Merge pull request #501 from e-clin/patch-1

Update 02-visualization.Rmd

02-visualization.Rmd

@@ -190,7 +190,7 @@ Other more complex components like `scales` and `coord`inate systems are left fo
 
 ### ggplot2 package
 
-In this book, we will use the `ggplot2` package for data visualization, which is an implementation of the `g`rammar of `g`raphics for R [@R-ggplot2]. As we noted earlier, a lot of the previous section was written in a computer code type font. This is because the various components of the grammar of graphics are specified in the `ggplot()` \index{ggplot2!ggplot()} function included in the `ggplot2` package. For the purposes of this book, we'll always provide the `ggplot()` function with the following arguments (i.e., inputs) at a minimum:
+In this book, we will use the `ggplot2` package for data visualization, which is an implementation of the **g**rammar of **g**raphics for R [@R-ggplot2]. As we noted earlier, a lot of the previous section was written in a computer code type font. This is because the various components of the grammar of graphics are specified in the `ggplot()` \index{ggplot2!ggplot()} function included in the `ggplot2` package. For the purposes of this book, we'll always provide the `ggplot()` function with the following arguments (i.e., inputs) at a minimum:
 
 * The data frame where the variables exist: the `data` argument.
 * The mapping of the variables to aesthetic attributes: the `mapping` argument which specifies the `aes`thetic attributes involved.
@@ -1203,7 +1203,7 @@ if (is_latex_output()) {
 generate_r_file_link("02-visualization.R")
 ```
 
-If you want to further unlock the power of the `ggplot2` package for data visualization, we suggest that you check out RStudio's "Data Visualization with ggplot2" cheatsheet. This cheatsheet summarizes much more than what we've discussed in this chapter. In particular, it presents many more than the 5 `geom`etric objects we covered in this chapter while providing quick and easy to read visual descriptions. For all the `geom`etric objects, it also lists all the possible aesthetic attributes one can tweak. In the current version of RStudio in late 2019, you can access this cheatsheet by going to the RStudio Menu Bar -> Help -> Cheatsheets -> "Data Visualization with ggplot2." `r if(is_html_output()) "You can see a preview in the figure below."` Alternatively, you can preview the cheat sheet by going to the ggplot2 Github page with this [link](https://github.com/rstudio/cheatsheets/blob/main/data-visualization-2.1.pdf).
+If you want to further unlock the power of the `ggplot2` package for data visualization, we suggest that you check out RStudio's "Data Visualization with ggplot2" cheatsheet. This cheatsheet summarizes much more than what we've discussed in this chapter. In particular, it presents many more than the 5 `geom`etric objects we covered in this chapter while providing quick and easy to read visual descriptions. For all the `geom`etric objects, it also lists all the possible aesthetic attributes one can tweak. In the current version of RStudio in mid 2024, you can access this cheatsheet by going to the RStudio Menu Bar -> Help -> Cheatsheets -> "Data Visualization with ggplot2." `r if(is_html_output()) "You can see a preview in the figure below."` Alternatively, you can preview the cheat sheet by going to the ggplot2 Github page with this [link](https://github.com/rstudio/cheatsheets/blob/main/data-visualization-2.1.pdf).
 
 ```{r ggplot-cheatsheet, echo=FALSE, fig.cap="Data Visualization with ggplot2 cheatsheet.", purl=FALSE}
 if (is_html_output()) {