R for Data Science, 2nd Edition

Chapter 27. A Field Guide to Base R

Introduction

To finish off the programming section, we’re going to give you a quick tour of the most important base R functions that we don’t otherwise discuss in the book. These tools are particularly useful as you do more programming and will help you read code you encounter in the wild.

This is a good place to remind you that the tidyverse is not the only way to solve data science problems. We teach the tidyverse in this book because tidyverse packages share a common design philosophy, increasing the consistency across functions, and making each new function or package a little easier to learn and use. It’s not possible to use the tidyverse without using base R, so we’ve actually already taught you a lot of base R functions, including library() to load packages; sum() and mean() for numeric summaries; the factor, date, and POSIXct data types; and of course all the basic operators such as +, -, /, *, |, &, and !. What we haven’t focused on so far is base R workflows, so we will highlight a few of those in this chapter.

After you read this book, you’ll learn other approaches to the same problems using base R, data.table, and other packages. You’ll undoubtedly encounter these other approaches when you start reading R code written by others, particularly if you’re using StackOverflow. It’s 100% OK to write code that uses a mix of approaches, and don’t let anyone tell you otherwise!

In this chapter, we’ll focus on four big topics: subsetting ...