr::include_graphics("https://posit.co/wp-content/themes/Posit/public/markdown-blogs/rstudio-1-4-preview-rainbow-parentheses/colorfulCode.png") +``` + + From 7e129ef52a67e79addbd744bb6a38ef0fcc897d0 Mon Sep 17 00:00:00 2001 From: carriewright11 Date: Sun, 29 Sep 2024 21:20:43 -0400 Subject: [PATCH 06/26] making directions more clear --- modules/RStudio/lab/RStudio_Lab.Rmd | 7 +------ modules/RStudio/lab/RStudio_Lab_Key.Rmd | 28 ++++++------------------- 2 files changed, 7 insertions(+), 28 deletions(-) diff --git a/modules/RStudio/lab/RStudio_Lab.Rmd b/modules/RStudio/lab/RStudio_Lab.Rmd index 295dce2f..9604d9f3 100644 --- a/modules/RStudio/lab/RStudio_Lab.Rmd +++ b/modules/RStudio/lab/RStudio_Lab.Rmd @@ -45,9 +45,7 @@ ggplot(aes(x = year, y = rate, colour = county, group = county), data = er_3) + scale_colour_manual(values = palette) ``` -```{r out.width = "100%", label = '0response'} -``` # Exercise @@ -70,10 +68,7 @@ x <- c(1, 2, 3) # Practice on Your Own! -### P.1 - -Update the options of the R chunk you just made (with the lowercase x in it) so that the output option is `show output only`. How does the chunk look now? -### P.2 +### P.1 Create another R Markdown Document from RStudio dropdowns: File → New File → R Markdown. diff --git a/modules/RStudio/lab/RStudio_Lab_Key.Rmd b/modules/RStudio/lab/RStudio_Lab_Key.Rmd index 90b3cb6a..9bf12305 100644 --- a/modules/RStudio/lab/RStudio_Lab_Key.Rmd +++ b/modules/RStudio/lab/RStudio_Lab_Key.Rmd @@ -28,24 +28,8 @@ When you click the **Knit** button (at the top of RStudio), a document will be g Here is code that will make a plot of some data.You can embed an R code chunk like this: Try pressing the green play button to see what happens. Make sure you have run the previous code chunk first by pressing the green play button in that chunk. -```{r plot, out.width = "100%"} -# keep only some counties -er_3 <- er_2 %>% - filter(county %in% c("Arapahoe", "Denver", "Jefferson", "Larimer")) - -palette <- c( - Arapahoe = "blue", - Denver = "darkgreen", - Jefferson = "orange", - Larimer = "purple" -) - -ggplot(aes(x = year, y = rate, colour = county, group = county), data = er_3) + - geom_line() + - scale_colour_manual(values = palette) -``` -```{r out.width = "100%", label = '0response'} +```{r out.width = "100%"} # keep only some counties er_3 <- er_2 %>% filter(county %in% c("Arapahoe", "Denver", "Jefferson", "Larimer")) @@ -73,7 +57,7 @@ Go through the code for the plot above and change the colors in `palette` to som ### 1.2 -Add a new R chunk after ``. You can use the insert chunk button or copy paste an existing code chunk. Include a lowercase x inside the chunk as the code. Make sure you press the knit button after this to see what the new chunk looks like. +Add a new R chunk after ``. You can use the insert chunk button or copy paste an existing code chunk. Include a lowercase x inside the chunk as the code. Make sure you press the knit button after this to see what the new chunk looks like. ```{r} x <- c(1, 2, 3) @@ -81,12 +65,12 @@ x <- c(1, 2, 3) +```{r} +x +``` + # Practice on Your Own! ### P.1 -Update the options of the R chunk you just made (with the lowercase x in it) so that the output option is `show output only`. How does the chunk look now? - -### P.2 - Create another R Markdown Document from RStudio dropdowns: File → New File → R Markdown. From 4fbbfaaa770f707f4f20f095c960687af3324a81 Mon Sep 17 00:00:00 2001 From: carriewright11 Date: Sun, 29 Sep 2024 21:28:26 -0400 Subject: [PATCH 07/26] adding stuff like sisbid lab --- modules/RStudio/lab/RStudio_Lab_Key.Rmd | 27 +++++++++++++++---------- 1 file changed, 16 insertions(+), 11 deletions(-) diff --git a/modules/RStudio/lab/RStudio_Lab_Key.Rmd b/modules/RStudio/lab/RStudio_Lab_Key.Rmd index 9bf12305..c6cba0b4 100644 --- a/modules/RStudio/lab/RStudio_Lab_Key.Rmd +++ b/modules/RStudio/lab/RStudio_Lab_Key.Rmd @@ -9,10 +9,14 @@ editor_options: Please read through everything and then try the exercises. This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see . The way you can create a file like this in RStudio is: File → New File → R Markdown and then using the default or using a template. +1.Try compiling this document using the “Knit HTML” button. What files are produced? + ### The Code The gray area below is a code chunk that will set up our packages and data. You can also run the code within the editor area by pressing the green play button. Don't worry right now about what the code is doing, we will cover this later. We just want you to get used to RStudio and RMarkdowns. This dataset is one we'll be working with quite a lot in the lectures. It contains county-level data about ER visits for heat-related illnesses in Colorado for the years 2011-2022. +2. Press the green play button. What happens? + ```{r setup, message=FALSE} library(tidyverse) @@ -28,6 +32,9 @@ When you click the **Knit** button (at the top of RStudio), a document will be g Here is code that will make a plot of some data.You can embed an R code chunk like this: Try pressing the green play button to see what happens. Make sure you have run the previous code chunk first by pressing the green play button in that chunk. +3. Try the play all previous button that is to the left of the play button on the following chunk. What happens? + +4. Then press the play button. What happens? ```{r out.width = "100%"} # keep only some counties @@ -46,18 +53,9 @@ ggplot(aes(x = year, y = rate, colour = county, group = county), data = er_3) + scale_colour_manual(values = palette) ``` +### Adding new chunks -# Exercise - -Here are a few changes that will show you how to change small things in `R` code and the output it makes. After each change, hit the **Knit** button again. - -### 1.1 - -Go through the code for the plot above and change the colors in `palette` to something other than what they originally were. See http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf for a large list of colors. For example, you could replace blue with red. - -### 1.2 - -Add a new R chunk after ``. You can use the insert chunk button or copy paste an existing code chunk. Include a lowercase x inside the chunk as the code. Make sure you press the knit button after this to see what the new chunk looks like. +5. Add a new R chunk after ``. You can use the insert chunk button or copy paste an existing code chunk. Include a lowercase x inside the chunk as the code. Make sure you press the knit button after this to see what the new chunk looks like. ```{r} x <- c(1, 2, 3) @@ -69,8 +67,15 @@ x <- c(1, 2, 3) x ``` + # Practice on Your Own! +Here are a few changes that will show you how to change small things in `R` code and the output it makes. After each change, hit the **Knit** button again. + ### P.1 +Go through the code for the plot above and change the colors in `palette` to something other than what they originally were. See http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf for a large list of colors. For example, you could replace blue with red. + +### P.2 + Create another R Markdown Document from RStudio dropdowns: File → New File → R Markdown. From 715ea77ea4b8fa7769edc940c68baf8abe211ef0 Mon Sep 17 00:00:00 2001 From: carriewright11 Date: Sun, 29 Sep 2024 21:42:09 -0400 Subject: [PATCH 08/26] removing automatic lab render from key for rstudio lab --- scripts/blank_labs_from_keys.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/blank_labs_from_keys.sh b/scripts/blank_labs_from_keys.sh index 660652d4..80b0c65d 100644 --- a/scripts/blank_labs_from_keys.sh +++ b/scripts/blank_labs_from_keys.sh @@ -1,6 +1,6 @@ #!/usr/bin/env bash -find modules/ -type f -name "*_Key.Rmd" -print0 | while IFS= read -r -d '' file; do +find modules/ -type f ! -name "*RStudio*" -name "*_Key.Rmd" -print0 | while IFS= read -r -d '' file; do # Remove Rmarkdown chunk content # !! Note that the chunks must be named, with the name ending with "response" # to be cleared. This prevents unwanted erasure of library loading and knitr From 62956600e60c4480d42a9d60b16fb97577ccf1c0 Mon Sep 17 00:00:00 2001 From: carriewright11 Date: Sun, 29 Sep 2024 21:48:40 -0400 Subject: [PATCH 09/26] adding the labs for RStudio to be more like sisbid --- modules/RStudio/lab/RStudio_Lab.Rmd | 38 +++++++++++++++---------- modules/RStudio/lab/RStudio_Lab_Key.Rmd | 12 ++++++-- 2 files changed, 32 insertions(+), 18 deletions(-) diff --git a/modules/RStudio/lab/RStudio_Lab.Rmd b/modules/RStudio/lab/RStudio_Lab.Rmd index 9604d9f3..7ee49ada 100644 --- a/modules/RStudio/lab/RStudio_Lab.Rmd +++ b/modules/RStudio/lab/RStudio_Lab.Rmd @@ -9,10 +9,14 @@ editor_options: Please read through everything and then try the exercises. This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see . The way you can create a file like this in RStudio is: File → New File → R Markdown and then using the default or using a template. +1. Try compiling this document using the “Knit HTML” button. What files are produced? + ### The Code The gray area below is a code chunk that will set up our packages and data. You can also run the code within the editor area by pressing the green play button. Don't worry right now about what the code is doing, we will cover this later. We just want you to get used to RStudio and RMarkdowns. This dataset is one we'll be working with quite a lot in the lectures. It contains county-level data about ER visits for heat-related illnesses in Colorado for the years 2011-2022. +2. Press the green play button. What happens? + ```{r setup, message=FALSE} library(tidyverse) @@ -28,7 +32,11 @@ When you click the **Knit** button (at the top of RStudio), a document will be g Here is code that will make a plot of some data.You can embed an R code chunk like this: Try pressing the green play button to see what happens. Make sure you have run the previous code chunk first by pressing the green play button in that chunk. -```{r plot, out.width = "100%"} +3. Try the "run all chunks above" button that is to the left of the play button on the following chunk. What happens? + +4. Then press the play button on this same chunk. What happens? + +```{r out.width = "100%"} # keep only some counties er_3 <- er_2 %>% filter(county %in% c("Arapahoe", "Denver", "Jefferson", "Larimer")) @@ -37,7 +45,7 @@ palette <- c( Arapahoe = "blue", Denver = "darkgreen", Jefferson = "orange", - Larimer = "purple" + Larimer = "salmon" ) ggplot(aes(x = year, y = rate, colour = county, group = county), data = er_3) + @@ -45,20 +53,9 @@ ggplot(aes(x = year, y = rate, colour = county, group = county), data = er_3) + scale_colour_manual(values = palette) ``` +### Adding new chunks - - -# Exercise - -Here are a few changes that will show you how to change small things in `R` code and the output it makes. After each change, hit the **Knit** button again. - -### 1.1 - -Go through the code for the plot above and change the colors in `palette` to something other than what they originally were. See http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf for a large list of colors. For example, you could replace blue with red. - -### 1.2 - -Add a new R chunk after ``. You can use the insert chunk button or copy paste an existing code chunk. Include a lowercase x inside the chunk as the code. Make sure you press the knit button after this to see what the new chunk looks like. +5. Add a new R chunk after ``. You can use the insert chunk button or copy paste an existing code chunk. Include a lowercase x inside the chunk as the code. Make sure you press the knit button after this to see what the new chunk looks like. ```{r} x <- c(1, 2, 3) @@ -66,9 +63,20 @@ x <- c(1, 2, 3) + + # Practice on Your Own! +Here are a few changes that will show you how to change small things in `R` code and the output it makes. After each change, hit the **Knit** button again. ### P.1 +Go through the code for the plot above and change the colors in `palette` to something other than what they originally were. See http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf for a large list of colors. For example, you could replace blue with red. + +### P.2 + Create another R Markdown Document from RStudio dropdowns: File → New File → R Markdown. + +### P.3 + +Add a new header with two hash symbols `##` at the start of a line with some text. Knit the report to see how it looks. diff --git a/modules/RStudio/lab/RStudio_Lab_Key.Rmd b/modules/RStudio/lab/RStudio_Lab_Key.Rmd index c6cba0b4..79ee8f3d 100644 --- a/modules/RStudio/lab/RStudio_Lab_Key.Rmd +++ b/modules/RStudio/lab/RStudio_Lab_Key.Rmd @@ -9,7 +9,7 @@ editor_options: Please read through everything and then try the exercises. This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see . The way you can create a file like this in RStudio is: File → New File → R Markdown and then using the default or using a template. -1.Try compiling this document using the “Knit HTML” button. What files are produced? +1. Try compiling this document using the “Knit HTML” button. What files are produced? ### The Code @@ -32,9 +32,9 @@ When you click the **Knit** button (at the top of RStudio), a document will be g Here is code that will make a plot of some data.You can embed an R code chunk like this: Try pressing the green play button to see what happens. Make sure you have run the previous code chunk first by pressing the green play button in that chunk. -3. Try the play all previous button that is to the left of the play button on the following chunk. What happens? +3. Try the "run all chunks above" button that is to the left of the play button on the following chunk. What happens? -4. Then press the play button. What happens? +4. Then press the play button on this same chunk. What happens? ```{r out.width = "100%"} # keep only some counties @@ -79,3 +79,9 @@ Go through the code for the plot above and change the colors in `palette` to som ### P.2 Create another R Markdown Document from RStudio dropdowns: File → New File → R Markdown. + +### P.3 + +Add a new header with two hash symbols `##` at the start of a line with some text. Knit the report to see how it looks. + +## New Header From 19988b579392be80faf6b6926ae00d1150762e95 Mon Sep 17 00:00:00 2001 From: carriewright11 Date: Sun, 29 Sep 2024 21:55:24 -0400 Subject: [PATCH 10/26] making it so that the lab makes sense after the basic R lab --- modules/RStudio/lab/RStudio_Lab.Rmd | 4 ++-- modules/RStudio/lab/RStudio_Lab_Key.Rmd | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/modules/RStudio/lab/RStudio_Lab.Rmd b/modules/RStudio/lab/RStudio_Lab.Rmd index 7ee49ada..3656ebdf 100644 --- a/modules/RStudio/lab/RStudio_Lab.Rmd +++ b/modules/RStudio/lab/RStudio_Lab.Rmd @@ -7,7 +7,7 @@ editor_options: # R Markdown -Please read through everything and then try the exercises. This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see . The way you can create a file like this in RStudio is: File → New File → R Markdown and then using the default or using a template. +The file you are reading is called an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see . The way you can create a file like this in RStudio is: File → New File → R Markdown and then using the default or using a template. 1. Try compiling this document using the “Knit HTML” button. What files are produced? @@ -15,7 +15,7 @@ Please read through everything and then try the exercises. This is an R Markdown The gray area below is a code chunk that will set up our packages and data. You can also run the code within the editor area by pressing the green play button. Don't worry right now about what the code is doing, we will cover this later. We just want you to get used to RStudio and RMarkdowns. This dataset is one we'll be working with quite a lot in the lectures. It contains county-level data about ER visits for heat-related illnesses in Colorado for the years 2011-2022. -2. Press the green play button. What happens? +2. Press the green play button. What happens (check the enviornment too!)? ```{r setup, message=FALSE} library(tidyverse) diff --git a/modules/RStudio/lab/RStudio_Lab_Key.Rmd b/modules/RStudio/lab/RStudio_Lab_Key.Rmd index 79ee8f3d..754ab895 100644 --- a/modules/RStudio/lab/RStudio_Lab_Key.Rmd +++ b/modules/RStudio/lab/RStudio_Lab_Key.Rmd @@ -7,7 +7,7 @@ editor_options: # R Markdown -Please read through everything and then try the exercises. This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see . The way you can create a file like this in RStudio is: File → New File → R Markdown and then using the default or using a template. +The file you are reading is called an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see . The way you can create a file like this in RStudio is: File → New File → R Markdown and then using the default or using a template. 1. Try compiling this document using the “Knit HTML” button. What files are produced? @@ -15,7 +15,7 @@ Please read through everything and then try the exercises. This is an R Markdown The gray area below is a code chunk that will set up our packages and data. You can also run the code within the editor area by pressing the green play button. Don't worry right now about what the code is doing, we will cover this later. We just want you to get used to RStudio and RMarkdowns. This dataset is one we'll be working with quite a lot in the lectures. It contains county-level data about ER visits for heat-related illnesses in Colorado for the years 2011-2022. -2. Press the green play button. What happens? +2. Press the green play button. What happens (check the enviornment too!)? ```{r setup, message=FALSE} library(tidyverse) From 2a37db1ed43e7d09ef159d0e90aaafe6ab8608e9 Mon Sep 17 00:00:00 2001 From: carriewright11 Date: Sun, 29 Sep 2024 21:59:22 -0400 Subject: [PATCH 11/26] fixing spelling --- modules/RStudio/lab/RStudio_Lab.Rmd | 2 +- modules/RStudio/lab/RStudio_Lab_Key.Rmd | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/RStudio/lab/RStudio_Lab.Rmd b/modules/RStudio/lab/RStudio_Lab.Rmd index 3656ebdf..8b31eda4 100644 --- a/modules/RStudio/lab/RStudio_Lab.Rmd +++ b/modules/RStudio/lab/RStudio_Lab.Rmd @@ -15,7 +15,7 @@ The file you are reading is called an R Markdown document. Markdown is a simple The gray area below is a code chunk that will set up our packages and data. You can also run the code within the editor area by pressing the green play button. Don't worry right now about what the code is doing, we will cover this later. We just want you to get used to RStudio and RMarkdowns. This dataset is one we'll be working with quite a lot in the lectures. It contains county-level data about ER visits for heat-related illnesses in Colorado for the years 2011-2022. -2. Press the green play button. What happens (check the enviornment too!)? +2. Press the green play button. What happens (check the environment too!)? ```{r setup, message=FALSE} library(tidyverse) diff --git a/modules/RStudio/lab/RStudio_Lab_Key.Rmd b/modules/RStudio/lab/RStudio_Lab_Key.Rmd index 754ab895..55fdd7cf 100644 --- a/modules/RStudio/lab/RStudio_Lab_Key.Rmd +++ b/modules/RStudio/lab/RStudio_Lab_Key.Rmd @@ -15,7 +15,7 @@ The file you are reading is called an R Markdown document. Markdown is a simple The gray area below is a code chunk that will set up our packages and data. You can also run the code within the editor area by pressing the green play button. Don't worry right now about what the code is doing, we will cover this later. We just want you to get used to RStudio and RMarkdowns. This dataset is one we'll be working with quite a lot in the lectures. It contains county-level data about ER visits for heat-related illnesses in Colorado for the years 2011-2022. -2. Press the green play button. What happens (check the enviornment too!)? +2. Press the green play button. What happens (check the environment too!)? ```{r setup, message=FALSE} library(tidyverse) From be9616fcd2a638c7b0f08f98eebb7ee1a5778222 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Mon, 30 Sep 2024 02:05:46 +0000 Subject: [PATCH 12/26] Render site --- help.html | 8 +- index.html | 2 +- modules/HW/homework2.Rmd | 2 +- modules/HW/homework2.html | 432 ++++++++------------------------------ 4 files changed, 99 insertions(+), 345 deletions(-) diff --git a/help.html b/help.html index 78200b5e..dbc9b4e0 100644 --- a/help.html +++ b/help.html @@ -350,14 +350,14 @@

Why are my changes not taking effect? It’s making my results look

Here we are creating a new object from an existing one:

new_rivers <- sample(rivers, 5)
 new_rivers
-
## [1] 202 215 260 375 300
+
## [1] 210 540 390 300 605

Using just this will only print the result and not actually change new_rivers:

new_rivers + 1
-
## [1] 203 216 261 376 301
+
## [1] 211 541 391 301 606

If we want to modify new_rivers and save that modified version, then we need to reassign new_rivers like so:

new_rivers <- new_rivers + 1
 new_rivers
-
## [1] 203 216 261 376 301
+
## [1] 211 541 391 301 606

If we forget to reassign this can cause subsequent steps to not work as expected because we will not be working with the data that has been modified.


@@ -406,7 +406,7 @@

Error: object ‘X’ not found

Make sure you run something like this, with the <- operator:

rivers2 <- new_rivers + 1
 rivers2
-
## [1] 204 217 262 377 302
+
## [1] 212 542 392 302 607

diff --git a/index.html b/index.html index 1c5838b1..780d4507 100644 --- a/index.html +++ b/index.html @@ -348,7 +348,7 @@

Testimonials from our other courses:

Find an Error!?


Feel free to submit typos/errors/etc via the GitHub repository associated with the class: https://github.com/fhdsl/DaSEH

-

This page was last updated on 2024-09-27.

+

This page was last updated on 2024-09-30.

Creative Commons License

diff --git a/modules/HW/homework2.Rmd b/modules/HW/homework2.Rmd index d2b4b9f8..7c23e5a7 100644 --- a/modules/HW/homework2.Rmd +++ b/modules/HW/homework2.Rmd @@ -118,7 +118,7 @@ We will be working with a dataset from the "Kaggle" website, which hosts competi ``` -13\. How many data points are from days where the maximum ground temperature got above or equal to 0 degrees Celsius? What percent/proportion do these represent? Use: +13\. How many data points are from days where the maximum ground temperature got above 0 degrees Celsius? What percent/proportion do these represent? Use: - `filter()` and `nrow()` - `group_by()` and `summarize()` or diff --git a/modules/HW/homework2.html b/modules/HW/homework2.html index 27567484..bed09834 100644 --- a/modules/HW/homework2.html +++ b/modules/HW/homework2.html @@ -13,228 +13,34 @@ Introduction to R: Homework 2 - - + + - - - - + + + + - - + h1.title {font-size: 38px;} + h2 {font-size: 30px;} + h3 {font-size: 24px;} + h4 {font-size: 18px;} + h5 {font-size: 16px;} + h6 {font-size: 12px;} + code {color: inherit; background-color: rgba(0, 0, 0, 0.04);} + pre:not([class]) { background-color: white } + + + code{white-space: pre-wrap;} + span.smallcaps{font-variant: small-caps;} + span.underline{text-decoration: underline;} + div.column{display: inline-block; vertical-align: top; width: 50%;} + div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} + ul.task-list{list-style: none;} + - - - - - - - - - - - - - - + code span.al { color: #ff0000; font-weight: bold; } /* Alert */ + code span.an { color: #60a0b0; font-weight: bold; font-style: italic; } /* Annotation */ + code span.at { color: #7d9029; } /* Attribute */ + code span.bn { color: #40a070; } /* BaseN */ + code span.bu { } /* BuiltIn */ + code span.cf { color: #007020; font-weight: bold; } /* ControlFlow */ + code span.ch { color: #4070a0; } /* Char */ + code span.cn { color: #880000; } /* Constant */ + code span.co { color: #60a0b0; font-style: italic; } /* Comment */ + code span.cv { color: #60a0b0; font-weight: bold; font-style: italic; } /* CommentVar */ + code span.do { color: #ba2121; font-style: italic; } /* Documentation */ + code span.dt { color: #902000; } /* DataType */ + code span.dv { color: #40a070; } /* DecVal */ + code span.er { color: #ff0000; font-weight: bold; } /* Error */ + code span.ex { } /* Extension */ + code span.fl { color: #40a070; } /* Float */ + code span.fu { color: #06287e; } /* Function */ + code span.im { } /* Import */ + code span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Information */ + code span.kw { color: #007020; font-weight: bold; } /* Keyword */ + code span.op { color: #666666; } /* Operator */ + code span.ot { color: #007020; } /* Other */ + code span.pp { color: #bc7a00; } /* Preprocessor */ + code span.sc { color: #4070a0; } /* SpecialChar */ + code span.ss { color: #bb6688; } /* SpecialString */ + code span.st { color: #4070a0; } /* String */ + code span.va { color: #19177c; } /* Variable */ + code span.vs { color: #4070a0; } /* VerbatimString */ + code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warning */ + + + + @@ -3322,10 +234,10 @@

library(tidyverse) # loads dplyr and other packages!
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
-✔ forcats   1.0.0     ✔ stringr   1.5.1
+✔ forcats   1.0.0     ✔ readr     2.1.5
+✔ ggplot2   3.5.1     ✔ stringr   1.5.1
 ✔ lubridate 1.9.3     ✔ tibble    3.2.1
 ✔ purrr     1.0.2     ✔ tidyr     1.3.1
-✔ readr     2.1.5     
 ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
 ✖ dplyr::filter() masks stats::filter()
 ✖ dplyr::lag()    masks stats::lag()
@@ -3333,18 +245,15 @@ 

Getting data to work with

-

We will work with data called ER about heat-related ER visits between 2011 and 2022, as reported by the state of Colorado, specifically made available by the Colorado Environmental Public Health Tracking program website. Full dataset available at https://coepht.colorado.gov/heat-related-illness.

+

We will work with data called er about heat-related ER visits between 2011 and 2022, as reported by the state of Colorado, specifically made available by the Colorado Environmental Public Health Tracking program website. Full dataset available at https://coepht.colorado.gov/heat-related-illness.

-
library(dasehr)
-ER <- CO_heat_ER_bygender
-#or we could do this:
-ER <- 
-  read_csv("https://daseh.org/data/Colorado_ER_heat_visits_by_county_gender.csv")
+
er <- 
+  read_csv("https://daseh.org/data/CO_ER_heat_visits.csv")
-
Rows: 240 Columns: 7
+
Rows: 768 Columns: 6
 ── Column specification ────────────────────────────────────────────────────────
 Delimiter: ","
-chr (2): county, gender
+chr (1): county
 dbl (5): rate, lower95cl, upper95cl, visits, year
 
 ℹ Use `spec()` to retrieve the full column specification for this data.
@@ -3354,53 +263,54 @@ 

The dim(), nrow(), and ncol() functions are good options to check the dimensions of your data before moving forward.

-
dim(ER) # rows, columns
+
dim(er) # rows, columns
-
[1] 240   7
+
[1] 768   6
-
nrow(ER) # number of rows
+
nrow(er) # number of rows
-
[1] 240
+
[1] 768
-
ncol(ER) # number of columns
+
ncol(er) # number of columns
-
[1] 7
+
[1] 6

Checking the data: glimpse()

In addition to head() and tail(), the glimpse()function of the dplyr package is another great function to look at your data.

-
glimpse(ER)
+
glimpse(er)
-
Rows: 240
-Columns: 7
+
Rows: 768
+Columns: 6
 $ county    <chr> "Adams", "Adams", "Adams", "Adams", "Adams", "Adams", "Adams…
-$ rate      <dbl> 7.596958, NA, 6.218501, NA, NA, 6.161907, 4.685828, 6.386427…
-$ lower95cl <dbl> 4.383039, NA, 3.367822, NA, NA, 3.350234, 2.314647, 3.619401…
-$ upper95cl <dbl> 11.680261, NA, 9.928823, NA, NA, 9.816068, 7.879132, 9.92620…
-$ visits    <dbl> 17, NA, 14, NA, NA, 14, 11, 16, 17, NA, 19, 18, 12, 13, 17, …
-$ year      <dbl> 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, …
-$ gender    <chr> "Female", "Female", "Female", "Female", "Female", "Female", …
+$ rate <dbl> 6.729918, 4.843983, 6.836648, 3.080950, 3.356538, 8.848504, … +$ lower95cl <dbl> NA, 2.848937, 4.359735, 1.711087, 1.892912, 6.124961, 4.2920… +$ upper95cl <dbl> 9.236776, NA, 9.313561, 4.846996, 5.232461, 11.572046, 8.977… +$ visits <dbl> 29, 23, 31, 15, 16, 42, 32, 37, 36, 24, 35, 45, 0, 0, NA, 0,… +$ year <dbl> 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, …
+ +

This dataset has information about both the rate of ER visits for heat-related illness and the number of visits, broken out by both year and CO county.

Checking your data: slice_sample()

What if you want to see the middle of your data? You can use the slice_sample() function of the dplyr package to see a random set of rows. You can specify the number of rows with the n argument.

-
slice_sample(ER, n = 2)
+
slice_sample(er, n = 2)
-
# A tibble: 2 × 7
-  county    rate lower95cl upper95cl visits  year gender
-  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr> 
-1 Arapahoe  7.18      4.04      10.3     21  2016 Male  
-2 Weld     NA        NA         NA       NA  2014 Female
+
# A tibble: 2 × 6
+  county      rate lower95cl upper95cl visits  year
+  <chr>      <dbl>     <dbl>     <dbl>  <dbl> <dbl>
+1 Las Animas    NA        NA        NA     NA  2017
+2 Jackson        0         0         0      0  2011
-
slice_sample(ER, n = 2)
+
slice_sample(er, n = 2)
-
# A tibble: 2 × 7
-  county     rate lower95cl upper95cl visits  year gender
-  <chr>     <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr> 
-1 Cheyenne   0         0         0         0  2012 Male  
-2 Jefferson  4.15      2.01      7.04     11  2011 Female
+
# A tibble: 2 × 6
+  county  rate lower95cl upper95cl visits  year
+  <chr>  <dbl>     <dbl>     <dbl>  <dbl> <dbl>
+1 Routt      0         0         0      0  2017
+2 Moffat    NA        NA        NA     NA  2012

Data frames and tibbles

@@ -3432,22 +342,22 @@

  • We see the data types for each column
  • -
    ER
    - -
    # A tibble: 240 × 7
    -   county  rate lower95cl upper95cl visits  year gender
    -   <chr>  <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr> 
    - 1 Adams   7.60      4.38     11.7      17  2011 Female
    - 2 Adams  NA        NA        NA        NA  2012 Female
    - 3 Adams   6.22      3.37      9.93     14  2013 Female
    - 4 Adams  NA        NA        NA        NA  2014 Female
    - 5 Adams  NA        NA        NA        NA  2015 Female
    - 6 Adams   6.16      3.35      9.82     14  2016 Female
    - 7 Adams   4.69      2.31      7.88     11  2017 Female
    - 8 Adams   6.39      3.62      9.93     16  2018 Female
    - 9 Adams   6.64      3.85     10.2      17  2019 Female
    -10 Adams  NA        NA        NA        NA  2020 Female
    -# ℹ 230 more rows
    +
    er
    + +
    # A tibble: 768 × 6
    +   county  rate lower95cl upper95cl visits  year
    +   <chr>  <dbl>     <dbl>     <dbl>  <dbl> <dbl>
    + 1 Adams   6.73     NA         9.24     29  2011
    + 2 Adams   4.84      2.85     NA        23  2012
    + 3 Adams   6.84      4.36      9.31     31  2013
    + 4 Adams   3.08      1.71      4.85     15  2014
    + 5 Adams   3.36      1.89      5.23     16  2015
    + 6 Adams   8.85      6.12     11.6      42  2016
    + 7 Adams   6.63      4.29      8.98     32  2017
    + 8 Adams   7.11      4.77      9.44     37  2018
    + 9 Adams   6.76      4.53      8.99     36  2019
    +10 Adams   4.76      2.82      6.70     24  2020
    +# ℹ 758 more rows

    Creating a tibble

    @@ -3499,7 +409,7 @@

    1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
    -

    rownames_to_column function

    +

    rownames_to_column function

    There is a function that specifically helps you do that.

    @@ -3521,17 +431,17 @@

    Let’s stick with the tibble ER data for our next lesson

    -
    head(ER)
    +
    head(er)
    -
    # A tibble: 6 × 7
    -  county  rate lower95cl upper95cl visits  year gender
    -  <chr>  <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr> 
    -1 Adams   7.60      4.38     11.7      17  2011 Female
    -2 Adams  NA        NA        NA        NA  2012 Female
    -3 Adams   6.22      3.37      9.93     14  2013 Female
    -4 Adams  NA        NA        NA        NA  2014 Female
    -5 Adams  NA        NA        NA        NA  2015 Female
    -6 Adams   6.16      3.35      9.82     14  2016 Female
    +
    # A tibble: 6 × 6
    +  county  rate lower95cl upper95cl visits  year
    +  <chr>  <dbl>     <dbl>     <dbl>  <dbl> <dbl>
    +1 Adams   6.73     NA         9.24     29  2011
    +2 Adams   4.84      2.85     NA        23  2012
    +3 Adams   6.84      4.36      9.31     31  2013
    +4 Adams   3.08      1.71      4.85     15  2014
    +5 Adams   3.36      1.89      5.23     16  2015
    +6 Adams   8.85      6.12     11.6      42  2016

    Renaming Columns

    @@ -3541,34 +451,35 @@

    checking names of columns, we can use the colnames() function (or names())

    -
    colnames(ER)
    +
    colnames(er)
    -
    [1] "county"    "rate"      "lower95cl" "upper95cl" "visits"    "year"     
    -[7] "gender"   
    +
    [1] "county"    "rate"      "lower95cl" "upper95cl" "visits"    "year"     

    Renaming Columns of a data frame or tibble

    To rename columns in dplyr, you can use the rename function.

    -

    For example, let’s rename lower95CI to lower_95_CI_limit. Notice the new name is listed first, similar to how a new object is assigned on the left!

    +

    Let’s rename lower95CI to lower_95_CI_limit. Notice the new name is listed first, similar to how a new object is assigned on the left!

    + +

    The lower95CI is the lower bound of the estimated rate of ER visits for a particular county and year.

    # general format! not code!
     {data you are creating or changing} <- rename({data you are using}, 
                                               {New Name} = {Old name})
    -
    renamed_ER<- rename(ER, lower_95_CI_limit = lower95cl)
    -head(renamed_ER)
    +
    renamed_er<- rename(er, lower_95_CI_limit = lower95cl)
    +head(renamed_er)
    -
    # A tibble: 6 × 7
    -  county  rate lower_95_CI_limit upper95cl visits  year gender
    -  <chr>  <dbl>             <dbl>     <dbl>  <dbl> <dbl> <chr> 
    -1 Adams   7.60              4.38     11.7      17  2011 Female
    -2 Adams  NA                NA        NA        NA  2012 Female
    -3 Adams   6.22              3.37      9.93     14  2013 Female
    -4 Adams  NA                NA        NA        NA  2014 Female
    -5 Adams  NA                NA        NA        NA  2015 Female
    -6 Adams   6.16              3.35      9.82     14  2016 Female
    +
    # A tibble: 6 × 6
    +  county  rate lower_95_CI_limit upper95cl visits  year
    +  <chr>  <dbl>             <dbl>     <dbl>  <dbl> <dbl>
    +1 Adams   6.73             NA         9.24     29  2011
    +2 Adams   4.84              2.85     NA        23  2012
    +3 Adams   6.84              4.36      9.31     31  2013
    +4 Adams   3.08              1.71      4.85     15  2014
    +5 Adams   3.36              1.89      5.23     16  2015
    +6 Adams   8.85              6.12     11.6      42  2016

    Take Care with Column Names

    @@ -3577,17 +488,17 @@

    See https://daseh.org/resources/quotes_vs_backticks.html for more guidance.

     # this will cause an error
    -renamed_ER <- rename(ER, lower_95%_CI_limit = lower95cl)
    +renamed_er <- rename(er, lower_95%_CI_limit = lower95cl)
    # this will work
    -renamed_ER <- rename(ER, `lower_95%_CI_limit` = lower95cl) 
    -head(renamed_ER, 2)
    +renamed_er <- rename(er, `lower_95%_CI_limit` = lower95cl) +head(renamed_er, 2) -
    # A tibble: 2 × 7
    -  county  rate `lower_95%_CI_limit` upper95cl visits  year gender
    -  <chr>  <dbl>                <dbl>     <dbl>  <dbl> <dbl> <chr> 
    -1 Adams   7.60                 4.38      11.7     17  2011 Female
    -2 Adams  NA                   NA         NA       NA  2012 Female
    +
    # A tibble: 2 × 6
    +  county  rate `lower_95%_CI_limit` upper95cl visits  year
    +  <chr>  <dbl>                <dbl>     <dbl>  <dbl> <dbl>
    +1 Adams   6.73                NA         9.24     29  2011
    +2 Adams   4.84                 2.85     NA        23  2012

    Unusual Column Names

    @@ -3617,65 +528,65 @@

    Curly quotes will not work!

    # this will cause an error!
    -renamed_ER <- rename(ER, ‘lower_95%_CI_limit’ = lower95cl)
    +renamed_er <- rename(er, ‘lower_95%_CI_limit’ = lower95cl)
    # this will work!
    -renamed_ER <- rename(ER, 'lower_95%_CI_limit' = lower95cl)
    +renamed_er <- rename(er, 'lower_95%_CI_limit' = lower95cl)

    Also true for double quotes

     # this will cause an error!
    -renamed_ER <- rename(ER, “lower_95%_CI_limit” = lower95cl)
    +renamed_er <- rename(er, “lower_95%_CI_limit” = lower95cl)
    # this will work!
    -renamed_ER <- rename(ER, "lower_95%_CI_limit" = lower95cl)
    +renamed_er <- rename(er, "lower_95%_CI_limit" = lower95cl)

    Rename multiple columns

    A comma can separate different column names to change.

    -
    renamed_ER <- rename(ER, 
    +
    renamed_er <- rename(er, 
                             lower_95perc_CI_limit = lower95cl, 
                             upper_95perc_CI_limit = upper95cl)
    -head(renamed_ER, 3)
    +head(renamed_er, 3)
    -
    # A tibble: 3 × 7
    -  county  rate lower_95perc_CI_limit upper_95perc_CI_limit visits  year gender
    -  <chr>  <dbl>                 <dbl>                 <dbl>  <dbl> <dbl> <chr> 
    -1 Adams   7.60                  4.38                 11.7      17  2011 Female
    -2 Adams  NA                    NA                    NA        NA  2012 Female
    -3 Adams   6.22                  3.37                  9.93     14  2013 Female
    +
    # A tibble: 3 × 6
    +  county  rate lower_95perc_CI_limit upper_95perc_CI_limit visits  year
    +  <chr>  <dbl>                 <dbl>                 <dbl>  <dbl> <dbl>
    +1 Adams   6.73                 NA                     9.24     29  2011
    +2 Adams   4.84                  2.85                 NA        23  2012
    +3 Adams   6.84                  4.36                  9.31     31  2013
    -

    Renaming all columns of a data frame: dplyr

    +

    Renaming all columns of a data frame: dplyr

    To rename all columns you use the rename_with(). In this case we will use toupper() to make all letters upper case. Could also use tolower() function.

    -
    ER_upper <- rename_with(ER, toupper)
    -head(ER_upper, 3)
    - -
    # A tibble: 3 × 7
    -  COUNTY  RATE LOWER95CL UPPER95CL VISITS  YEAR GENDER
    -  <chr>  <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr> 
    -1 Adams   7.60      4.38     11.7      17  2011 Female
    -2 Adams  NA        NA        NA        NA  2012 Female
    -3 Adams   6.22      3.37      9.93     14  2013 Female
    - -
    rename_with(ER_upper, tolower)
    - -
    # A tibble: 240 × 7
    -   county  rate lower95cl upper95cl visits  year gender
    -   <chr>  <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr> 
    - 1 Adams   7.60      4.38     11.7      17  2011 Female
    - 2 Adams  NA        NA        NA        NA  2012 Female
    - 3 Adams   6.22      3.37      9.93     14  2013 Female
    - 4 Adams  NA        NA        NA        NA  2014 Female
    - 5 Adams  NA        NA        NA        NA  2015 Female
    - 6 Adams   6.16      3.35      9.82     14  2016 Female
    - 7 Adams   4.69      2.31      7.88     11  2017 Female
    - 8 Adams   6.39      3.62      9.93     16  2018 Female
    - 9 Adams   6.64      3.85     10.2      17  2019 Female
    -10 Adams  NA        NA        NA        NA  2020 Female
    -# ℹ 230 more rows
    +
    er_upper <- rename_with(er, toupper)
    +head(er_upper, 3)
    + +
    # A tibble: 3 × 6
    +  COUNTY  RATE LOWER95CL UPPER95CL VISITS  YEAR
    +  <chr>  <dbl>     <dbl>     <dbl>  <dbl> <dbl>
    +1 Adams   6.73     NA         9.24     29  2011
    +2 Adams   4.84      2.85     NA        23  2012
    +3 Adams   6.84      4.36      9.31     31  2013
    + +
    rename_with(er_upper, tolower)
    + +
    # A tibble: 768 × 6
    +   county  rate lower95cl upper95cl visits  year
    +   <chr>  <dbl>     <dbl>     <dbl>  <dbl> <dbl>
    + 1 Adams   6.73     NA         9.24     29  2011
    + 2 Adams   4.84      2.85     NA        23  2012
    + 3 Adams   6.84      4.36      9.31     31  2013
    + 4 Adams   3.08      1.71      4.85     15  2014
    + 5 Adams   3.36      1.89      5.23     16  2015
    + 6 Adams   8.85      6.12     11.6      42  2016
    + 7 Adams   6.63      4.29      8.98     32  2017
    + 8 Adams   7.11      4.77      9.44     37  2018
    + 9 Adams   6.76      4.53      8.99     36  2019
    +10 Adams   4.76      2.82      6.70     24  2020
    +# ℹ 758 more rows

    janitor package

    @@ -3688,10 +599,12 @@

    The clean_names function can intuit what fixes you might need. Here it make sure year names aren’t just a number, so that the colnames don’t need ticks or quotes to be used.

    +

    The yearly_co2_emissions dataset contains estimated CO2 emissions for 265 countries between the years 1751 and 2014.

    +
    #library(dasehr)
    -CO2 <- dasehr::yearly_co2_emissions
    +yearly_co2 <- dasehr::yearly_co2_emissions
     # or this:
    -CO2 <- 
    +yearly_co2 <- 
       read_csv("https://daseh.org/data/Yearly_CO2_Emissions_1000_tonnes.csv")
    Rows: 192 Columns: 265
    @@ -3703,7 +616,7 @@ 

    ℹ Use `spec()` to retrieve the full column specification for this data. ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
    -
    head(CO2, n = 2)
    +
    head(yearly_co2, n = 2)
    # A tibble: 2 × 265
       country  `1751` `1752` `1753` `1754` `1755` `1756` `1757` `1758` `1759` `1760`
    @@ -3718,7 +631,7 @@ 

    # `1785` <dbl>, `1786` <dbl>, `1787` <dbl>, `1788` <dbl>, `1789` <dbl>, # `1790` <dbl>, `1791` <dbl>, `1792` <dbl>, `1793` <dbl>, `1794` <dbl>, …
    -
    clean_names(CO2)
    +
    clean_names(yearly_co2)
    # A tibble: 192 × 265
        country     x1751 x1752 x1753 x1754 x1755 x1756 x1757 x1758 x1759 x1760 x1761
    @@ -3786,93 +699,95 @@ 

    Let’s get our data again

    +

    We’ll work with the CO heat-related ER visits dataset again.

    +

    This time lets also make it a smaller subset so it is easier for us to see the full dataset as we work through examples.

    #library(dasehr)
    -#ER <- CO_heat_ER_bygender
    +#er <- CO_heat_ER
     #or this:
    -#read_csv("https://daseh.org/data/Colorado_ER_heat_visits_by_county_gender.csv")
    +#read_csv("https://daseh.org/data/CO_ER_heat_visits.csv")
     set.seed(1234)
    -ER_30 <-slice_sample(ER, n = 30)
    +er_30 <-slice_sample(er, n = 30)

    Subset columns of a data frame - tidyverse way:

    To grab (or “pull” out) the year column the tidyverse way we can use the pull function:

    -
    pull(ER_30, year)
    +
    pull(er_30, year)
    -
     [1] 2014 2018 2016 2015 2018 2013 2015 2011 2020 2022 2022 2012 2017 2020 2016
    -[16] 2020 2017 2012 2018 2012 2016 2014 2012 2014 2012 2015 2014 2018 2013 2013
    +
     [1] 2018 2015 2021 2019 2014 2012 2017 2016 2012 2012 2017 2016 2020 2014 2020
    +[16] 2014 2011 2022 2018 2013 2017 2021 2015 2020 2012 2016 2013 2014 2017 2022

    Subset columns of a data frame: dplyr

    The select command from dplyr allows you to subset (still a tibble!)

    -
    select(ER_30, year)
    +
    select(er_30, year)
    # A tibble: 30 × 1
         year
        <dbl>
    - 1  2014
    - 2  2018
    - 3  2016
    - 4  2015
    - 5  2018
    - 6  2013
    - 7  2015
    - 8  2011
    - 9  2020
    -10  2022
    + 1  2018
    + 2  2015
    + 3  2021
    + 4  2019
    + 5  2014
    + 6  2012
    + 7  2017
    + 8  2016
    + 9  2012
    +10  2012
     # ℹ 20 more rows

    Select multiple columns

    We can use select to select for multiple columns.

    -
    select(ER_30, year, rate, county)
    +
    select(er_30, year, rate, county)
    # A tibble: 30 × 3
    -    year  rate county   
    -   <dbl> <dbl> <chr>    
    - 1  2014 NA    Arapahoe 
    - 2  2018  3.76 Denver   
    - 3  2016  8.13 Larimer  
    - 4  2015  3.94 El Paso  
    - 5  2018 11.1  Weld     
    - 6  2013  5.99 El Paso  
    - 7  2015  4.12 Jefferson
    - 8  2011  5.76 Jefferson
    - 9  2020 NA    Larimer  
    -10  2022  5.01 Jefferson
    +    year  rate county    
    +   <dbl> <dbl> <chr>     
    + 1  2018  NA   Garfield  
    + 2  2015  NA   Chaffee   
    + 3  2021  15.9 Pueblo    
    + 4  2019   0   Rio Grande
    + 5  2014   0   Lake      
    + 6  2012  NA   Chaffee   
    + 7  2017  NA   Chaffee   
    + 8  2016   0   Teller    
    + 9  2012  NA   Prowers   
    +10  2012   0   Hinsdale  
     # ℹ 20 more rows

    Select columns of a data frame: dplyr

    The select command from dplyr allows you to subset columns matching patterns:

    -
    head(ER_30, 2)
    +
    head(er_30, 2)
    -
    # A tibble: 2 × 7
    -  county    rate lower95cl upper95cl visits  year gender
    -  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr> 
    -1 Arapahoe NA        NA        NA        NA  2014 Female
    -2 Denver    3.76      1.97      6.12     13  2018 Female
    +
    # A tibble: 2 × 6
    +  county    rate lower95cl upper95cl visits  year
    +  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl>
    +1 Garfield    NA        NA        NA     NA  2018
    +2 Chaffee     NA        NA        NA     NA  2015
    -
    select(ER_30, ends_with("cl"))
    +
    select(er_30, ends_with("cl"))
    # A tibble: 30 × 2
        lower95cl upper95cl
            <dbl>     <dbl>
    - 1     NA        NA   
    - 2      1.97      6.12
    - 3      4.32     13.1 
    - 4      2.01      6.52
    - 5      6.59     16.9 
    - 6      3.32      8.66
    - 7      2.02      6.94
    - 8      3.12      9.20
    - 9     NA        NA   
    -10      2.71      7.99
    + 1     NA         NA  
    + 2     NA         NA  
    + 3      9.54      22.2
    + 4      0          0  
    + 5      0          0  
    + 6     NA         NA  
    + 7     NA         NA  
    + 8      0          0  
    + 9     NA         NA  
    +10      0          0  
     # ℹ 20 more rows

    See the Select “helpers”

    @@ -3890,100 +805,100 @@

    Combining tidyselect helpers with regular selection

    -
    head(ER_30, 2)
    +
    head(er_30, 2)
    -
    # A tibble: 2 × 7
    -  county    rate lower95cl upper95cl visits  year gender
    -  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr> 
    -1 Arapahoe NA        NA        NA        NA  2014 Female
    -2 Denver    3.76      1.97      6.12     13  2018 Female
    +
    # A tibble: 2 × 6
    +  county    rate lower95cl upper95cl visits  year
    +  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl>
    +1 Garfield    NA        NA        NA     NA  2018
    +2 Chaffee     NA        NA        NA     NA  2015
    -
    select(ER_30, ends_with("cl"), year)
    +
    select(er_30, ends_with("cl"), year)
    # A tibble: 30 × 3
        lower95cl upper95cl  year
            <dbl>     <dbl> <dbl>
    - 1     NA        NA     2014
    - 2      1.97      6.12  2018
    - 3      4.32     13.1   2016
    - 4      2.01      6.52  2015
    - 5      6.59     16.9   2018
    - 6      3.32      8.66  2013
    - 7      2.02      6.94  2015
    - 8      3.12      9.20  2011
    - 9     NA        NA     2020
    -10      2.71      7.99  2022
    + 1     NA         NA    2018
    + 2     NA         NA    2015
    + 3      9.54      22.2  2021
    + 4      0          0    2019
    + 5      0          0    2014
    + 6     NA         NA    2012
    + 7     NA         NA    2017
    + 8      0          0    2016
    + 9     NA         NA    2012
    +10      0          0    2012
     # ℹ 20 more rows

    Multiple tidyselect functions

    Follows OR logic.

    -
    select(ER_30, ends_with("cl"), starts_with("r"))
    +
    select(er_30, ends_with("cl"), starts_with("r"))
    # A tibble: 30 × 3
        lower95cl upper95cl  rate
            <dbl>     <dbl> <dbl>
    - 1     NA        NA    NA   
    - 2      1.97      6.12  3.76
    - 3      4.32     13.1   8.13
    - 4      2.01      6.52  3.94
    - 5      6.59     16.9  11.1 
    - 6      3.32      8.66  5.99
    - 7      2.02      6.94  4.12
    - 8      3.12      9.20  5.76
    - 9     NA        NA    NA   
    -10      2.71      7.99  5.01
    + 1     NA         NA    NA  
    + 2     NA         NA    NA  
    + 3      9.54      22.2  15.9
    + 4      0          0     0  
    + 5      0          0     0  
    + 6     NA         NA    NA  
    + 7     NA         NA    NA  
    + 8      0          0     0  
    + 9     NA         NA    NA  
    +10      0          0     0  
     # ℹ 20 more rows

    Multiple patterns with tidyselect

    Need to combine the patterns with the c() function.

    -
    select(ER_30, starts_with(c("r", "l")))
    +
    select(er_30, starts_with(c("r", "l")))
    # A tibble: 30 × 2
         rate lower95cl
        <dbl>     <dbl>
    - 1 NA        NA   
    - 2  3.76      1.97
    - 3  8.13      4.32
    - 4  3.94      2.01
    - 5 11.1       6.59
    - 6  5.99      3.32
    - 7  4.12      2.02
    - 8  5.76      3.12
    - 9 NA        NA   
    -10  5.01      2.71
    + 1  NA       NA   
    + 2  NA       NA   
    + 3  15.9      9.54
    + 4   0        0   
    + 5   0        0   
    + 6  NA       NA   
    + 7  NA       NA   
    + 8   0        0   
    + 9  NA       NA   
    +10   0        0   
     # ℹ 20 more rows
    -

    The where() function can help select columns of a specific class

    +

    The where() function can help select columns of a specific class

    is.character() and is.numeric() are often the most helpful

    -
    head(ER_30, 2)
    +
    head(er_30, 2)
    -
    # A tibble: 2 × 7
    -  county    rate lower95cl upper95cl visits  year gender
    -  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr> 
    -1 Arapahoe NA        NA        NA        NA  2014 Female
    -2 Denver    3.76      1.97      6.12     13  2018 Female
    +
    # A tibble: 2 × 6
    +  county    rate lower95cl upper95cl visits  year
    +  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl>
    +1 Garfield    NA        NA        NA     NA  2018
    +2 Chaffee     NA        NA        NA     NA  2015
    -
    select(ER_30, where(is.numeric))
    +
    select(er_30, where(is.numeric))
    # A tibble: 30 × 5
         rate lower95cl upper95cl visits  year
        <dbl>     <dbl>     <dbl>  <dbl> <dbl>
    - 1 NA        NA        NA        NA  2014
    - 2  3.76      1.97      6.12     13  2018
    - 3  8.13      4.32     13.1      14  2016
    - 4  3.94      2.01      6.52     12  2015
    - 5 11.1       6.59     16.9      18  2018
    - 6  5.99      3.32      8.66     20  2013
    - 7  4.12      2.02      6.94     11  2015
    - 8  5.76      3.12      9.20     14  2011
    - 9 NA        NA        NA        NA  2020
    -10  5.01      2.71      7.99     14  2022
    + 1  NA       NA         NA       NA  2018
    + 2  NA       NA         NA       NA  2015
    + 3  15.9      9.54      22.2     26  2021
    + 4   0        0          0        0  2019
    + 5   0        0          0        0  2014
    + 6  NA       NA         NA       NA  2012
    + 7  NA       NA         NA       NA  2017
    + 8   0        0          0        0  2016
    + 9  NA       NA         NA       NA  2012
    +10   0        0          0        0  2012
     # ℹ 20 more rows

    Subsetting Rows

    @@ -3998,13 +913,15 @@

    The command in dplyr for subsetting rows is filter.

    -
    filter(ER_30, year > 2020)
    +
    filter(er_30, year > 2020)
    -
    # A tibble: 2 × 7
    -  county     rate lower95cl upper95cl visits  year gender
    -  <chr>     <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr> 
    -1 Jefferson  5.01      2.71      7.99     14  2022 Male  
    -2 Jefferson  4.56      2.43      7.35     14  2022 Female
    +
    # A tibble: 4 × 6
    +  county   rate lower95cl upper95cl visits  year
    +  <chr>   <dbl>     <dbl>     <dbl>  <dbl> <dbl>
    +1 Pueblo   15.9      9.54      22.2     26  2021
    +2 Otero    NA       NA         NA       NA  2022
    +3 Mesa     12.0      7.12      18.2     19  2021
    +4 Chaffee   0        0          0        0  2022

    Subset rows of a data frame: dplyr

    @@ -4028,164 +945,149 @@

  • column that was already removed
  • -

    Subset rows of a data frame: dplyr

    +

    Subset rows of a data frame: dplyr

    You can filter by two conditions using & or commas (must meet both conditions):

    -
    filter(ER_30, rate > 9, year == 2012)
    +
    filter(er_30, rate > 9, year == 2012)
    -
    filter(ER_30, rate > 9 & year == 2012) # same result
    +
    filter(er_30, rate > 9 & year == 2012) # same result
    -
    # A tibble: 1 × 7
    -  county  rate lower95cl upper95cl visits  year gender
    -  <chr>  <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr> 
    -1 Weld    12.8      6.96      20.3     15  2012 Male  
    +
    # A tibble: 0 × 6
    +# ℹ 6 variables: county <chr>, rate <dbl>, lower95cl <dbl>, upper95cl <dbl>,
    +#   visits <dbl>, year <dbl>

    Subset rows of a data frame: dplyr

    If you want OR statements (meaning the data can meet either condition does not need to meet both), you need to use | between conditions:

    -
    filter(ER_30, rate > 9 | year == 2012)
    +
    filter(er_30, rate > 9 | year == 2012)
    -
    # A tibble: 9 × 7
    -  county     rate lower95cl upper95cl visits  year gender
    -  <chr>     <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr> 
    -1 Weld      11.1       6.59     16.9      18  2018 Male  
    -2 El Paso   NA        NA        NA        NA  2012 Female
    -3 Statewide  7.56      6.48      8.65    193  2012 Male  
    -4 El Paso    9.07      5.74     12.4      31  2018 Male  
    -5 Adams      5.48      2.90      8.88     13  2012 Male  
    -6 Pueblo    18.5       9.91     29.7      14  2014 Male  
    -7 Cheyenne   0         0         0         0  2012 Male  
    -8 Weld      12.8       6.96     20.3      15  2012 Male  
    -9 Statewide 10.1       8.95     11.3     293  2018 Male  
    +
    # A tibble: 6 × 6
    +  county    rate lower95cl upper95cl visits  year
    +  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl>
    +1 Pueblo    15.9      9.54      22.2     26  2021
    +2 Chaffee   NA       NA         NA       NA  2012
    +3 Prowers   NA       NA         NA       NA  2012
    +4 Hinsdale   0        0          0        0  2012
    +5 Mesa      12.0      7.12      18.2     19  2021
    +6 Phillips   0        0          0        0  2012
    -

    Subset rows of a data frame: dplyr

    +

    Subset rows of a data frame: dplyr

    The %in% operator can be used find values from a pre-made list (using c()) for a single column at a time.

    -
    filter(ER_30,  county %in% c("Denver","Weld","Arapahoe"))
    - -
    # A tibble: 8 × 7
    -  county    rate lower95cl upper95cl visits  year gender
    -  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr> 
    -1 Arapahoe NA        NA        NA        NA  2014 Female
    -2 Denver    3.76      1.97      6.12     13  2018 Female
    -3 Weld     11.1       6.59     16.9      18  2018 Male  
    -4 Denver    8.51      5.30     11.7      30  2016 Male  
    -5 Denver    3.56      1.82      5.88     13  2017 Female
    -6 Weld     12.8       6.96     20.3      15  2012 Male  
    -7 Arapahoe NA        NA        NA        NA  2014 Male  
    -8 Weld     NA        NA        NA        NA  2013 Female
    - -
    filter(ER_30,  county == "Denver"| county == "Weld"| county == "Arapahoe") #equivalent
    - -
    # A tibble: 8 × 7
    -  county    rate lower95cl upper95cl visits  year gender
    -  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr> 
    -1 Arapahoe NA        NA        NA        NA  2014 Female
    -2 Denver    3.76      1.97      6.12     13  2018 Female
    -3 Weld     11.1       6.59     16.9      18  2018 Male  
    -4 Denver    8.51      5.30     11.7      30  2016 Male  
    -5 Denver    3.56      1.82      5.88     13  2017 Female
    -6 Weld     12.8       6.96     20.3      15  2012 Male  
    -7 Arapahoe NA        NA        NA        NA  2014 Male  
    -8 Weld     NA        NA        NA        NA  2013 Female
    - -

    Subset rows of a data frame: dplyr

    +
    filter(er_30,  county %in% c("Denver","Larimer","Pueblo"))
    + +
    # A tibble: 3 × 6
    +  county   rate lower95cl upper95cl visits  year
    +  <chr>   <dbl>     <dbl>     <dbl>  <dbl> <dbl>
    +1 Pueblo  15.9       9.54     22.2      26  2021
    +2 Denver   2.95      1.75      4.46     19  2013
    +3 Larimer  5.49      3.16      8.45     17  2014
    + +
    filter(er_30,  county == "Denver"| county == "Larimer"| county == "Pueblo") #equivalent
    + +
    # A tibble: 3 × 6
    +  county   rate lower95cl upper95cl visits  year
    +  <chr>   <dbl>     <dbl>     <dbl>  <dbl> <dbl>
    +1 Pueblo  15.9       9.54     22.2      26  2021
    +2 Denver   2.95      1.75      4.46     19  2013
    +3 Larimer  5.49      3.16      8.45     17  2014
    + +

    Subset rows of a data frame: dplyr

    The %in% operator can be used find values from a pre-made list (using c()) for a single column at a time with different columns.

    -
    filter(ER_30, year %in% c(2013,2020), county %in% c("Denver","Weld"))
    +
    filter(er_30, year %in% c(2013,2021), county %in% c("Denver","Pueblo"))
    -
    # A tibble: 1 × 7
    -  county  rate lower95cl upper95cl visits  year gender
    -  <chr>  <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr> 
    -1 Weld      NA        NA        NA     NA  2013 Female
    +
    # A tibble: 2 × 6
    +  county  rate lower95cl upper95cl visits  year
    +  <chr>  <dbl>     <dbl>     <dbl>  <dbl> <dbl>
    +1 Pueblo 15.9       9.54     22.2      26  2021
    +2 Denver  2.95      1.75      4.46     19  2013

    Be careful with column names and filter

    This will not work the way you might expect! Best to stick with nothing but the column name if it is a typical name.

    -
    filter(ER_30, "year" > 2014)
    +
    filter(er_30, "year" > 2014)
    -
    # A tibble: 30 × 7
    -   county     rate lower95cl upper95cl visits  year gender
    -   <chr>     <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr> 
    - 1 Arapahoe  NA        NA        NA        NA  2014 Female
    - 2 Denver     3.76      1.97      6.12     13  2018 Female
    - 3 Larimer    8.13      4.32     13.1      14  2016 Female
    - 4 El Paso    3.94      2.01      6.52     12  2015 Female
    - 5 Weld      11.1       6.59     16.9      18  2018 Male  
    - 6 El Paso    5.99      3.32      8.66     20  2013 Male  
    - 7 Jefferson  4.12      2.02      6.94     11  2015 Male  
    - 8 Jefferson  5.76      3.12      9.20     14  2011 Male  
    - 9 Larimer   NA        NA        NA        NA  2020 Male  
    -10 Jefferson  5.01      2.71      7.99     14  2022 Male  
    +
    # A tibble: 30 × 6
    +   county      rate lower95cl upper95cl visits  year
    +   <chr>      <dbl>     <dbl>     <dbl>  <dbl> <dbl>
    + 1 Garfield    NA       NA         NA       NA  2018
    + 2 Chaffee     NA       NA         NA       NA  2015
    + 3 Pueblo      15.9      9.54      22.2     26  2021
    + 4 Rio Grande   0        0          0        0  2019
    + 5 Lake         0        0          0        0  2014
    + 6 Chaffee     NA       NA         NA       NA  2012
    + 7 Chaffee     NA       NA         NA       NA  2017
    + 8 Teller       0        0          0        0  2016
    + 9 Prowers     NA       NA         NA       NA  2012
    +10 Hinsdale     0        0          0        0  2012
     # ℹ 20 more rows

    Don’t use quotes for atypical names

    Atypical names are those with punctuation, spaces, start with a number, or are just a number.

    -
    ER_30_rename <- rename(ER_30, `year!` = year)
    +
    er_30_rename <- rename(er_30, `year!` = year)
     
    -filter(ER_30_rename, "year!" > 2013) # will not work correctly
    +filter(er_30_rename, "year!" > 2013) # will not work correctly
    -
    # A tibble: 30 × 7
    -   county     rate lower95cl upper95cl visits `year!` gender
    -   <chr>     <dbl>     <dbl>     <dbl>  <dbl>   <dbl> <chr> 
    - 1 Arapahoe  NA        NA        NA        NA    2014 Female
    - 2 Denver     3.76      1.97      6.12     13    2018 Female
    - 3 Larimer    8.13      4.32     13.1      14    2016 Female
    - 4 El Paso    3.94      2.01      6.52     12    2015 Female
    - 5 Weld      11.1       6.59     16.9      18    2018 Male  
    - 6 El Paso    5.99      3.32      8.66     20    2013 Male  
    - 7 Jefferson  4.12      2.02      6.94     11    2015 Male  
    - 8 Jefferson  5.76      3.12      9.20     14    2011 Male  
    - 9 Larimer   NA        NA        NA        NA    2020 Male  
    -10 Jefferson  5.01      2.71      7.99     14    2022 Male  
    +
    # A tibble: 30 × 6
    +   county      rate lower95cl upper95cl visits `year!`
    +   <chr>      <dbl>     <dbl>     <dbl>  <dbl>   <dbl>
    + 1 Garfield    NA       NA         NA       NA    2018
    + 2 Chaffee     NA       NA         NA       NA    2015
    + 3 Pueblo      15.9      9.54      22.2     26    2021
    + 4 Rio Grande   0        0          0        0    2019
    + 5 Lake         0        0          0        0    2014
    + 6 Chaffee     NA       NA         NA       NA    2012
    + 7 Chaffee     NA       NA         NA       NA    2017
    + 8 Teller       0        0          0        0    2016
    + 9 Prowers     NA       NA         NA       NA    2012
    +10 Hinsdale     0        0          0        0    2012
     # ℹ 20 more rows

    Be careful with column names and filter

    Using backticks works!

    -
    filter(ER_30_rename, `year!` > 2013) 
    - -
    # A tibble: 21 × 7
    -   county     rate lower95cl upper95cl visits `year!` gender
    -   <chr>     <dbl>     <dbl>     <dbl>  <dbl>   <dbl> <chr> 
    - 1 Arapahoe  NA        NA        NA        NA    2014 Female
    - 2 Denver     3.76      1.97      6.12     13    2018 Female
    - 3 Larimer    8.13      4.32     13.1      14    2016 Female
    - 4 El Paso    3.94      2.01      6.52     12    2015 Female
    - 5 Weld      11.1       6.59     16.9      18    2018 Male  
    - 6 Jefferson  4.12      2.02      6.94     11    2015 Male  
    - 7 Larimer   NA        NA        NA        NA    2020 Male  
    - 8 Jefferson  5.01      2.71      7.99     14    2022 Male  
    - 9 Jefferson  4.56      2.43      7.35     14    2022 Female
    -10 El Paso   NA        NA        NA        NA    2017 Female
    -# ℹ 11 more rows
    +
    filter(er_30_rename, `year!` > 2013) 
    + +
    # A tibble: 23 × 6
    +   county      rate lower95cl upper95cl visits `year!`
    +   <chr>      <dbl>     <dbl>     <dbl>  <dbl>   <dbl>
    + 1 Garfield   NA        NA        NA        NA    2018
    + 2 Chaffee    NA        NA        NA        NA    2015
    + 3 Pueblo     15.9       9.54     22.2      26    2021
    + 4 Rio Grande  0         0         0         0    2019
    + 5 Lake        0         0         0         0    2014
    + 6 Chaffee    NA        NA        NA        NA    2017
    + 7 Teller      0         0         0         0    2016
    + 8 Boulder     3.51      1.77      5.83     12    2017
    + 9 Fremont    NA        NA        NA        NA    2016
    +10 Kiowa      NA        NA        NA        NA    2020
    +# ℹ 13 more rows

    Be careful with column names and filter

    -
    filter(ER_30, "county" == "Denver") # this will not work
    +
    filter(er_30, "county" == "Denver") # this will not work
    -
    # A tibble: 0 × 7
    -# ℹ 7 variables: county <chr>, rate <dbl>, lower95cl <dbl>, upper95cl <dbl>,
    -#   visits <dbl>, year <dbl>, gender <chr>
    +
    # A tibble: 0 × 6
    +# ℹ 6 variables: county <chr>, rate <dbl>, lower95cl <dbl>, upper95cl <dbl>,
    +#   visits <dbl>, year <dbl>

    Be careful with column names and filter

    -
    filter(ER_30, county == "Denver")# this works!
    +
    filter(er_30, county == "Denver")# this works!
    -
    # A tibble: 3 × 7
    -  county  rate lower95cl upper95cl visits  year gender
    -  <chr>  <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr> 
    -1 Denver  3.76      1.97      6.12     13  2018 Female
    -2 Denver  8.51      5.30     11.7      30  2016 Male  
    -3 Denver  3.56      1.82      5.88     13  2017 Female
    +
    # A tibble: 1 × 6
    +  county  rate lower95cl upper95cl visits  year
    +  <chr>  <dbl>     <dbl>     <dbl>  <dbl> <dbl>
    +1 Denver  2.95      1.75      4.46     19  2013

    filter() is tricky

    @@ -4225,88 +1127,82 @@

    Get the data

    #library(dasehr)
    -#ER <- CO_heat_ER_bygender
    +#ER <- CO_heat_ER
     # or this:
    -#read_csv("https://daseh.org/data/Colorado_ER_heat_visits_by_county_gender.csv")
    +#read_csv("https://daseh.org/data/Colorado_ER_heat_visits.csv")
     set.seed(1234)
    -ER_30 <-slice_sample(ER, n = 30)
    +er_30 <-slice_sample(er, n = 30)

    Combining filter and select

    You can combine filter and select to subset the rows and columns, respectively, of a data frame:

    -
    select(filter(ER_30, year > 2012), county)
    - -
    # A tibble: 24 × 1
    -   county   
    -   <chr>    
    - 1 Arapahoe 
    - 2 Denver   
    - 3 Larimer  
    - 4 El Paso  
    - 5 Weld     
    - 6 El Paso  
    - 7 Jefferson
    - 8 Larimer  
    - 9 Jefferson
    -10 Jefferson
    -# ℹ 14 more rows
    +
    select(filter(er_30, year > 2012), county)
    + +
    # A tibble: 25 × 1
    +   county    
    +   <chr>     
    + 1 Garfield  
    + 2 Chaffee   
    + 3 Pueblo    
    + 4 Rio Grande
    + 5 Lake      
    + 6 Chaffee   
    + 7 Teller    
    + 8 Boulder   
    + 9 Fremont   
    +10 Kiowa     
    +# ℹ 15 more rows

    Nesting

    In R, the common way to perform multiple operations is to wrap functions around each other in a “nested” form.

    -
    head(select(ER_30, year, county), 2)
    +
    head(select(er_30, year, county), 2)
    # A tibble: 2 × 2
        year county  
       <dbl> <chr>   
    -1  2014 Arapahoe
    -2  2018 Denver  
    +1 2018 Garfield +2 2015 Chaffee

    Nesting can get confusing looking

    -
    select(filter(ER_30, year > 2000 & county == "Denver"), year, rate)
    +
    select(filter(er_30, year > 2000 & county == "Denver"), year, rate)
    -
    # A tibble: 3 × 2
    +
    # A tibble: 1 × 2
        year  rate
       <dbl> <dbl>
    -1  2018  3.76
    -2  2016  8.51
    -3  2017  3.56
    +1 2013 2.95

    Assigning Temporary Objects

    One can also create temporary objects and reassign them:

    -
    ER_30_CO <- filter(ER_30, year > 2000 & county == "Denver")
    -ER_30_CO <- select(ER_30_CO, year, rate)
    +
    er_30_Den <- filter(er_30, year > 2000 & county == "Denver")
    +er_30_Den <- select(er_30_Den, year, rate)
     
    -head(ER_30_CO)
    +head(er_30_Den)
    -
    # A tibble: 3 × 2
    +
    # A tibble: 1 × 2
        year  rate
       <dbl> <dbl>
    -1  2018  3.76
    -2  2016  8.51
    -3  2017  3.56
    +1 2013 2.95

    Using the pipe (comes with dplyr):

    -

    The pipe %>% makes this much more readable. It reads left side “pipes” into right side. RStudio CMD/Ctrl + Shift + M shortcut. Pipe tb into filter, then pipe that into select:

    +

    The pipe %>% makes this much more readable. It reads left side “pipes” into right side. RStudio CMD/Ctrl + Shift + M shortcut. Pipe er_30 into filter, then pipe that into select:

    -
    ER_30 %>% filter(year > 2000 & county == "Denver") %>% select(year, rate)
    +
    er_30 %>% filter(year > 2000 & county == "Denver") %>% select(year, rate)
    -
    # A tibble: 3 × 2
    +
    # A tibble: 1 × 2
        year  rate
       <dbl> <dbl>
    -1  2018  3.76
    -2  2016  8.51
    -3  2017  3.56
    +1 2013 2.95

    Adding/Removing Columns

    -

    Adding columns to a data frame: dplyr (tidyverse way)

    +

    Adding columns to a data frame: dplyr (tidyverse way)

    The mutate function in dplyr allows you to add or modify columns of a data frame.

    @@ -4315,18 +1211,18 @@

    {data object to update} <- mutate({data to use}, {new variable name} = {new variable source})
    -
    ER_30 <- mutate(ER_30, newcol = rate * 2)
    -head(ER_30, 4)
    +
    er_30 <- mutate(er_30, newcol = rate * 2)
    +head(er_30, 4)
    -
    # A tibble: 4 × 8
    -  county    rate lower95cl upper95cl visits  year gender newcol
    -  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr>   <dbl>
    -1 Arapahoe NA        NA        NA        NA  2014 Female  NA   
    -2 Denver    3.76      1.97      6.12     13  2018 Female   7.53
    -3 Larimer   8.13      4.32     13.1      14  2016 Female  16.3 
    -4 El Paso   3.94      2.01      6.52     12  2015 Female   7.89
    +
    # A tibble: 4 × 7
    +  county      rate lower95cl upper95cl visits  year newcol
    +  <chr>      <dbl>     <dbl>     <dbl>  <dbl> <dbl>  <dbl>
    +1 Garfield    NA       NA         NA       NA  2018   NA  
    +2 Chaffee     NA       NA         NA       NA  2015   NA  
    +3 Pueblo      15.9      9.54      22.2     26  2021   31.8
    +4 Rio Grande   0        0          0        0  2019    0  
    -

    Use mutate to modify existing columns

    +

    Use mutate to modify existing columns

    The mutate function in dplyr allows you to add or modify columns of a data frame.

    @@ -4335,75 +1231,75 @@

    {data object to update} <- mutate({data to use}, {variable name to change} = {variable modification}) -
    ER_30 <- mutate(ER_30, newcol = newcol / 2)
    -head(ER_30, 4)
    +
    er_30 <- mutate(er_30, newcol = newcol / 2)
    +head(er_30, 4)
    -
    # A tibble: 4 × 8
    -  county    rate lower95cl upper95cl visits  year gender newcol
    -  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr>   <dbl>
    -1 Arapahoe NA        NA        NA        NA  2014 Female  NA   
    -2 Denver    3.76      1.97      6.12     13  2018 Female   3.76
    -3 Larimer   8.13      4.32     13.1      14  2016 Female   8.13
    -4 El Paso   3.94      2.01      6.52     12  2015 Female   3.94
    +
    # A tibble: 4 × 7
    +  county      rate lower95cl upper95cl visits  year newcol
    +  <chr>      <dbl>     <dbl>     <dbl>  <dbl> <dbl>  <dbl>
    +1 Garfield    NA       NA         NA       NA  2018   NA  
    +2 Chaffee     NA       NA         NA       NA  2015   NA  
    +3 Pueblo      15.9      9.54      22.2     26  2021   15.9
    +4 Rio Grande   0        0          0        0  2019    0  
    -

    You can pipe data into mutate

    +

    You can pipe data into mutate

    -
    ER_30 <- ER_30 %>% mutate(newcol = newcol / 2)
    -head(ER_30,4)
    +
    er_30 <- er_30 %>% mutate(newcol = newcol / 2)
    +head(er_30,4)
    -
    # A tibble: 4 × 8
    -  county    rate lower95cl upper95cl visits  year gender newcol
    -  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr>   <dbl>
    -1 Arapahoe NA        NA        NA        NA  2014 Female  NA   
    -2 Denver    3.76      1.97      6.12     13  2018 Female   1.88
    -3 Larimer   8.13      4.32     13.1      14  2016 Female   4.07
    -4 El Paso   3.94      2.01      6.52     12  2015 Female   1.97
    +
    # A tibble: 4 × 7
    +  county      rate lower95cl upper95cl visits  year newcol
    +  <chr>      <dbl>     <dbl>     <dbl>  <dbl> <dbl>  <dbl>
    +1 Garfield    NA       NA         NA       NA  2018  NA   
    +2 Chaffee     NA       NA         NA       NA  2015  NA   
    +3 Pueblo      15.9      9.54      22.2     26  2021   7.95
    +4 Rio Grande   0        0          0        0  2019   0   

    mutate function

    Removing columns of a data frame: dplyr

    +

    Removing columns of a data frame: dplyr

    The NULL method is still very common.

    The select function can remove a column with exclamation mark (!) our using the minus sign (-):

    -
    select(ER_30, !newcol)
    +
    select(er_30, !newcol)
    -
    # A tibble: 6 × 7
    -  county    rate lower95cl upper95cl visits  year gender
    -  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr> 
    -1 Arapahoe NA        NA        NA        NA  2014 Female
    -2 Denver    3.76      1.97      6.12     13  2018 Female
    -3 Larimer   8.13      4.32     13.1      14  2016 Female
    -4 El Paso   3.94      2.01      6.52     12  2015 Female
    -5 Weld     11.1       6.59     16.9      18  2018 Male  
    -6 El Paso   5.99      3.32      8.66     20  2013 Male  
    +
    # A tibble: 6 × 6
    +  county      rate lower95cl upper95cl visits  year
    +  <chr>      <dbl>     <dbl>     <dbl>  <dbl> <dbl>
    +1 Garfield    NA       NA         NA       NA  2018
    +2 Chaffee     NA       NA         NA       NA  2015
    +3 Pueblo      15.9      9.54      22.2     26  2021
    +4 Rio Grande   0        0          0        0  2019
    +5 Lake         0        0          0        0  2014
    +6 Chaffee     NA       NA         NA       NA  2012

    Or, you can simply select the columns you want to keep, ignoring the ones you want to remove.

    -

    Removing columns in a data frame: dplyr

    +

    Removing columns in a data frame: dplyr

    You can use c() to list the columns to remove.

    Remove newcol and year:

    -
    select(ER_30, !c(newcol, year))
    +
    select(er_30, !c(newcol, year))
    -
    # A tibble: 30 × 6
    -   county     rate lower95cl upper95cl visits gender
    -   <chr>     <dbl>     <dbl>     <dbl>  <dbl> <chr> 
    - 1 Arapahoe  NA        NA        NA        NA Female
    - 2 Denver     3.76      1.97      6.12     13 Female
    - 3 Larimer    8.13      4.32     13.1      14 Female
    - 4 El Paso    3.94      2.01      6.52     12 Female
    - 5 Weld      11.1       6.59     16.9      18 Male  
    - 6 El Paso    5.99      3.32      8.66     20 Male  
    - 7 Jefferson  4.12      2.02      6.94     11 Male  
    - 8 Jefferson  5.76      3.12      9.20     14 Male  
    - 9 Larimer   NA        NA        NA        NA Male  
    -10 Jefferson  5.01      2.71      7.99     14 Male  
    +
    # A tibble: 30 × 5
    +   county      rate lower95cl upper95cl visits
    +   <chr>      <dbl>     <dbl>     <dbl>  <dbl>
    + 1 Garfield    NA       NA         NA       NA
    + 2 Chaffee     NA       NA         NA       NA
    + 3 Pueblo      15.9      9.54      22.2     26
    + 4 Rio Grande   0        0          0        0
    + 5 Lake         0        0          0        0
    + 6 Chaffee     NA       NA         NA       NA
    + 7 Chaffee     NA       NA         NA       NA
    + 8 Teller       0        0          0        0
    + 9 Prowers     NA       NA         NA       NA
    +10 Hinsdale     0        0          0        0
     # ℹ 20 more rows

    Ordering columns

    @@ -4412,95 +1308,95 @@

    The select function can reorder columns.

    -
    head(ER_30, 2)
    +
    head(er_30, 2)
    -
    # A tibble: 2 × 8
    -  county    rate lower95cl upper95cl visits  year gender newcol
    -  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr>   <dbl>
    -1 Arapahoe NA        NA        NA        NA  2014 Female  NA   
    -2 Denver    3.76      1.97      6.12     13  2018 Female   1.88
    +
    # A tibble: 2 × 7
    +  county    rate lower95cl upper95cl visits  year newcol
    +  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl>  <dbl>
    +1 Garfield    NA        NA        NA     NA  2018     NA
    +2 Chaffee     NA        NA        NA     NA  2015     NA
    -
    ER_30 %>% select(year, rate, county) %>%
    +
    er_30 %>% select(year, rate, county) %>%
     head(2)
    # A tibble: 2 × 3
        year  rate county  
       <dbl> <dbl> <chr>   
    -1  2014 NA    Arapahoe
    -2  2018  3.76 Denver  
    +1 2018 NA Garfield +2 2015 NA Chaffee
    -

    Ordering the columns of a data frame: dplyr

    +

    Ordering the columns of a data frame: dplyr

    The select function can reorder columns. Put newcol first, then select the rest of columns:

    -
    select(ER_30, newcol, everything())
    +
    select(er_30, newcol, everything())
    -
    # A tibble: 3 × 8
    -  newcol county    rate lower95cl upper95cl visits  year gender
    -   <dbl> <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr> 
    -1  NA    Arapahoe NA        NA        NA        NA  2014 Female
    -2   1.88 Denver    3.76      1.97      6.12     13  2018 Female
    -3   4.07 Larimer   8.13      4.32     13.1      14  2016 Female
    +
    # A tibble: 3 × 7
    +  newcol county    rate lower95cl upper95cl visits  year
    +   <dbl> <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl>
    +1  NA    Garfield  NA       NA         NA       NA  2018
    +2  NA    Chaffee   NA       NA         NA       NA  2015
    +3   7.95 Pueblo    15.9      9.54      22.2     26  2021
    -

    Ordering the columns of a data frame: dplyr

    +

    Ordering the columns of a data frame: dplyr

    Put year at the end (“remove, everything, then add back in”):

    -
    select(ER_30, !year, everything(), year)
    +
    select(er_30, !year, everything(), year)
    -
    # A tibble: 3 × 8
    -  county    rate lower95cl upper95cl visits gender newcol  year
    -  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <chr>   <dbl> <dbl>
    -1 Arapahoe NA        NA        NA        NA Female  NA     2014
    -2 Denver    3.76      1.97      6.12     13 Female   1.88  2018
    -3 Larimer   8.13      4.32     13.1      14 Female   4.07  2016
    +
    # A tibble: 3 × 7
    +  county    rate lower95cl upper95cl visits newcol  year
    +  <chr>    <dbl>     <dbl>     <dbl>  <dbl>  <dbl> <dbl>
    +1 Garfield  NA       NA         NA       NA  NA     2018
    +2 Chaffee   NA       NA         NA       NA  NA     2015
    +3 Pueblo    15.9      9.54      22.2     26   7.95  2021
    -

    Ordering the column names of a data frame: alphabetically

    +

    Ordering the column names of a data frame: alphabetically

    Using the base R order() function.

    -
    order(colnames(ER_30))
    - -
    [1] 1 7 3 8 2 4 5 6
    - -
    ER_30 %>% select(order(colnames(ER_30)))
    - -
    # A tibble: 30 × 8
    -   county    gender lower95cl newcol  rate upper95cl visits  year
    -   <chr>     <chr>      <dbl>  <dbl> <dbl>     <dbl>  <dbl> <dbl>
    - 1 Arapahoe  Female     NA     NA    NA        NA        NA  2014
    - 2 Denver    Female      1.97   1.88  3.76      6.12     13  2018
    - 3 Larimer   Female      4.32   4.07  8.13     13.1      14  2016
    - 4 El Paso   Female      2.01   1.97  3.94      6.52     12  2015
    - 5 Weld      Male        6.59   5.57 11.1      16.9      18  2018
    - 6 El Paso   Male        3.32   3.00  5.99      8.66     20  2013
    - 7 Jefferson Male        2.02   2.06  4.12      6.94     11  2015
    - 8 Jefferson Male        3.12   2.88  5.76      9.20     14  2011
    - 9 Larimer   Male       NA     NA    NA        NA        NA  2020
    -10 Jefferson Male        2.71   2.50  5.01      7.99     14  2022
    +
    order(colnames(er_30))
    + +
    [1] 1 3 7 2 4 5 6
    + +
    er_30 %>% select(order(colnames(er_30)))
    + +
    # A tibble: 30 × 7
    +   county     lower95cl newcol  rate upper95cl visits  year
    +   <chr>          <dbl>  <dbl> <dbl>     <dbl>  <dbl> <dbl>
    + 1 Garfield       NA     NA     NA        NA       NA  2018
    + 2 Chaffee        NA     NA     NA        NA       NA  2015
    + 3 Pueblo          9.54   7.95  15.9      22.2     26  2021
    + 4 Rio Grande      0      0      0         0        0  2019
    + 5 Lake            0      0      0         0        0  2014
    + 6 Chaffee        NA     NA     NA        NA       NA  2012
    + 7 Chaffee        NA     NA     NA        NA       NA  2017
    + 8 Teller          0      0      0         0        0  2016
    + 9 Prowers        NA     NA     NA        NA       NA  2012
    +10 Hinsdale        0      0      0         0        0  2012
     # ℹ 20 more rows
    -

    Ordering the columns of a data frame: dplyr

    +

    Ordering the columns of a data frame: dplyr

    In addition to select we can also use the relocate() function of dplyr to rearrange the columns for more complicated moves.

    -

    For example, let say we just wanted year to be before BUYER_STATE.

    +

    For example, let say we just wanted year to be before `rate``.

    -
    head(ER_30, 1)
    +
    head(er_30, 1)
    -
    # A tibble: 1 × 8
    -  county    rate lower95cl upper95cl visits  year gender newcol
    -  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr>   <dbl>
    -1 Arapahoe    NA        NA        NA     NA  2014 Female     NA
    +
    # A tibble: 1 × 7
    +  county    rate lower95cl upper95cl visits  year newcol
    +  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl>  <dbl>
    +1 Garfield    NA        NA        NA     NA  2018     NA
    -
    tb_carb <- relocate(ER_30, year, .before = rate)
    +
    er_year_fix <- relocate(er_30, year, .before = rate)
     
    -head(tb_carb, 1)
    +head(er_year_fix, 1)
    -
    # A tibble: 1 × 8
    -  county    year  rate lower95cl upper95cl visits gender newcol
    -  <chr>    <dbl> <dbl>     <dbl>     <dbl>  <dbl> <chr>   <dbl>
    -1 Arapahoe  2014    NA        NA        NA     NA Female     NA
    +
    # A tibble: 1 × 7
    +  county    year  rate lower95cl upper95cl visits newcol
    +  <chr>    <dbl> <dbl>     <dbl>     <dbl>  <dbl>  <dbl>
    +1 Garfield  2018    NA        NA        NA     NA     NA

    Ordering rows

    @@ -4508,63 +1404,63 @@

    The arrange function can reorder rows By default, arrange orders in increasing order:

    -
    arrange(ER_30, year)
    - -
    # A tibble: 30 × 8
    -   county     rate lower95cl upper95cl visits  year gender newcol
    -   <chr>     <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr>   <dbl>
    - 1 Jefferson  5.76      3.12      9.20     14  2011 Male     2.88
    - 2 El Paso   NA        NA        NA        NA  2012 Female  NA   
    - 3 Statewide  7.56      6.48      8.65    193  2012 Male     3.78
    - 4 Adams      5.48      2.90      8.88     13  2012 Male     2.74
    - 5 Cheyenne   0         0         0         0  2012 Male     0   
    - 6 Weld      12.8       6.96     20.3      15  2012 Male     6.38
    - 7 El Paso    5.99      3.32      8.66     20  2013 Male     3.00
    - 8 Weld      NA        NA        NA        NA  2013 Female  NA   
    - 9 Statewide  4.94      4.06      5.82    124  2013 Female   2.47
    -10 Arapahoe  NA        NA        NA        NA  2014 Female  NA   
    +
    arrange(er_30, year)
    + +
    # A tibble: 30 × 7
    +   county      rate lower95cl upper95cl visits  year newcol
    +   <chr>      <dbl>     <dbl>     <dbl>  <dbl> <dbl>  <dbl>
    + 1 Saguache    0         0         0         0  2011   0   
    + 2 Chaffee    NA        NA        NA        NA  2012  NA   
    + 3 Prowers    NA        NA        NA        NA  2012  NA   
    + 4 Hinsdale    0         0         0         0  2012   0   
    + 5 Phillips    0         0         0         0  2012   0   
    + 6 Denver      2.95      1.75      4.46     19  2013   1.47
    + 7 San Miguel NA        NA        NA        NA  2013  NA   
    + 8 Lake        0         0         0         0  2014   0   
    + 9 Delta       0         0         0         0  2014   0   
    +10 Adams       3.08      1.71      4.85     15  2014   1.54
     # ℹ 20 more rows

    Ordering the rows of a data frame: dplyr

    Use the desc to arrange the rows in descending order:

    -
    arrange(ER_30, desc(year))
    - -
    # A tibble: 30 × 8
    -   county     rate lower95cl upper95cl visits  year gender newcol
    -   <chr>     <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr>   <dbl>
    - 1 Jefferson  5.01      2.71      7.99     14  2022 Male     2.50
    - 2 Jefferson  4.56      2.43      7.35     14  2022 Female   2.28
    - 3 Larimer   NA        NA        NA        NA  2020 Male    NA   
    - 4 Statewide  6.60      5.66      7.53    197  2020 Male     3.30
    - 5 Cheyenne   0         0         0         0  2020 Male     0   
    - 6 Denver     3.76      1.97      6.12     13  2018 Female   1.88
    - 7 Weld      11.1       6.59     16.9      18  2018 Male     5.57
    - 8 El Paso    9.07      5.74     12.4      31  2018 Male     4.54
    - 9 Statewide 10.1       8.95     11.3     293  2018 Male     5.06
    -10 El Paso   NA        NA        NA        NA  2017 Female  NA   
    +
    arrange(er_30, desc(year))
    + +
    # A tibble: 30 × 7
    +   county      rate lower95cl upper95cl visits  year newcol
    +   <chr>      <dbl>     <dbl>     <dbl>  <dbl> <dbl>  <dbl>
    + 1 Otero       NA       NA         NA       NA  2022  NA   
    + 2 Chaffee      0        0          0        0  2022   0   
    + 3 Pueblo      15.9      9.54      22.2     26  2021   7.95
    + 4 Mesa        12.0      7.12      18.2     19  2021   6.01
    + 5 Kiowa       NA       NA         NA       NA  2020  NA   
    + 6 Park        NA       NA         NA       NA  2020  NA   
    + 7 Rio Blanco  NA       NA         NA       NA  2020  NA   
    + 8 Rio Grande   0        0          0        0  2019   0   
    + 9 Garfield    NA       NA         NA       NA  2018  NA   
    +10 Dolores      0        0          0        0  2018   0   
     # ℹ 20 more rows

    Ordering the rows of a data frame: dplyr

    You can combine increasing and decreasing orderings:

    -
    arrange(ER_30, rate, desc(year)) %>% head(n = 2)
    +
    arrange(er_30, rate, desc(year)) %>% head(n = 2)
    -
    # A tibble: 2 × 8
    -  county    rate lower95cl upper95cl visits  year gender newcol
    -  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr>   <dbl>
    -1 Cheyenne     0         0         0      0  2020 Male        0
    -2 Cheyenne     0         0         0      0  2012 Male        0
    +
    # A tibble: 2 × 7
    +  county      rate lower95cl upper95cl visits  year newcol
    +  <chr>      <dbl>     <dbl>     <dbl>  <dbl> <dbl>  <dbl>
    +1 Chaffee        0         0         0      0  2022      0
    +2 Rio Grande     0         0         0      0  2019      0
    -
    arrange(ER_30, desc(year), rate) %>% head(n = 2)
    +
    arrange(er_30, desc(year), rate) %>% head(n = 2)
    -
    # A tibble: 2 × 8
    -  county     rate lower95cl upper95cl visits  year gender newcol
    -  <chr>     <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr>   <dbl>
    -1 Jefferson  4.56      2.43      7.35     14  2022 Female   2.28
    -2 Jefferson  5.01      2.71      7.99     14  2022 Male     2.50
    +
    # A tibble: 2 × 7
    +  county   rate lower95cl upper95cl visits  year newcol
    +  <chr>   <dbl>     <dbl>     <dbl>  <dbl> <dbl>  <dbl>
    +1 Chaffee     0         0         0      0  2022      0
    +2 Otero      NA        NA        NA     NA  2022     NA

    Summary

    @@ -4599,16 +1495,16 @@

    {data object to update} <- mutate({data to use}, {new variable name} = {new variable source}) -
    ER_30 <- mutate(ER_30, newcol = count/2.2)
    +
    er_30 <- mutate(ER_30, newcol = count/2.2)

    A note about base R:

    The $ operator is similar to pull(). This is the base R way to do this:

    -
    ER_30$year
    +
    er_30$year
    -
     [1] 2014 2018 2016 2015 2018 2013 2015 2011 2020 2022 2022 2012 2017 2020 2016
    -[16] 2020 2017 2012 2018 2012 2016 2014 2012 2014 2012 2015 2014 2018 2013 2013
    +
     [1] 2018 2015 2021 2019 2014 2012 2017 2016 2012 2012 2017 2016 2020 2014 2020
    +[16] 2014 2011 2022 2018 2013 2017 2021 2015 2020 2012 2016 2013 2014 2017 2022

    Although it is easier (for this one task), mixing and matching the $ operator with tidyverse functions usually doesn’t work. Therefore, we want to let you know about it in case you see it, but we suggest that you try working with the tidyverse way.

    @@ -4618,15 +1514,15 @@

    Just want you to be aware of this as it is very common.

    -
    ER_30$newcol <- ER_30$rate/2.2
    -head(ER_30,3)
    +
    er_30$newcol <- er_30$rate/2.2
    +head(er_30,3)
    -
    # A tibble: 3 × 8
    -  county    rate lower95cl upper95cl visits  year gender newcol
    -  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr>   <dbl>
    -1 Arapahoe NA        NA        NA        NA  2014 Female  NA   
    -2 Denver    3.76      1.97      6.12     13  2018 Female   1.71
    -3 Larimer   8.13      4.32     13.1      14  2016 Female   3.70
    +
    # A tibble: 3 × 7
    +  county    rate lower95cl upper95cl visits  year newcol
    +  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl>  <dbl>
    +1 Garfield  NA       NA         NA       NA  2018  NA   
    +2 Chaffee   NA       NA         NA       NA  2015  NA   
    +3 Pueblo    15.9      9.54      22.2     26  2021   7.22

    Even though $ is easier for creating new columns, mutate is really powerful, so it’s worth getting used to.

    @@ -4646,23 +1542,23 @@

    Instead of removing rows like filter, which() simply shows where the values occur if they pass a specific condition. We will see that this can be helpful later when we want to select and filter in more complicated ways.

    -
    which(select(ER_30, year) == 2014)
    +
    which(select(er_30, year) == 2012)
    -
    [1]  1 22 24 27
    +
    [1]  6  9 10 25
    -
    select(ER_30, year) == 2014 %>% head(10)
    +
    select(er_30, year) == 2012 %>% head(10)
           year
    - [1,]  TRUE
    + [1,] FALSE
      [2,] FALSE
      [3,] FALSE
      [4,] FALSE
      [5,] FALSE
    - [6,] FALSE
    + [6,]  TRUE
      [7,] FALSE
      [8,] FALSE
    - [9,] FALSE
    -[10,] FALSE
    + [9,]  TRUE
    +[10,]  TRUE
     [11,] FALSE
     [12,] FALSE
     [13,] FALSE
    @@ -4674,53 +1570,53 @@ 

    [19,] FALSE [20,] FALSE [21,] FALSE -[22,] TRUE +[22,] FALSE [23,] FALSE -[24,] TRUE -[25,] FALSE +[24,] FALSE +[25,] TRUE [26,] FALSE -[27,] TRUE +[27,] FALSE [28,] FALSE [29,] FALSE [30,] FALSE

    Remove a column in base R

    -
    ER_30$year <- NULL
    +
    er_30$year <- NULL

    Renaming Columns of a data frame: base R

    We can use the colnames function to extract and/or directly reassign column names of df:

    -
    colnames(ER_30) # just prints
    +
    colnames(er_30) # just prints
    [1] "county"    "rate"      "lower95cl" "upper95cl" "visits"    "year"     
    -[7] "gender"    "newcol"   
    +[7] "newcol" -
    colnames(ER_30)[1:2] <- c("County", "Rate") # reassigns
    -head(ER_30)
    +
    colnames(er_30)[1:2] <- c("County", "Rate") # reassigns
    +head(er_30)
    -
    # A tibble: 6 × 8
    -  County    Rate lower95cl upper95cl visits  year gender newcol
    -  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr>   <dbl>
    -1 Arapahoe NA        NA        NA        NA  2014 Female  NA   
    -2 Denver    3.76      1.97      6.12     13  2018 Female   1.71
    -3 Larimer   8.13      4.32     13.1      14  2016 Female   3.70
    -4 El Paso   3.94      2.01      6.52     12  2015 Female   1.79
    -5 Weld     11.1       6.59     16.9      18  2018 Male     5.07
    -6 El Paso   5.99      3.32      8.66     20  2013 Male     2.72
    +
    # A tibble: 6 × 7
    +  County      Rate lower95cl upper95cl visits  year newcol
    +  <chr>      <dbl>     <dbl>     <dbl>  <dbl> <dbl>  <dbl>
    +1 Garfield    NA       NA         NA       NA  2018  NA   
    +2 Chaffee     NA       NA         NA       NA  2015  NA   
    +3 Pueblo      15.9      9.54      22.2     26  2021   7.22
    +4 Rio Grande   0        0          0        0  2019   0   
    +5 Lake         0        0          0        0  2014   0   
    +6 Chaffee     NA       NA         NA       NA  2012  NA   

    Subset rows of a data frame with indices:

    Let’s select rows 1 and 3 from df using brackets:

    -
    ER_30[ c(1, 3), ]
    +
    er_30[ c(1, 3), ]
    -
    # A tibble: 2 × 8
    -  County    Rate lower95cl upper95cl visits  year gender newcol
    -  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr>   <dbl>
    -1 Arapahoe NA        NA         NA       NA  2014 Female  NA   
    -2 Larimer   8.13      4.32      13.1     14  2016 Female   3.70
    +
    # A tibble: 2 × 7
    +  County    Rate lower95cl upper95cl visits  year newcol
    +  <chr>    <dbl>     <dbl>     <dbl>  <dbl> <dbl>  <dbl>
    +1 Garfield  NA       NA         NA       NA  2018  NA   
    +2 Pueblo    15.9      9.54      22.2     26  2021   7.22

    Subset columns of a data frame:

    @@ -4728,59 +1624,59 @@

    For data frames and matrices (2-dimensional objects), the brackets are [rows, columns] subsetting. We can grab the x column using the index of the column or the column name (“year”)

    -
    ER_30[, 6]
    +
    er_30[, 6]
    # A tibble: 30 × 1
         year
        <dbl>
    - 1  2014
    - 2  2018
    - 3  2016
    - 4  2015
    - 5  2018
    - 6  2013
    - 7  2015
    - 8  2011
    - 9  2020
    -10  2022
    + 1  2018
    + 2  2015
    + 3  2021
    + 4  2019
    + 5  2014
    + 6  2012
    + 7  2017
    + 8  2016
    + 9  2012
    +10  2012
     # ℹ 20 more rows
    -
    ER_30[, "year"]
    +
    er_30[, "year"]
    # A tibble: 30 × 1
         year
        <dbl>
    - 1  2014
    - 2  2018
    - 3  2016
    - 4  2015
    - 5  2018
    - 6  2013
    - 7  2015
    - 8  2011
    - 9  2020
    -10  2022
    + 1  2018
    + 2  2015
    + 3  2021
    + 4  2019
    + 5  2014
    + 6  2012
    + 7  2017
    + 8  2016
    + 9  2012
    +10  2012
     # ℹ 20 more rows

    Subset columns of a data frame:

    We can select multiple columns using multiple column names:

    -
    ER_30[, c("County", "Rate")]
    +
    er_30[, c("County", "Rate")]
    # A tibble: 30 × 2
    -   County     Rate
    -   <chr>     <dbl>
    - 1 Arapahoe  NA   
    - 2 Denver     3.76
    - 3 Larimer    8.13
    - 4 El Paso    3.94
    - 5 Weld      11.1 
    - 6 El Paso    5.99
    - 7 Jefferson  4.12
    - 8 Jefferson  5.76
    - 9 Larimer   NA   
    -10 Jefferson  5.01
    +   County      Rate
    +   <chr>      <dbl>
    + 1 Garfield    NA  
    + 2 Chaffee     NA  
    + 3 Pueblo      15.9
    + 4 Rio Grande   0  
    + 5 Lake         0  
    + 6 Chaffee     NA  
    + 7 Chaffee     NA  
    + 8 Teller       0  
    + 9 Prowers     NA  
    +10 Hinsdale     0  
     # ℹ 20 more rows
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Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see . The way you can create a file like this in RStudio is: File → New File → R Markdown and then using the default or using a template. -1. Try compiling this document using the “Knit HTML” button. What files are produced? +1. Try compiling this document using the “Knit” button. What files are produced? ### The Code From 46fd48a15c527f3e2b27492208bb947ec3bcad1c Mon Sep 17 00:00:00 2001 From: Carrie Wright <23014755+carriewright11@users.noreply.github.com> Date: Mon, 30 Sep 2024 10:59:33 -0400 Subject: [PATCH 21/26] Update modules/RStudio/lab/RStudio_Lab_Key.Rmd --- modules/RStudio/lab/RStudio_Lab_Key.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/RStudio/lab/RStudio_Lab_Key.Rmd b/modules/RStudio/lab/RStudio_Lab_Key.Rmd index 440c570a..a7dc03c3 100644 --- a/modules/RStudio/lab/RStudio_Lab_Key.Rmd +++ b/modules/RStudio/lab/RStudio_Lab_Key.Rmd @@ -9,7 +9,7 @@ editor_options: The file you are reading is called an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see . The way you can create a file like this in RStudio is: File → New File → R Markdown and then using the default or using a template. -1. Try compiling this document using the “Knit HTML” button. What files are produced? +1. Try compiling this document using the “Knit” button. What files are produced? ### The Code From 670e3232d499edeed407caddce476c30533f4efe Mon Sep 17 00:00:00 2001 From: Carrie Wright <23014755+carriewright11@users.noreply.github.com> Date: Mon, 30 Sep 2024 11:04:32 -0400 Subject: [PATCH 22/26] Update modules/RStudio/lab/RStudio_Lab.Rmd --- modules/RStudio/lab/RStudio_Lab.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/RStudio/lab/RStudio_Lab.Rmd b/modules/RStudio/lab/RStudio_Lab.Rmd index 1f54e8df..b83fc6a0 100644 --- a/modules/RStudio/lab/RStudio_Lab.Rmd +++ b/modules/RStudio/lab/RStudio_Lab.Rmd @@ -26,7 +26,7 @@ er_2 <- er %>% filter(!is.na(rate)) ``` -When you click the **Knit** button (at the top of RStudio), a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. Note that when it does this it starts from scratch - it can't use any objects you created outside of the Rmd file. +When you click the **Knit** button (at the top of RStudio), a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. Note that when it does this it starts from scratch - it can't use any objects you created outside of the Rmd file. Therefore it essentially has its own environment that is different from the one that you see when working interactively in RStudio. ### Plotting some data From 02ed77f2da3e84b5ee3d60fcfeb4b45362a10e23 Mon Sep 17 00:00:00 2001 From: Carrie Wright <23014755+carriewright11@users.noreply.github.com> Date: Mon, 30 Sep 2024 11:04:43 -0400 Subject: [PATCH 23/26] Update modules/RStudio/lab/RStudio_Lab_Key.Rmd --- modules/RStudio/lab/RStudio_Lab_Key.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/RStudio/lab/RStudio_Lab_Key.Rmd b/modules/RStudio/lab/RStudio_Lab_Key.Rmd index a7dc03c3..f2bb88eb 100644 --- a/modules/RStudio/lab/RStudio_Lab_Key.Rmd +++ b/modules/RStudio/lab/RStudio_Lab_Key.Rmd @@ -26,7 +26,7 @@ er_2 <- er %>% filter(!is.na(rate)) ``` -When you click the **Knit** button (at the top of RStudio), a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. Note that when it does this it starts from scratch - it can't use any objects you created outside of the Rmd file. +When you click the **Knit** button (at the top of RStudio), a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. Note that when it does this it starts from scratch - it can't use any objects you created outside of the Rmd file. Therefore it essentially has its own environment that is different from the one that you see when working interactively in RStudio. ### Plotting some data From 4d9a523e324954613ae0f58ee6b9c18360c0e18a Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Mon, 30 Sep 2024 15:12:32 +0000 Subject: [PATCH 24/26] Render site --- help.html | 8 +- modules/RStudio/RStudio.html | 44 +- modules/RStudio/lab/RStudio_Lab.html | 43 +- modules/RStudio/lab/RStudio_Lab_Key.html | 59 +- modules/Reproducibility/Reproducibility.html | 3258 +----------------- 5 files changed, 207 insertions(+), 3205 deletions(-) diff --git a/help.html b/help.html index c1256943..872e60ed 100644 --- a/help.html +++ b/help.html @@ -350,14 +350,14 @@

    Why are my changes not taking effect? It’s making my results look

    Here we are creating a new object from an existing one:

    new_rivers <- sample(rivers, 5)
     new_rivers
    -
    ## [1]  630  230  338  500 1306
    +
    ## [1] 350 306 525 350 380

    Using just this will only print the result and not actually change new_rivers:

    new_rivers + 1
    -
    ## [1]  631  231  339  501 1307
    +
    ## [1] 351 307 526 351 381

    If we want to modify new_rivers and save that modified version, then we need to reassign new_rivers like so:

    new_rivers <- new_rivers + 1
     new_rivers
    -
    ## [1]  631  231  339  501 1307
    +
    ## [1] 351 307 526 351 381

    If we forget to reassign this can cause subsequent steps to not work as expected because we will not be working with the data that has been modified.


    @@ -406,7 +406,7 @@

    Error: object ‘X’ not found

    Make sure you run something like this, with the <- operator:

    rivers2 <- new_rivers + 1
     rivers2
    -
    ## [1]  632  232  340  502 1308
    +
    ## [1] 352 308 527 352 382

    diff --git a/modules/RStudio/RStudio.html b/modules/RStudio/RStudio.html index 03aa56bf..6e8928bf 100644 --- a/modules/RStudio/RStudio.html +++ b/modules/RStudio/RStudio.html @@ -392,32 +392,12 @@

    -

    Chunk settings

    - -

    - -

    Chunk settings

    - -

    You can specify if a chunk will be seen in the report or not.

    - -

    -

    Errors

    R studio can help you find issues in your code. Note that sometimes the error occurs earlier than RStudio thinks.

    -

    Rainbow Parentheses

    - -

    Tools –> Global Options –> Code –> Display –> Use rainbow parentheses

    - -

    Press enter to save this setting and get out of this menu.

    - -



    - -

    -

    Useful R Studio Shortcuts

      @@ -496,9 +476,31 @@

    Extra Slides

    +

    Chunk settings

    + +

    + +

    Chunk settings

    + +

    You can specify if a chunk will be seen in the report or not.

    + +

    +

    Sometimes you want to hide your code

    +

    If you want to keep your code so people can see it if they want to there is a nice option called code folding - check it out here: https://stackoverflow.com/questions/69326576/show-output-but-hide-code-when-sending-rmd-to-other-people

    + +

    Rainbow Parentheses

    + +

    Tools –> Global Options –> Code –> Display –> Use rainbow parentheses

    + +

    This can help you see your code more easily.

    + +

    Press enter to save this setting and get out of this menu.

    + +



    + +

    diff --git a/modules/RStudio/lab/RStudio_Lab.html b/modules/RStudio/lab/RStudio_Lab.html index 9e2c95e8..2708bd55 100644 --- a/modules/RStudio/lab/RStudio_Lab.html +++ b/modules/RStudio/lab/RStudio_Lab.html @@ -167,21 +167,31 @@

    Starting with R and RMarkdown

    R Markdown

    -

    Please read through everything and then try the exercises. This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com. The way you can create a file like this in RStudio is: File → New File → R Markdown and then using the default or using a template.

    +

    The file you are reading is called an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com. The way you can create a file like this in RStudio is: File → New File → R Markdown and then using the default or using a template.

    +
      +
    1. Try compiling this document using the “Knit” button. What files are produced?
    2. +

    The Code

    The gray area below is a code chunk that will set up our packages and data. You can also run the code within the editor area by pressing the green play button. Don’t worry right now about what the code is doing, we will cover this later. We just want you to get used to RStudio and RMarkdowns. This dataset is one we’ll be working with quite a lot in the lectures. It contains county-level data about ER visits for heat-related illnesses in Colorado for the years 2011-2022.

    +
      +
    1. Press the green play button. What happens (check the environment too!)?
    2. +
    library(tidyverse)
     
     er <- read_csv(file = "https://daseh.org/data/CO_ER_heat_visits.csv")
     # keep non-missing data
     er_2 <- er %>%
       filter(!is.na(rate))
    -

    When you click the Knit button (at the top of RStudio), a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. Next we will show you some code - no worries about understanding it yet, we just want to get you familiar with where code goes. When code is in an RMarkdown file chunk, it is saved to a file. When it is in the console, it is not saved. The console is useful for installing packages like we just did, this is because we only need to do it once, so we don’t usually need to save the code.

    +

    When you click the Knit button (at the top of RStudio), a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. Note that when it does this it starts from scratch - it can’t use any objects you created outside of the Rmd file. Therefore it essentially has its own environment that is different from the one that you see when working interactively in RStudio.

    Plotting some data

    -

    Here is code that will make a plot of some data.You can embed an R code chunk like this: Try pressing the green play button to see what happens. Make sure you have run the previous code chunk first by pressing the green play button in that chunk.

    +

    Here is code that will make a plot of some data.

    +
      +
    1. Try the “run all chunks above” button that is to the left of the play button on the following chunk. What happens?

    2. +
    3. Then press the play button on this same chunk. What happens?

    4. +
    # keep only some counties
     er_3 <- er_2 %>%
       filter(county %in% c("Arapahoe", "Denver", "Jefferson", "Larimer"))
    @@ -190,39 +200,38 @@ 

    Plotting some data

    Arapahoe = "blue", Denver = "darkgreen", Jefferson = "orange", - Larimer = "purple" + Larimer = "salmon" ) ggplot(aes(x = year, y = rate, colour = county, group = county), data = er_3) + geom_line() + scale_colour_manual(values = palette)
    -

    +

    -
    -
    -

    Exercise

    -

    Here are a few changes that will show you how to change small things in R code and the output it makes. After each change, hit the Knit button again.

    -
    -

    1.1

    -

    Go through the code for the plot above and change the colors in palette to something other than what they originally were. See http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf for a large list of colors. For example, you could replace blue with red.

    -
    -
    -

    1.2

    -

    Add a new R chunk after <!-- Your code chunk goes here -->. You can use the insert chunk button or copy paste an existing code chunk. Include a lowercase x inside the chunk as the code. Make sure you press the knit button after this to see what the new chunk looks like.

    +
    +

    Adding new chunks

    +
      +
    1. Add a new R chunk after <!-- Your code chunk goes here -->. You can use the insert chunk button or copy paste an existing code chunk. Include a lowercase x inside the chunk as the code. Make sure you press the knit button after this to see what the new chunk looks like.
    2. +
    x <- c(1, 2, 3)

    Practice on Your Own!

    +

    Here are a few changes that will show you how to change small things in R code and the output it makes. After each change, hit the Knit button again.

    P.1

    -

    Update the options of the R chunk you just made (with the lowercase x in it) so that the output option is show output only. How does the chunk look now?

    +

    Go through the code for the plot above and change the colors in palette to something other than what they originally were. See http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf for a large list of colors. For example, you could replace blue with red.

    P.2

    Create another R Markdown Document from RStudio dropdowns: File → New File → R Markdown.

    +
    +

    P.3

    +

    Add a new header with two hash symbols ## at the start of a line with some text. Knit the report to see how it looks.

    +
    diff --git a/modules/RStudio/lab/RStudio_Lab_Key.html b/modules/RStudio/lab/RStudio_Lab_Key.html index bc0fd1bf..c6270ea3 100644 --- a/modules/RStudio/lab/RStudio_Lab_Key.html +++ b/modules/RStudio/lab/RStudio_Lab_Key.html @@ -167,36 +167,31 @@

    Starting with R and RMarkdown - Key

    R Markdown

    -

    Please read through everything and then try the exercises. This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com. The way you can create a file like this in RStudio is: File → New File → R Markdown and then using the default or using a template.

    +

    The file you are reading is called an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com. The way you can create a file like this in RStudio is: File → New File → R Markdown and then using the default or using a template.

    +
      +
    1. Try compiling this document using the “Knit” button. What files are produced?
    2. +

    The Code

    The gray area below is a code chunk that will set up our packages and data. You can also run the code within the editor area by pressing the green play button. Don’t worry right now about what the code is doing, we will cover this later. We just want you to get used to RStudio and RMarkdowns. This dataset is one we’ll be working with quite a lot in the lectures. It contains county-level data about ER visits for heat-related illnesses in Colorado for the years 2011-2022.

    +
      +
    1. Press the green play button. What happens (check the environment too!)?
    2. +
    library(tidyverse)
     
     er <- read_csv(file = "https://daseh.org/data/CO_ER_heat_visits.csv")
     # keep non-missing data
     er_2 <- er %>%
       filter(!is.na(rate))
    -

    When you click the Knit button (at the top of RStudio), a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. Next we will show you some code - no worries about understanding it yet, we just want to get you familiar with where code goes. When code is in an RMarkdown file chunk, it is saved to a file. When it is in the console, it is not saved. The console is useful for installing packages like we just did, this is because we only need to do it once, so we don’t usually need to save the code.

    +

    When you click the Knit button (at the top of RStudio), a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. Note that when it does this it starts from scratch - it can’t use any objects you created outside of the Rmd file. Therefore it essentially has its own environment that is different from the one that you see when working interactively in RStudio.

    Plotting some data

    -

    Here is code that will make a plot of some data.You can embed an R code chunk like this: Try pressing the green play button to see what happens. Make sure you have run the previous code chunk first by pressing the green play button in that chunk.

    -
    # keep only some counties
    -er_3 <- er_2 %>%
    -  filter(county %in% c("Arapahoe", "Denver", "Jefferson", "Larimer"))
    -
    -palette <- c(
    -  Arapahoe = "blue",
    -  Denver = "darkgreen",
    -  Jefferson = "orange",
    -  Larimer = "purple"
    -)
    -
    -ggplot(aes(x = year, y = rate, colour = county, group = county), data = er_3) +
    -  geom_line() +
    -  scale_colour_manual(values = palette)
    -

    +

    Here is code that will make a plot of some data.

    +
      +
    1. Try the “run all chunks above” button that is to the left of the play button on the following chunk. What happens?

    2. +
    3. Then press the play button on this same chunk. What happens?

    4. +
    # keep only some counties
     er_3 <- er_2 %>%
       filter(county %in% c("Arapahoe", "Denver", "Jefferson", "Larimer"))
    @@ -213,31 +208,35 @@ 

    Plotting some data

    scale_colour_manual(values = palette)

    -
    -
    -

    Exercise

    -

    Here are a few changes that will show you how to change small things in R code and the output it makes. After each change, hit the Knit button again.

    -
    -

    1.1

    -

    Go through the code for the plot above and change the colors in palette to something other than what they originally were. See http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf for a large list of colors. For example, you could replace blue with red.

    -
    -
    -

    1.2

    -

    Add a new R chunk after <!-- Your code chunk goes here -->. You can use the insert chunk button or copy paste an existing code chunk. Include a lowercase x inside the chunk as the code. Make sure you press the knit button after this to see what the new chunk looks like.

    +
    +

    Adding new chunks

    +
      +
    1. Add a new R chunk after <!-- Your code chunk goes here -->. You can use the insert chunk button or copy paste an existing code chunk. Include a lowercase x inside the chunk as the code. Make sure you press the knit button after this to see what the new chunk looks like.
    2. +
    x <- c(1, 2, 3)
    +
    x 
    +
    ## [1] 1 2 3

    Practice on Your Own!

    +

    Here are a few changes that will show you how to change small things in R code and the output it makes. After each change, hit the Knit button again.

    P.1

    -

    Update the options of the R chunk you just made (with the lowercase x in it) so that the output option is show output only. How does the chunk look now?

    +

    Go through the code for the plot above and change the colors in palette to something other than what they originally were. See http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf for a large list of colors. For example, you could replace blue with red.

    P.2

    Create another R Markdown Document from RStudio dropdowns: File → New File → R Markdown.

    +
    +

    P.3

    +

    Add a new header with two hash symbols ## at the start of a line with some text. Knit the report to see how it looks.

    +
    +
    +

    New Header

    +
    diff --git a/modules/Reproducibility/Reproducibility.html b/modules/Reproducibility/Reproducibility.html index f753a86f..4fa97280 100644 --- a/modules/Reproducibility/Reproducibility.html +++ b/modules/Reproducibility/Reproducibility.html @@ -32,3154 +32,129 @@ }; - - - - - - - - - - - - - - + code span.al { color: #ff0000; font-weight: bold; } /* Alert */ + code span.an { color: #60a0b0; font-weight: bold; font-style: italic; } /* Annotation */ + code span.at { color: #7d9029; } /* Attribute */ + code span.bn { color: #40a070; } /* BaseN */ + code span.bu { } /* BuiltIn */ + code span.cf { color: #007020; font-weight: bold; } /* ControlFlow */ + code span.ch { color: #4070a0; } /* Char */ + code span.cn { color: #880000; } /* Constant */ + code span.co { color: #60a0b0; font-style: italic; } /* Comment */ + code span.cv { color: #60a0b0; font-weight: bold; font-style: italic; } /* CommentVar */ + code span.do { color: #ba2121; font-style: italic; } /* Documentation */ + code span.dt { color: #902000; } /* DataType */ + code span.dv { color: #40a070; } /* DecVal */ + code span.er { color: #ff0000; font-weight: bold; } /* Error */ + code span.ex { } /* Extension */ + code span.fl { color: #40a070; } /* Float */ + code span.fu { color: #06287e; } /* Function */ + code span.im { } /* Import */ + code span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Information */ + code span.kw { color: #007020; font-weight: bold; } /* Keyword */ + code span.op { color: #666666; } /* Operator */ + code span.ot { color: #007020; } /* Other */ + code span.pp { color: #bc7a00; } /* Preprocessor */ + code span.sc { color: #4070a0; } /* SpecialChar */ + code span.ss { color: #bb6688; } /* SpecialString */ + code span.st { color: #4070a0; } /* String */ + code span.va { color: #19177c; } /* Variable */ + code span.vs { color: #4070a0; } /* VerbatimString */ + code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warning */ + + + + @@ -3274,9 +249,24 @@

    Need random numbers to stay consistent?

    -

    Use set.seed() : sets the starting state for the random number generator (RNG) in R.

    +

    Use set.seed() : sets the starting state for the random number generator (RNG) in R.

    + +
    set.seed(123)
    +sample(10)
    + +
     [1]  3 10  2  8  6  9  1  7  5  4
    + +
    set.seed(123)
    +sample(10)
    -
     [1] 10  7  6  3  1  2  5  9  4  8
    +
     [1]  3 10  2  8  6  9  1  7  5  4
    + +
    set.seed(456)
    +sample(10)
    + +
     [1]  5  3  6 10  4  9  1  2  8  7
    + +

    Note that these are only psuedo random and the values are created doing calculations based on the given seed. Thus the same “random” values will be reproduced by everyone using the same seed with set.seed.

    R Markdown syntax

    @@ -3300,6 +290,8 @@

    Session info can help

    +
    sessionInfo()
    +

    session info

    Lab 1

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