diff --git a/wrangle.Rmd b/wrangle.Rmd
index 92570b9..a3caa27 100644
--- a/wrangle.Rmd
+++ b/wrangle.Rmd
@@ -10,25 +10,34 @@ There are three main parts to data wrangling:
 knitr::include_graphics("diagrams/data-science-wrangle.png")
 ```
 
+<!--# TO DO: Redo the diagram without highlighting import. -->
+
 This part of the book proceeds as follows:
 
--   In [tibbles], you'll learn about the variant of the data frame that we use in this book: the **tibble**.
+-   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 [data import], you'll learn how to get your data from disk and into R.
-    We'll focus on plain-text rectangular formats, but will give you pointers to packages that help with other types of data.
-
--   In [tidy data], you'll learn about tidy data, a consistent way of storing your data that makes transformation, visualisation, and modelling easier.
+-   In Chapter \@ref(tidy-data), you'll learn about tidy data, a consistent way of storing your data that makes transformation, visualisation, and modelling easier.
     You'll learn the underlying principles, and how to get your data into a tidy form.
 
+-   In Chapter \@ref(rectangle-data), you'll learn about hierarchical data formats and how to turn them into rectangular data via unnesting.
+
+-   Chapter \@ref(column-wise-operations) will give you tools for performing the same operation on multiple columns.
+
+-   Chapter \@ref(row-wise-operations) will give you tools for performing operations over rows.
+
 Data wrangling also encompasses data transformation, which you've already learned a little about.
 Now we'll focus on new skills for three specific types of data you will frequently encounter in practice:
 
--   [Relational data] will give you tools for working with multiple interrelated datasets.
+-   Chapter \@ref(relational-data) will give you tools for working with multiple interrelated datasets.
 
--   [Strings] will introduce regular expressions, a powerful tool for manipulating strings.
+-   Chapter \@ref(list-columns) will give you tools for working with list columns --- data stored in columns of a tibble as lists.
 
--   [Factors] are how R stores categorical data.
+-   Chapter \@ref(strings) will give you tools for working with strings and introduce regular expressions, a powerful tool for manipulating strings.
+
+-   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.
 
--   [Dates and times] will give you the key tools for working with dates and date-times.
+-   Chapter \@ref(dates-and-times) will give you the key tools for working with dates and date-times.
+
+<!--# TO DO: Revisit bullet points about new chapters. -->