Add missing intro subheads

model-building.Rmd

@@ -1,5 +1,7 @@
 # Model building
 
+## Introduction
+
 In the previous chapter you learned how some basic models worked, and learned some basic tools for understanding what a model is telling you about your data. In this chapter, we're going talk more about the model building process: how you start from nothing, and end up with a good model. 
 
 We are going to focus on predictive models, how you can use simple fitted models to help better understand your data. Many of the models will be motivated by plots: you'll use a model captures to strong signals in the data so you can focus on what remains. This is a different motivation from most introductions to modelling, but if you go on to more traditional coverage, you can apply these same ideas to help you understand what's going on.

model-many.Rmd

@@ -1,5 +1,7 @@
 # Many models
 
+## Introduction
+
 In this chapter you're going to learn three powerful ideas that help you to work with large numbers of models with ease:
 
 1.  Using many simple models to better understand complex datasets.
@@ -35,7 +37,7 @@ The following sections will dive into more detail about the individual technique
 
 This chapter focusses on models generated from subsets of your data (exhaustive partition defined by the value of one or more variables). This is a powerful technique for data exploration, and is often a key step on the way to a single complex model that combines the information from all subsets. In the next chapter, you'll learn about another set of techniques that generates multiple models: resampling. Resampling is a powerful tool to help you understand the inferential properties of a model.
 
-## Prerequisites
+### Prerequisites
 
 Working with many models requires a combination of packages that you're already familiar with from data exploration, wrangling, programming, and modelling.
 

pipes.Rmd

@@ -1,5 +1,7 @@
 # Pipes
 
+## Introduction
+
 Pipes let you transform the way you call deeply nested functions. Using a pipe doesn't affect what the code does; behind the scenes it is run in (almost) the exact same way. What the pipe does is change how _you_ write, and read, code. 
 
 You've been using the pipe for a while now, so you already understand the basics. The point of this chapter is to explore the pipe in more detail. You'll learn the alternatives that the pipe replaces, and the pros and cons of the pipe. Importantly, you'll also learn situations in which you should avoid the pipe. 

tidy.Rmd

@@ -1,5 +1,7 @@
 # Tidy data
 
+## Introduction
+
 > "Tidy datasets are all alike, but every messy dataset is messy in its
 > own way." --– Hadley Wickham