Update chapter references

data-import.Rmd

@@ -1,4 +1,4 @@
-# Data import
+# Data import {#data-import}
 
 <!--# TO DO: This chapter got moved here from the wrangle section, make sure it makes sense in this new location, doesn't assume anything that comes after it. -->
 

data-transform.Rmd

@@ -1,4 +1,4 @@
-# Data transformation {#transform}
+# Data transformation {#data-transform}
 
 ## Introduction
 

data-visualize.Rmd

@@ -1,4 +1,4 @@
-# Data visualisation
+# Data visualisation {#data-visualisation}
 
 ## Introduction
 

whole-game.Rmd

@@ -13,14 +13,17 @@ knitr::include_graphics("diagrams/data-science-explore.png")
 In this part of the book you will learn some useful tools that have an immediate payoff:
 
 -   Visualisation is a great place to start with R programming, because the payoff is so clear: you get to make elegant and informative plots that help you understand data.
-    In [data visualisation] you'll dive into visualisation, learning the basic structure of a ggplot2 plot, and powerful techniques for turning data into plots.
+    In Chapter \@ref(data-visualisation) you'll dive into visualisation, learning the basic structure of a ggplot2 plot, and powerful techniques for turning data into plots.
 
--   Visualisation alone is typically not enough, so in [data transformation] you'll learn the key verbs that allow you to select important variables, filter out key observations, create new variables, and compute summaries.
+-   Visualisation alone is typically not enough, so in Chapter \@ref(data-transform) you'll learn the key verbs that allow you to select important variables, filter out key observations, create new variables, and compute summaries.
 
--   Finally, in [exploratory data analysis], you'll combine visualisation and transformation with your curiosity and scepticism to ask and answer interesting questions about data.
+-   Before you can transform and visualise your data, you need to first get your data into R.
+    In Chapter \@ref(data-import) you'll learn the basics of getting plain-text rectangular data into R.
 
-Modelling is an important part of the exploratory process, but you don't have the skills to effectively learn or apply it yet.
+-   Finally, in Chapter \@ref(exploratory-data-analysis), you'll combine visualisation and transformation with your curiosity and scepticism to ask and answer interesting questions about data.
+
+Modelling is an important part of the exploratory process, but you don't have the skills to effectively learn or apply it yet so we will not cover it in this part.
 
 Nestled among these three chapters that teach you the tools of exploration are three chapters that focus on your R workflow.
-In [workflow: basics], [workflow: scripts], and [workflow: projects] you'll learn good practices for writing and organising your R code.
+In Chapters \@ref(workflow-basics), \@ref(workflow-scripts), and \@ref(workflow-projects), you'll learn good workflow practices for writing and organising your R code.
 These will set you up for success in the long run, as they'll give you the tools to stay organised when you tackle real projects.