Merge pull request #194 from fhdsl/subset_oct

[Subsetting] adding cheatsheets and gut checks

modules/Reproducibility/Reproducibility.Rmd

@@ -67,6 +67,12 @@ knitr::include_graphics("images/reproducibility.png")
 ottrpal::include_slide("https://docs.google.com/presentation/d/1nV7x0mIIE4oWVKxpv4qJNvO17y51MajsERGtzR2qClk/edit#slide=id.gf1accd298e_0_673")
 ```
 
+## We can't get to replicability without reproducibility
+
+```{r, fig.alt="session info", out.width = "80%", echo = FALSE, fig.align='center'}
+ottrpal::include_slide("https://docs.google.com/presentation/d/1nV7x0mIIE4oWVKxpv4qJNvO17y51MajsERGtzR2qClk/edit#slide=id.g3070a1ee60e_0_0")
+```
+
 ## It's worth the wait
 
 ```{r, fig.alt="session info", out.width = "80%", echo = FALSE, fig.align='center'}
@@ -80,6 +86,7 @@ ottrpal::include_slide("https://docs.google.com/presentation/d/1nV7x0mIIE4oWVKxp
 ottrpal::include_slide("https://docs.google.com/presentation/d/1nV7x0mIIE4oWVKxpv4qJNvO17y51MajsERGtzR2qClk/edit#slide=id.gf1cd772e00_0_330")
 ```
 
+
 ## The process
 
 ```{r, fig.alt="session info", out.width = "80%", echo = FALSE, fig.align='center'}

modules/Subsetting_Data_in_R/Subsetting_Data_in_R.Rmd

@@ -30,7 +30,7 @@ We are constantly making improvements.
 -  Reproducible science makes everyone's life easier!  
 - `readr`has helpful functions like `read_csv()` that can help you import data into R  
 
-📃[Cheatsheet](https://daseh.org/modules/cheatsheets/Day-2.pdf)
+📃 [Day 2 Cheatsheet](https://daseh.org/modules/cheatsheets/Day-2.pdf)
 
 
 ## Overview
@@ -382,6 +382,15 @@ test
 clean_names(test)
 ```
 
+## GUT CHECK: Which of the following would NOT always work with a column called `counties_of_seattle_with_population_over_10,000`?
+
+A. Renaming it using `rename` function to something simpler like `seattle_counties_over_10thous`.
+
+B. Keeping it as is and use backticks around the column name when you use it.
+
+C. Keeping it as is and use quotes around the column name when you use it.
+
+
 ## Summary
 
 - data frames are simpler version of a data table
@@ -396,8 +405,14 @@ clean_names(test)
 
 ## Lab Part 1
 
-🏠 [Class Website](https://daseh.org/)    
-💻 [Lab](https://daseh.org/modules/Subsetting_Data_in_R/lab/Subsetting_Data_in_R_Lab.Rmd)
+🏠 [Class Website](https://daseh.org/) 
+
+💻 [Lab](https://daseh.org/modules/Subsetting_Data_in_R/lab/Subsetting_Data_in_R_Lab.Rmd) 
+
+📃 [Day 3 Cheatsheet](https://daseh.org/modules/cheatsheets/Day-3.pdf)
+
+📃 [Posit's `dplyr` Cheatsheet](https://rstudio.github.io/cheatsheets/data-transformation.pdf)
+
 
 # Subsetting Columns
 
@@ -472,6 +487,7 @@ knitr::include_graphics("images/tidyselect.png")
 head(er_30, 2)
 select(er_30, ends_with("cl"), year)
 ```
+
 ## Multiple tidyselect functions
 
 Follows OR logic.
@@ -481,14 +497,7 @@ select(er_30, ends_with("cl"), starts_with("r"))
 
 ```
 
-## Multiple patterns with tidyselect
 
-Need to combine the patterns with the `c()` function.
-
-```{r}
-select(er_30, starts_with(c("r", "l")))
-
-```
 
 
 ## The `where()` function can help select columns of a specific class{.codesmall}
@@ -501,6 +510,11 @@ select(er_30, where(is.numeric))
 
 ```
 
+## GUT CHECK: What function would be useful for getting a vector version of a column?
+
+A. `pull()`
+
+B. `select()`
 
 
 
@@ -636,6 +650,17 @@ knitr::include_graphics("https://media.giphy.com/media/5b5OU7aUekfdSAER5I/giphy.
 ```
 https://media.giphy.com/media/5b5OU7aUekfdSAER5I/giphy.gif
 
+
+## GUT CHECK: If we want to keep just rows that meet either or two conditions, what code should we use?
+
+A. `filter()` with `|`
+
+B. `select()` with `|`
+
+C. `filter()` with `&`
+
+D. `select()` with `&`
+
 ## Summary
 
 - `pull()` to get values out of a data frame/tibble
@@ -657,6 +682,9 @@ https://media.giphy.com/media/5b5OU7aUekfdSAER5I/giphy.gif
 
 🏠 [Class Website](https://daseh.org)    
 💻 [Lab](https://daseh.org/modules/Subsetting_Data_in_R/lab/Subsetting_Data_in_R_Lab.Rmd)
+📃 [Day 3 Cheatsheet](https://daseh.org/modules/cheatsheets/Day-3.pdf)
+
+📃 [Posit's `dplyr` Cheatsheet](https://rstudio.github.io/cheatsheets/data-transformation.pdf)
 
 ## Get the data
 
@@ -816,32 +844,23 @@ select(er_30, newcol, everything())
 head(select(er_30, newcol, everything()), 3)
 ```
 
-## Ordering the columns of a data frame: dplyr {.codesmall}
+<!-- ## Ordering the columns of a data frame: dplyr {.codesmall} -->
 
-Put `year` at the end ("remove, everything, then add back in"):
+<!-- Put `year` at the end ("remove, everything, then add back in"): -->
 
-```{r, eval = FALSE}
-select(er_30, !year, everything(), year)
-```
-
-```{r, echo = FALSE}
-head(select(er_30, !year, everything(), year), 3)
-```
+<!-- ```{r, eval = FALSE} -->
+<!-- select(er_30, !year, everything(), year) -->
+<!-- ``` -->
 
+<!-- ```{r, echo = FALSE} -->
+<!-- head(select(er_30, !year, everything(), year), 3) -->
+<!-- ``` -->
 
-## Ordering the column names of a data frame: alphabetically {.codesmall}
-
-Using the base R `order()` function.
-
-```{r}
-order(colnames(er_30))
-er_30 %>% select(order(colnames(er_30)))
-```
 
 
 ## Ordering the columns of a data frame: dplyr {.codesmall}
 
-In addition to `select` we can also use the `relocate()` function of dplyr to rearrange the columns for more complicated moves.
+In addition to `select` we can also use the `relocate()` function of dplyr to rearrange the columns for more complicated moves with the `.before` and `.after` arguments.
 
 For example, let say we just wanted `year` to be before `rate``.
 
@@ -881,6 +900,23 @@ arrange(er_30, rate, desc(year)) %>% head(n = 2)
 arrange(er_30, desc(year), rate) %>% head(n = 2)
 ```
 
+## GUT CHECK: What function would be useful for changing a column to be a percentage instead of a ratio?
+
+A. `filter()`
+
+B. `select()`
+
+C. `mutate()`
+
+
+## GUT CHECK: How would we interpret `er_30 %>% filter(year > 2020) %>% select(year, rate)`?
+
+A. Get the `er_30` data, then filter it for rows with `year` values over 2020, then select only the `year` and `rate` columns.
+
+B. Get the `er_30` data, then filter it rows with `year` values over 2020, then select for rows that have values for `year` and `rate`.
+
+
+
 
 ## Summary
 
@@ -941,6 +977,9 @@ Even though `$` is easier for creating new columns, `mutate` is really powerful,
 
 💻 [Lab](https://daseh.org/modules/Subsetting_Data_in_R/lab/Subsetting_Data_in_R_Lab.Rmd)
 
+📃 [Day 3 Cheatsheet](https://daseh.org/modules/cheatsheets/Day-3.pdf)
+
+📃 [Posit's `dplyr` Cheatsheet](https://rstudio.github.io/cheatsheets/data-transformation.pdf)
 
 
 ```{r, fig.alt="The End", out.width = "50%", echo = FALSE, fig.align='center'}
@@ -951,6 +990,25 @@ Image by <a href="https://pixabay.com/users/geralt-9301/?utm_source=link-attribu
 
 # Extra Slides
 
+## Multiple patterns with tidyselect
+
+Need to combine the patterns with the `c()` function.
+
+```{r}
+select(er_30, ends_with("cl"), starts_with("r"))
+select(er_30, starts_with(c("r", "l"))) # here we combine two patterns
+
+```
+
+## Ordering the column names of a data frame: alphabetically {.codesmall}
+
+Using the base R `order()` function.
+
+```{r}
+order(colnames(er_30))
+er_30 %>% select(order(colnames(er_30)))
+```
+
 ## `which()` function
 
 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.