R for Data Science, 2nd Edition

Chapter 19. Joins

Introduction

It’s rare that a data analysis involves only a single data frame. Typically you have many data frames, and you must join them together to answer the questions that you’re interested in. This chapter will introduce you to two important types of joins:

• Mutating joins, which add new variables to one data frame from matching observations in another.
• Filtering joins, which filter observations from one data frame based on whether they match an observation in another.

We’ll begin by discussing keys, the variables used to connect a pair of data frames in a join. We cement the theory with an examination of the keys in the datasets from the nycflights13 package and then use that knowledge to start joining data frames together. Next we’ll discuss how joins work, focusing on their action on the rows. We’ll finish up with a discussion of non-equi joins, a family of joins that provide a more flexible way of matching keys than the default equality relationship.

Prerequisites

In this chapter, we’ll explore the five related datasets from nycflights13 using the join functions from dplyr.

library(tidyverse)
library(nycflights13)

Keys

To understand joins, you need to first understand how two tables can be connected through a pair of keys, within each table. In this section, you’ll learn about the two types of key and see examples of both in the datasets of the nycflights13 package. You’ll also learn how to check that your keys are valid and what to do if your ...