Fixing more yaml

Author: Susan Vanderplas

part-gen-prog/06-debugging.qmd

@@ -137,7 +137,7 @@ Redesigning your code to be more modular and more organized is also a good idea.
 In programming, as in life, big, general problems are very hard to solve effectively. 
 Instead, the goal is to break a problem down into smaller pieces that may actually be solvable. 
 
-::: demo
+:::: demo
 ### Demo: Exhaustion
 
 This example inspired by @grimesThis500YearOldPiece2019. 
@@ -201,7 +201,7 @@ In programming, of course, this list would perhaps be a bit more sequential, but
   - [2] Revise your to-do list and try a different tactic if what you were trying didn't work.
 
 :::
-:::
+::::
 
 
 ## Minimal Working (or Reproducible) Examples
@@ -239,7 +239,7 @@ Describe what you see and what you'd hope to see if the code were working.
 - [reprex magic - Vignette adapted from a blog post by Nick Tierney](https://reprex.tidyverse.org/articles/articles/magic-reprex.html)
 :::
 
-::: demo
+:::: demo
 ### Demo: Minimum Working Examples in Practice 
 
 ::: panel-tabset
@@ -351,7 +351,7 @@ I didn't know enough python or enough about GitHub Actions to diagnose the probl
 
 :::
 
-:::
+::::
 
 ::: example
 ### Example: Debugging Exercises 
@@ -375,7 +375,7 @@ When the code is executed, you get a window into what the variables look like du
 
 This is called **print debugging** and it is an incredibly useful tool.
 
-::: demo
+:::: demo
 #### Demo: Nested Functions
 
 ::: panel-tabset
@@ -480,11 +480,11 @@ aa(5)
 ```
 :::
 
-:::
+::::
 
 For more complex data structures, it can be useful to add `str()`, `head()`, or `summary()` functions. 
 
-::: demo
+:::: demo
 #### Real world demo: Web Scraping
 
 In fall 2020, I wrote a webscraper to get election polling data from the RealClearPolitics site as part of the [`electionViz` package](https://www.github.com/heike/electionViz).
@@ -571,9 +571,9 @@ By printing out all of the tags that contain `node`, I could see the order -- in
 I asked the function to return the location of the first table node, so the index (2nd value printed out) should match table in the character vector that was printed out first. 
 I could then see that the HTML node that is returned is in fact the table node. 
 
-:::
+::::
 
-::: example
+:::: example
 #### Example: Hurricanes in R
 
 Not all bugs result in error messages, unfortunately, which makes higher-level techniques like `traceback()` less useful. 
@@ -791,7 +791,7 @@ for (i in 1:5) {
 ```
 :::
 
-:::
+::::
 
 Once you've found your problem, go back and delete or comment out your print statements, as they're no longer necessary. 
 If you think you may need them again, comment them out, otherwise, just delete them so that your code is neat, clean, and concise.
@@ -802,7 +802,7 @@ If you think you may need them again, comment them out, otherwise, just delete t
 `traceback()` can help you narrow down where an error occurs by taking you through the series of function calls that led up to the error. 
 This may help you identify which function is actually causing the problem, which is especially useful when you have nested functions or are using package functions that depend on other packages.
 
-::: demo
+:::: demo
 #### Demo: Using traceback
 
 ::: panel-tabset
@@ -875,7 +875,7 @@ Python's [traceback](https://docs.python.org/3/library/traceback.html) informati
 
 :::
 
-:::
+::::
 
 ### Interactive Debugging
 
@@ -911,7 +911,9 @@ This ensures that ipdb is launched when an error is reached.
 :::
 
 :::: example
+
 #### Example: Interactive Debugging with XKCD {-}
+
 ::: panel-tabset
 
 ##### Problem {-}
@@ -988,7 +990,6 @@ get_xkcd() %>%
   as.raster() %>%
   plot()
 ```
-:::
 
 
 ##### Python {-}
@@ -1116,9 +1117,9 @@ plt.show()
 
 :::
 
-:::
+::::
 
-::: example
+:::: example
 #### Example: More Interactive Debugging with XKCD
 
 ::: panel-tabset
@@ -1246,7 +1247,7 @@ res = get_xkcd('abcd')
 
 :::
 
-:::
+::::
 
 ### R `debug()`
 
@@ -1259,7 +1260,7 @@ So, if you can't change the code in the function causing the error, `debug()` is
 Otherwise, using `browser()` is generally easier. 
 Essentially, `debug()` places a `browser()` statement at the first line of a function, but without having to actually alter the function's source code. 
 
-::: demo
+:::: demo
 #### Demo: `debug()` in R
 
 ```{r}
@@ -1301,12 +1302,12 @@ We probably could have gotten there from reading the error message carefully, bu
 
 But, until I run `undebug(lm)`, every call to `lm` will take me into the debug window. 
 
-:::
+::::
 
 `undebug(f)` will remove the debug flag on the function `f`. `debugonce(f)` will only debug f the first time it is run. 
 
 
-::: example
+:::: example
 #### Example: `debug` in R
 
 ::: panel-tabset
@@ -1354,6 +1355,6 @@ larger <- function(x, y) {
 
 :::
 
-:::
+::::
 
 ## References  {#sec-debugging-refs}

part-wrangling/01-data-input.qmd

@@ -79,9 +79,9 @@ In base R (no extra packages), you can read fixed-width files in using `read.fwf
 
 ```{r}
 #| label: read-fwf-colbreaks
-#| error: !expr T
-#| eval: !expr '-1'
-#| echo: !expr '-2'
+#| error: true
+#| eval: -1
+#| echo: -2
 url <- "https://www.mesonet.org/index.php/dataMdfMts/dataController/getFile/202206070000/mdf/TEXT/"
 url <- "../data/mesodata.txt"
 data <- read.fwf(url, 
@@ -143,7 +143,7 @@ You can also write fixed-width files if you *really* want to:
 
 ```{r}
 #| label: gdata-demo
-#| message: !expr F
+#| message: false
 if (!"gdata" %in% installed.packages()) install.packages("gdata")
 library(gdata)
 write.fwf(mtcars, file = tempfile())
@@ -155,7 +155,7 @@ The `readr` package creates data-frame like objects called tibbles (a souped-up
 
 ```{r}
 #| label: readr-demo
-#| message: !expr F
+#| message: false
 library(readr) # Better data importing in R
 
 read_table(url, skip = 2) # Gosh, that was much easier!
@@ -189,7 +189,7 @@ Delimited text files are files where fields are separated by a specific characte
 
 ```{r}
 #| label: delimited-text-files-r
-#| collapse: !expr F
+#| collapse: false
 url <- "https://raw.githubusercontent.com/srvanderplas/datasets/main/clean/pokemon_gen_1-9.csv"
 
 pokemon_info <- read.csv(url, header = T, stringsAsFactors = F)
@@ -204,7 +204,7 @@ The most common delimited text format is CSV: comma-separated value.
 
 ```{r}
 #| label: delimited-text-files-r3
-#| collapse: !expr F
+#| collapse: false
 library(readr)
 url <- "https://raw.githubusercontent.com/srvanderplas/datasets/main/clean/pokemon_gen_1-9.csv"
 pokemon_info <- read_csv(url)
@@ -218,7 +218,7 @@ There is a family of `read_xxx` functions in `pandas` including functions to rea
 
 ```{python}
 #| label: delimited-text-files-py
-#| collapse: !expr F
+#| collapse: false
 import pandas as pd
 
 url <- "https://raw.githubusercontent.com/srvanderplas/datasets/main/clean/pokemon_gen_1-9.csv"
@@ -317,8 +317,8 @@ Because these data sets are comparatively large, they are available as compresse
 
 ```{r}
 #| label: rebrickable-lego-data-once
-#| eval: !expr T
-#| include: !expr F
+#| eval: true
+#| include: false
 # Download the data if it doesn't exist, as it's used later
 library(readr)
 if (!file.exists("../data/lego_sets.csv")) {
@@ -337,7 +337,7 @@ The `readr` package and `pandas` can handle .csv.gz files with no problems. Try
 
 ```{r}
 #| label: rebrickable-lego-data
-#| eval: !expr T
+#| eval: true
 library(readr)
 legosets <- read_csv("https://cdn.rebrickable.com/media/downloads/sets.csv.gz")
 write_csv(legosets, "../data/lego_sets.csv")
@@ -347,7 +347,7 @@ write_csv(legosets, "../data/lego_sets.csv")
 #### Python Solution {-}
 ```{python}
 #| label: rebrickable-lego-data-py
-#| eval: !expr F
+#| eval: false
 import pandas as pd
 
 legosets = pd.read_csv("https://cdn.rebrickable.com/media/downloads/sets.csv.gz")
@@ -374,7 +374,7 @@ It is usually helpful to open the spreadsheet up in a graphical program first to
 
 ```{r}
 #| label: spreadsheet-readin
-#| warning: !expr F
+#| warning: false
 if (!"readxl" %in% installed.packages()) install.packages("readxl")
 library(readxl)
 
@@ -468,7 +468,7 @@ One simple hack-ish way to read google sheets in Python (so long as the sheet is
 
 ```{python}
 #| label: google-sheets-py2
-#| eval: !expr T
+#| eval: true
 import pandas as pd
 
 sheet_id = "1wZhPLMCHKJvwOkP4juclhjFgqIY8fQFMemwKL2c64vk"
@@ -486,7 +486,7 @@ This method is described in @schaferReadDataGoogle2020 for Python, but I have ad
 
 ```{R}
 #| label: google-sheets-r2
-#| eval: !expr T
+#| eval: true
 library(readr)
 sheet_id = "1wZhPLMCHKJvwOkP4juclhjFgqIY8fQFMemwKL2c64vk"
 sheet_name = "Items"
@@ -506,7 +506,7 @@ Copy this code and run it on your computer to read in a sheet from google drive
 
 ```{r}
 #| label: google-sheets-r
-#| eval: !expr F
+#| eval: false
 library(googlesheets4)
 gs4_auth(scopes = "https://www.googleapis.com/auth/drive.readonly") # Read-only permissions
 data_is_plural <- read_sheet("https://docs.google.com/spreadsheets/d/1wZhPLMCHKJvwOkP4juclhjFgqIY8fQFMemwKL2c64vk/edit#gid=0")
@@ -521,7 +521,7 @@ Then, you will need to obtain a client token JSON file following [these instruct
 
 ```{python}
 #| label: google-sheets-py
-#| eval: !expr F
+#| eval: false
 import gspread as gs
 import pandas as pd
 ```

part-wrangling/02-basic-data-vis.qmd

@@ -12,7 +12,7 @@ This section is intended as a very light overview of how you might create charts
 You will need the `seaborn` (python) and `ggplot2` (R) packages for this section. 
 
 ```{r}
-#| eval: !expr F
+#| eval: false
 install.packages("ggplot2")
 ```
 
@@ -23,7 +23,7 @@ To install seaborn, pick one of the following methods (you can read more about t
 ### System Terminal
 
 ```{bash}
-#| eval: !expr F
+#| eval: false
 pip3 install seaborn matplotlib
 ```
 
@@ -32,7 +32,7 @@ pip3 install seaborn matplotlib
 This package installation method requires that you have a virtual environment set up (that is, if you are on Windows, don't try to install packages this way).
 
 ```{r}
-#| eval: !expr F
+#| eval: false
 reticulate::py_install(c("seaborn", "matplotlib"))
 ```
 
@@ -41,7 +41,7 @@ reticulate::py_install(c("seaborn", "matplotlib"))
 In a python chunk (or the python terminal), you can run the following command. This depends on something called "IPython magic" commands, so if it doesn't work for you, try the System Terminal method instead.
 
 ```{python}
-#| eval: !expr F
+#| eval: false
 %pip install seaborn matplotlib
 ```
 
@@ -113,8 +113,8 @@ ggplot(hbcu_all, aes(x = Year, y = `4-year`)) + geom_line() +
 
 ```{python}
 #| label: line-graph-python-p9
-#| eval: !expr F
-#| include: !expr F
+#| eval: false
+#| include: false
 
 ggplot(hbcu_all, aes(x = "Year", y = "4-year")) + geom_line() + \
  ggtitle("4-year HBCU College Enrollment")
@@ -161,14 +161,14 @@ hbcu_long = pd.melt(hbcu_all, id_vars = ['Year'], value_vars = hbcu_all.columns[
 #### Original Data
 ```{r}
 #| label: pivot-operation-data
-#| echo: !expr F
+#| echo: false
 knitr::kable(head(hbcu_all))
 ```
 
 #### Long Data
 ```{r}
 #| label: pivot-operation-data-2
-#| echo: !expr F
+#| echo: false
 
 knitr::kable(head(hbcu_long))
 ```
@@ -201,8 +201,8 @@ ggplot(hbcu_long, aes(x = Year, y = value, color = type)) + geom_line() +
 #### Python
 ```{python}
 #| label: long-form-demo-python-p9
-#| eval: !expr F
-#| include: !expr F
+#| eval: false
+#| include: false
 
 ggplot(hbcu_long, aes(x = "Year", y = "value", color = "variable")) + geom_line() + \
   ggtitle("HBCU College Enrollment") + \