# Introduction

To start your journey in mastering R, the following six chapters will help you learn what I consider to be the foundational components of R. I expect that you’ve already seen many of these pieces before, but you probably have not studied them deeply. To help check your existing knowledge, each chapter starts with a quiz; if you get all the questions right, feel free to skip to the next chapter!

1. In Chapter 2, you’ll learn about one of the most important distinctions you haven’t previously needed to grapple with: the difference between an object and its name. Improving your mental model here will help you make better predictions about when R copies data and hence which basic operations are cheap and which are expensive.

2. Every day you’ve used R, you’ve used vectors, so Chapter 3 will dive into the details, helping you learn how the different types of vector fit together. You’ll also learn about attributes, which allow you to store arbitrary metadata, and form the basis for two of R’s object oriented programming toolkits

3. To write concise and performant R code it is important to fully appreciate the power of subsetting with [, [[ and \$, as described in Chapter 4. Understanding the fundamental components of subsetting will allow you to solve new problems by combining the building blocks in novel ways.

4. The tools of control flow allow you to only execute code under certain conditions, or to repeatedly execute code with changing inputs. In Chapter 5, you’ll learn the important if and for constructs, as well as related tools like switch() and while.

5. Functions are the most important building block of R code, and in Chapter 6, you’ll learn exactly how they work, including the scoping rules, which govern how R looks up values from names. You’ll also learn more of the details behind R’s lazy evaluation, and how you can control what happens when you exit a function.

6. In Chapter 7, you’ll learn about a data structure that is crucial for understanding how R works, but quite unimportant for data analysis: the environment. Environments are the data structure that binds names to values, and they power tools like package namespaces. Unlike most programming languages, environments in R are “first class” which means that you can manipulate them just like other objects.

7. Chapter 8 concludes this section of the book with a discussion of “conditions”, the umbrella term used to describe errors, warnings, and messages. You’ve certainly encountered these before, so in this chapter you learn how to signal them appropriately in your own functions, and how to handle them when signalled elsewhere.