tidyverse typeset consistency

Author: trevorcampbell

intro.Rmd

@@ -1,6 +1,6 @@
 \mainmatter
 
-# R and the tidyverse {#intro}
+# R and the Tidyverse {#intro}
 
 ```{r intro-setup, include=FALSE}
 library(magick)

reading.Rmd

@@ -1220,9 +1220,9 @@ please follow the instructions for computer setup needed to run the worksheets
 found in Chapter \@ref(move-to-your-own-machine).
 
 ## Additional resources
-- The [`readr` page on the tidyverse website](https://readr.tidyverse.org/) is where you should look if you want to learn more about the functions in this chapter, the full set of arguments you can use, and other related functions. The site also provides a very nice cheat sheet that summarizes many of the data wrangling functions from this chapter.
+- The [`readr` page on the Tidyverse website](https://readr.tidyverse.org/) is where you should look if you want to learn more about the functions in this chapter, the full set of arguments you can use, and other related functions. The site also provides a very nice cheat sheet that summarizes many of the data wrangling functions from this chapter.
 - Sometimes you might run into data in such poor shape that none of the reading functions we cover in this chapter works. In that case, you can consult the [data import chapter](https://r4ds.had.co.nz/data-import.html) from [R for Data Science](https://r4ds.had.co.nz/), which goes into a lot more detail about how R parses text from files into data frames.
-- The documentation for many of the reading functions we cover in this chapter can be found [on the tidyverse website](https://readr.tidyverse.org/reference/read_delim.html). This site shows you the full set of arguments available for each function.
+- The documentation for many of the reading functions we cover in this chapter can be found [on the Tidyverse website](https://readr.tidyverse.org/reference/read_delim.html). This site shows you the full set of arguments available for each function.
 - The [`here` package](https://cran.r-project.org/web/packages/here/index.html) provides a way for you to construct or find your files' paths. 
 - The [`readxl` documentation](https://readxl.tidyverse.org/) provides more details on reading data from Excel, such as reading in data with multiple sheets, or specifying the cells to read in. 
 - The [`rio` package](https://github.com/leeper/rio) provides an alternative set of tools for reading and writing data in R. It aims to be a "Swiss army knife" for data reading/writing/converting, and supports a wide variety of data types (including data formats generated by other statistical software like SPSS and SAS).

viz.Rmd

@@ -1512,7 +1512,7 @@ please follow the instructions for computer setup needed to run the worksheets
 found in Chapter \@ref(move-to-your-own-machine).
 
 ## Additional resources
-- The [`ggplot2` page on the tidyverse website](https://ggplot2.tidyverse.org) is where you should look if you want to learn more about the functions in this chapter, the full set of arguments you can use, and other related functions. The site also provides a very nice cheat sheet that summarizes many of the data wrangling functions from this chapter.
+- The [`ggplot2` page on the Tidyverse website](https://ggplot2.tidyverse.org) is where you should look if you want to learn more about the functions in this chapter, the full set of arguments you can use, and other related functions. The site also provides a very nice cheat sheet that summarizes many of the data wrangling functions from this chapter.
 - The [Fundamentals of Data Visualization](https://serialmentor.com/dataviz/) has a wealth of information on designing effective visualizations. It is not specific to any particular programming language or library. If you want to improve your visualization skills, this is the next place to look.
 - [*R for Data Science*](https://r4ds.had.co.nz/) has a chapter on [creating visualizations using `ggplot2`](https://r4ds.had.co.nz/data-visualisation.html). This reference is specific to R and `ggplot2`, but provides a much more detailed introduction to the full set of tools that `ggplot2` provides. This chapter is where you should look if you want to learn how to make more intricate visualizations in `ggplot2` than what is included in this chapter.
 - The [`theme` function documentation](https://ggplot2.tidyverse.org/reference/theme.html)

wrangling.Rmd

@@ -1635,13 +1635,12 @@ found in Chapter \@ref(move-to-your-own-machine).
     functions, we recommend you check out [this
     chapter](http://stat545.com/block010_dplyr-end-single-table.html#where-were-we)
     of the data wrangling, exploration, and analysis with R book.
-  - The [`dplyr` page on the tidyverse website](https://dplyr.tidyverse.org/) is
+  - The [`dplyr` page on the Tidyverse website](https://dplyr.tidyverse.org/) is
     another resource to learn more about the functions in this
     chapter, the full set of arguments you can use, and other related functions.
     The site also provides a very nice cheat sheet that summarizes many of the
     data wrangling functions from this chapter.
-  - Check out the [tidyselect
-    page](https://tidyselect.r-lib.org/reference/select_helpers.html) for a
+  - Check out the [`tidyselect` page](https://tidyselect.r-lib.org/reference/select_helpers.html) for a
     comprehensive list of `select` helpers.
   - [*R for Data Science*](https://r4ds.had.co.nz/) has a few chapters related to
     data wrangling that go into more depth than this book. For example, the
@@ -1658,7 +1657,7 @@ found in Chapter \@ref(move-to-your-own-machine).
     loop](https://r4ds.had.co.nz/iteration.html#iteration).
   - There are many `select` helpers that can be used to efficiently subset 
     columns in a data frame when paired with the `select` function, 
-    or other functions that also use the tidy select syntax for column selection 
+    or other functions that also use the `tidyselect` syntax for column selection 
     (e.g., `pivot-longer`). 
     The [documentation for `select` helpers](https://tidyselect.r-lib.org/reference/select_helpers.html) 
     is a useful reference to find the helper you need for your particular problem.