From 9a7c0c405ce77aaf0d281be8bc60bb24cd46d7cd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mine=20=C3=87etinkaya-Rundel?= Date: Wed, 13 Apr 2022 22:41:24 -0400 Subject: [PATCH] Fix references --- data-tidy.Rmd | 2 +- transform.Rmd | 17 +++++++++-------- 2 files changed, 10 insertions(+), 9 deletions(-) diff --git a/data-tidy.Rmd b/data-tidy.Rmd index 134ea05..6acb67d 100644 --- a/data-tidy.Rmd +++ b/data-tidy.Rmd @@ -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. diff --git a/transform.Rmd b/transform.Rmd index 85a7f29..7aeb4da 100644 --- a/transform.Rmd +++ b/transform.Rmd @@ -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) ... - +- Chapter \@ref(numbers) ... -- Chapter \@ref(vector-tools) ... - - +- Chapter \@ref(logicals) ... - Chapter \@ref(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. + +