R for Data Science

Chapter 17. Iteration with purrr

Introduction

In Chapter 15, we talked about how important it is to reduce duplication in your code by creating functions instead of copying and pasting. Reducing code duplication has three main benefits:

• It’s easier to see the intent of your code, because your eyes are drawn to what’s different, not what stays the same.

• It’s easier to respond to changes in requirements. As your needs change, you only need to make changes in one place, rather than remembering to change every place that you copied and pasted the code.

• You’re likely to have fewer bugs because each line of code is used in more places.

One tool for reducing duplication is functions, which reduce duplication by identifying repeated patterns of code and extracting them out into independent pieces that can be easily reused and updated. Another tool for reducing duplication is iteration, which helps you when you need to do the same thing to multiple inputs: repeating the same operation on different columns, or on different datasets. In this chapter you’ll learn about two important iteration paradigms: imperative programming and functional programming. On the imperative side you have tools like for loops and while loops, which are a great place to start because they make iteration very explicit, so it’s obvious what’s happening. However, for loops are quite verbose, and require quite a bit of bookkeeping code that is duplicated for every for loop. Functional programming ...