Remove !expr F/T

Author: Susan Vanderplas

part-advanced-topics/03-web-scraping.qmd

@@ -1,7 +1,7 @@
 # Web Scraping {#sec-data-web}
 
 ```{r}
-#| include: !expr F
+#| include: false
 knitr::knit_engines$set(html = function(options) {
   code <- paste(options$code, collapse = '\n')
   out  <- ""
@@ -234,13 +234,13 @@ The city then included some small grocery stores that are very close to meeting
 We can [download the KML file](https://opendata.dc.gov/api/download/v1/items/1d7c9d0e3aac49c1aa88d377a3bae430/kml?layers=4) and change the extension to XML so that the file opens in a standard web browser, or we can directly download the KML file as an XML file as in the code chunk below.
 
 ```{r}
-#| include: !expr F
+#| include: false
 options(width = 120)
 ```
 
 ```{r}
 #| label: get-dc-grocery-data
-#| error: !expr T
+#| error: true
 url <- "https://opendata.dc.gov/api/download/v1/items/1d7c9d0e3aac49c1aa88d377a3bae430/kml?layers=4"
 filename <- "../data/DC_Grocery_Stores_2025.xml"
 
@@ -254,15 +254,15 @@ xml_tbl <- data.frame(line = 1:length(xml_text),
 ```
 
 ```{r}
-#| echo: !expr F
+#| echo: false
 #| paged-print: true
 
 DT::datatable(xml_tbl[c(1:10, 35:64, 3145:3151),], rownames = F, escape = 2, editable = F, options = list(searching = F))
 ```
 
 
 ```{r}
-#| include: !expr F
+#| include: false
 options(width = 80)
 ```
 
@@ -399,7 +399,7 @@ HTML:
 
 Rendered:
 ```{r}
-#| echo: !expr F
+#| echo: false
 htmltools::knit_print.html("<table><tr><th>Heading Col 1</th><th>Heading Col 2</th></tr><tr><td>Value 1</td><td>Value 2</td></tr></table>")
 ```
 
@@ -1332,8 +1332,8 @@ rev_war_battles2 <- tibble(
 When writing custom functions for web scrapign, it's usually helpful to give yourself an idea of what the function does in a comment -- you can often reuse these functions, but that only works if you're kind to future you and remind yourself what they actually do. 
 
 ```{r}
-#| eval: !expr F
-#| include: !expr F
+#| eval: false
+#| include: false
 saveRDS(rev_war_battles2, "../data/rev-war-battles2.RDS")
 ```
 ```{r}
@@ -1344,7 +1344,7 @@ head(rev_war_battles2)
 
 
 ```{r}
-#| eval: !expr F
+#| eval: false
 library(purrr)
 
 process_battle_row <- function(node){# <1>
@@ -1365,8 +1365,8 @@ rev_war_battles3 <- map(rev_war_rows, process_battle_row) |> bind_rows() # <4>
 4. Call `process_battle_row` on each row and bind the rows together. 
 
 ```{r}
-#| eval: !expr F
-#| include: !expr F
+#| eval: false
+#| include: false
 saveRDS(rev_war_battles3, "../data/rev-war-battles3.RDS")
 ```
 ```{r}
@@ -1476,7 +1476,7 @@ If the structured table doesn't exist, the location row doesn't exist, or there
 ##### R {-}
 
 ```{r}
-#| eval: !expr F
+#| eval: false
 library(tidyr)
 library(tibble)
 
@@ -1516,8 +1516,8 @@ battle_locations <- purrr::map(rev_war_battles2$link, get_battle_location)
 battle_locations <- bind_rows(battle_locations)
 ```
 ```{r}
-#| include: !expr F
-#| eval: !expr F
+#| include: false
+#| eval: false
 saveRDS(battle_locations, file = "../data/rev_war_battle_locations.RDS")
 ```
 ```{r}
@@ -1999,7 +1999,7 @@ team_data_clean <- map(team_data, clean_team_df) |>
 
 ##### Python {-}
 ```{python}
-#| cache: !expr F
+#| cache: false
 from warnings import warn
 
 def get_players(sub_df):

part-advanced-topics/04-xml-json-list-processing.qmd

@@ -1,5 +1,5 @@
 
-# Record-based Data and List Processing Strategies {#sec-xml}
+# Record-based Data and List Processing Strategies {#sec-record-data}
 
 @sec-data-web introduces how XML and HTML documents are constructed and demonstrates different techniques for scraping data from the web.  
 @sec-data-apis introduces Application Programming Interfaces (APIs) to get data from the web in a cleaner, more efficient way.

part-advanced-topics/06-pdf-tools.qmd

@@ -262,7 +262,7 @@ You can also run `tesseract` from the command line, if you [install the library]
 For the sake of shorter commands, let's assume I'm working with a 1-page PDF file named `file.pdf` and want to create `file.txt` which contains the text of the image-based PDF.
 
 ```{bash}
-#| eval: !expr F
+#| eval: false
 pdftoppm -png ../data/file.pdf file # <1>
 tesseract  -l eng ../data/file-1.png ../data/file-1-bash # <2>
 ```
@@ -415,7 +415,7 @@ In any case, it's possible to use this command within R or Python, but once you
 Let's OCR the Lancaster county, NE home appraisal image PDF and see what we come up with.
 
 ```{bash}
-#| eval: !expr F
+#| eval: false
 ocrmypdf -l eng ../data/file.pdf ../data/file-ocrmypdf.pdf
 ```
 
@@ -851,7 +851,7 @@ There's a function, `locate_areas()`, that works interactively - it opens a view
 `locate_areas` provides a sequence of coordinates that are relatively consistent across multiple full-page tables, so we might try to use those coordinates to improve our table parsing. 
 
 ```{r}
-#| eval: !expr F
+#| eval: false
 locate_areas(files[1], pages = rep(page_numbers[1], 5))
 ```
 

part-advanced-topics/07-intro-interactive-graphics.qmd

@@ -56,8 +56,8 @@ We'll demonstrate plotly's capabilities using the `volcanoes` data from [Tidy Tu
 
 ```{r}
 #| label: volcano-data-setup-r
-#| message: !expr F
-#| warning: !expr F
+#| message: false
+#| warning: false
 if (!"plotly" %in% installed.packages()) 
   install.packages("plotly")
 
@@ -180,7 +180,7 @@ pl.io.write_html(fig, file = file, auto_open = False)
 ```
 
 ```{r}
-#| echo: !expr F
+#| echo: false
 htmltools::includeHTML(py$file)
 ```
 
@@ -240,7 +240,7 @@ file = 'plotly-python/distplot-volcano.html'
 pl.io.write_html(fig, file = file, auto_open = False)
 ```
 ```{r}
-#| echo: !expr F
+#| echo: false
 library(reticulate)
 htmltools::includeHTML(py$file)
 ```
@@ -572,7 +572,7 @@ pl.io.write_html(fig, file = file, auto_open = False)
 ```
 
 ```{r}
-#| echo: !expr F
+#| echo: false
 htmltools::includeHTML(py$file)
 ```
 
@@ -588,7 +588,7 @@ file = 'plotly-python/vei-volcano.html'
 pl.io.write_html(fig, file = file, auto_open = False)
 ```
 ```{r}
-#| echo: !expr F
+#| echo: false
 htmltools::includeHTML(py$file)
 ```
 
@@ -606,7 +606,7 @@ file = 'plotly-python/vei-duration-volcano.html'
 pl.io.write_html(fig, file = file, auto_open = False)
 ```
 ```{r}
-#| echo: !expr F
+#| echo: false
 htmltools::includeHTML(py$file)
 ```
 
@@ -640,7 +640,7 @@ file = 'plotly-python/scatter-volcano.html'
 pl.io.write_html(fig, file = file, auto_open = False)
 ```
 ```{r}
-#| echo: !expr F
+#| echo: false
 htmltools::includeHTML(py$file)
 ```
 :::
@@ -677,7 +677,7 @@ file = 'plotly-python/scatter-hover-volcano.html'
 pl.io.write_html(fig, file = file, auto_open = False)
 ```
 ```{r}
-#| echo: !expr F
+#| echo: false
 htmltools::includeHTML(py$file)
 ```
 
@@ -733,7 +733,7 @@ file = 'plotly-python/scatter-hover-update-volcano.html'
 pl.io.write_html(fig, file = file, auto_open = False)
 ```
 ```{r}
-#| echo: !expr F
+#| echo: false
 htmltools::includeHTML(py$file)
 ```
 :::
@@ -842,7 +842,7 @@ To explore the leaflet package, we'll start out playing with a [dataset of Bigfo
 
 ```{r}
 #| label: leaflet-setup
-#| echo: !expr F
+#| echo: false
 if (!"leaflet" %in% installed.packages()) install.packages("leaflet")
 
 library(leaflet)
@@ -852,7 +852,7 @@ bigfoot_data <- read_csv("https://query.data.world/s/egnaxxvegdkzzrhfhdh4izb6etm
 ```
 ```{r}
 #| label: leaflet-setup
-#| eval: !expr F
+#| eval: false
 ```
 
 We can start out by plotting a map with the location of each sighting. I've colored the points in a seasonal color scheme, and added the description of each incident as a mouseover label.
@@ -903,7 +903,7 @@ We can also plot regions, instead of just points. I downloaded a dataset release
 
 I've set this chunk to not evaluate because it causes the book to be painfully large.
 ```{r}
-#| eval: !expr F
+#| eval: false
 library(sf)
 bees <- st_read("../data/Crotch_s_Bumble_Bee_Range_-_CDFW_[ds3095].geojson")
 bees <- sf::st_transform(bees, 4326)

part-advanced-topics/08-javascript-graphics.qmd

@@ -52,8 +52,8 @@ We'll demonstrate plotly's capabilities using the `volcanoes` data from [Tidy Tu
 
 ```{r}
 #| label: volcano-data-setup-r
-#| message: !expr F
-#| warning: !expr F
+#| message: false
+#| warning: false
 if (!"plotly" %in% installed.packages()) 
   install.packages("plotly")
 
@@ -176,7 +176,7 @@ pl.io.write_html(fig, file = file, auto_open = False)
 ```
 
 ```{r}
-#| echo: !expr F
+#| echo: false
 htmltools::includeHTML(py$file)
 ```
 
@@ -236,7 +236,7 @@ file = 'plotly-python/distplot-volcano.html'
 pl.io.write_html(fig, file = file, auto_open = False)
 ```
 ```{r}
-#| echo: !expr F
+#| echo: false
 library(reticulate)
 htmltools::includeHTML(py$file)
 ```
@@ -568,7 +568,7 @@ pl.io.write_html(fig, file = file, auto_open = False)
 ```
 
 ```{r}
-#| echo: !expr F
+#| echo: false
 htmltools::includeHTML(py$file)
 ```
 
@@ -584,7 +584,7 @@ file = 'plotly-python/vei-volcano.html'
 pl.io.write_html(fig, file = file, auto_open = False)
 ```
 ```{r}
-#| echo: !expr F
+#| echo: false
 htmltools::includeHTML(py$file)
 ```
 
@@ -602,7 +602,7 @@ file = 'plotly-python/vei-duration-volcano.html'
 pl.io.write_html(fig, file = file, auto_open = False)
 ```
 ```{r}
-#| echo: !expr F
+#| echo: false
 htmltools::includeHTML(py$file)
 ```
 
@@ -636,7 +636,7 @@ file = 'plotly-python/scatter-volcano.html'
 pl.io.write_html(fig, file = file, auto_open = False)
 ```
 ```{r}
-#| echo: !expr F
+#| echo: false
 htmltools::includeHTML(py$file)
 ```
 :::
@@ -673,7 +673,7 @@ file = 'plotly-python/scatter-hover-volcano.html'
 pl.io.write_html(fig, file = file, auto_open = False)
 ```
 ```{r}
-#| echo: !expr F
+#| echo: false
 htmltools::includeHTML(py$file)
 ```
 
@@ -729,7 +729,7 @@ file = 'plotly-python/scatter-hover-update-volcano.html'
 pl.io.write_html(fig, file = file, auto_open = False)
 ```
 ```{r}
-#| echo: !expr F
+#| echo: false
 htmltools::includeHTML(py$file)
 ```
 :::
@@ -838,7 +838,7 @@ To explore the leaflet package, we'll start out playing with a [dataset of Bigfo
 
 ```{r}
 #| label: leaflet-setup
-#| echo: !expr F
+#| echo: false
 if (!"leaflet" %in% installed.packages()) install.packages("leaflet")
 
 library(leaflet)
@@ -848,7 +848,7 @@ bigfoot_data <- read_csv("https://query.data.world/s/egnaxxvegdkzzrhfhdh4izb6etm
 ```
 ```{r}
 #| label: leaflet-setup
-#| eval: !expr F
+#| eval: false
 ```
 
 We can start out by plotting a map with the location of each sighting. I've colored the points in a seasonal color scheme, and added the description of each incident as a mouseover label.
@@ -899,7 +899,7 @@ We can also plot regions, instead of just points. I downloaded a dataset release
 
 I've set this chunk to not evaluate because it causes the book to be painfully large.
 ```{r}
-#| eval: !expr F
+#| eval: false
 library(sf)
 bees <- st_read("../data/Crotch_s_Bumble_Bee_Range_-_CDFW_[ds3095].geojson")
 bees <- sf::st_transform(bees, 4326)