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Fix references

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Mine Çetinkaya-Rundel 2022-04-13 22:41:24 -04:00
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data-tidy.Rmd
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@ -144,7 +144,7 @@ Sometimes this is easy; other times you'll need to consult with the people who o
Next, you'll **pivot** your data into a tidy form, with variables in the columns and observations in the rows.
tidyr provides two functions for pivoting data: `pivot_longer()`, which makes datasets **longer** by increasing rows and reducing columns, and `pivot_wider()` which makes datasets **wider** by increasing columns and reducing rows.
`pivot_longer()` is very useful for tidying data; `pivot_wider()` is more useful for making non-tidy data (we'll come back to this in Section \@ref(non-tidy-data)), but is occasionally also needed for tidying..
`pivot_longer()` is very useful for tidying data; `pivot_wider()` is more useful for making non-tidy data (we'll come back to this in Section \@ref(rectangle-data)), but is occasionally also needed for tidying..
The following sections work through the use of `pivot_longer()` and `pivot_wider()` to tackle a wide range of realistic datasets.
These examples are drawn from `vignette("pivot", package = "tidyr")` which includes more variations and more challenging problems.

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transform.Rmd
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@ -11,25 +11,26 @@ Now we'll focus on new skills for specific types of data you will frequently enc
This part of the book proceeds as follows:
- In Chapter \@ref(tibbles), you'll learn about the variant of the data frame that we use in this book: the **tibble**. You'll learn what makes them different from regular data frames, and how you can construct them "by hand".
- In Chapter \@ref(tibbles), you'll learn about the variant of the data frame that we use in this book: the **tibble**.
You'll learn what makes them different from regular data frames, and how you can construct them "by hand".
- Chapter \@ref(relational-data) will give you tools for working with multiple interrelated datasets.
- Chapter \@ref(logicals-numbers) ...
<!--# TO DO: Something about logicals and numbers -->
- Chapter \@ref(numbers) ...
- Chapter \@ref(vector-tools) ...
<!--# TO DO: Something about general vector tools -->
- Chapter \@ref(logicals) ...
- Chapter \@ref(missing-values)...
<!--# TO DO: Something about missing values -->
- Chapter \@ref(strings) will give you tools for working with strings and introduce regular expressions, a powerful tool for manipulating strings.
- Chapter \@ref(regular-expressions) ...
- Chapter \@ref(factors) will introduce factors -- how R stores categorical data.
They are used when a variable has a fixed set of possible values, or when you want to use a non-alphabetical ordering of a string.
- Chapter \@ref(dates-and-times) will give you the key tools for working with dates and date-times.
- Chapter \@ref(column-wise) will give you tools for performing the same operation on multiple columns.
<!-- TO DO: Add chapter descriptions -->