R for Data Science, 2nd Edition

Chapter 13. Numbers

Introduction

Numeric vectors are the backbone of data science, and you’ve already used them a bunch of times earlier in the book. Now it’s time to systematically survey what you can do with them in R, ensuring that you’re well situated to tackle any future problem involving numeric vectors.

We’ll start by giving you a couple of tools to make numbers if you have strings and then go into a little more detail on count(). Then we’ll dive into various numeric transformations that pair well with mutate(), including more general transformations that can be applied to other types of vectors but are often used with numeric vectors. We’ll finish off by covering the summary functions that pair well with summarize() and show you how they can also be used with mutate().

Prerequisites

This chapter mostly uses functions from base R, which are available without loading any packages. But we still need the tidyverse because we’ll use these base R functions inside of tidyverse functions such as mutate() and filter(). Like in the previous chapter, we’ll use real examples from nycflights13, as well as toy examples made with c() and tribble().

library(tidyverse)
library(nycflights13)

Making Numbers

In most cases, you’ll get numbers already recorded in one of R’s numeric types: integer or double. In some cases, however, you’ll encounter them as strings, possibly because you’ve created them by pivoting from column headers or because something has gone wrong in your data import ...