R for Data Science, 2nd Edition

Chapter 3. Data Transformation

Introduction

Visualization is an important tool for generating insight, but it’s rare that you get the data in exactly the right form you need to make the graph you want. Often you’ll need to create some new variables or summaries to answer your questions with your data, or maybe you just want to rename the variables or reorder the observations to make the data a little easier to work with. You’ll learn how to do all that (and more!) in this chapter, which will introduce you to data transformation using the dplyr package and a new dataset on flights that departed New York City in 2013.

The goal of this chapter is to give you an overview of all the key tools for transforming a data frame. We’ll start with functions that operate on rows and then columns of a data frame, and then we’ll circle back to talk more about the pipe, an important tool that you use to combine verbs. We will then introduce the ability to work with groups. We will end the chapter with a case study that showcases these functions in action, and we’ll come back to the functions in more detail in later chapters, as we start to dig into specific types of data (e.g., numbers, strings, dates).

Prerequisites

In this chapter we’ll focus on the dplyr package, another core member of the tidyverse. We’ll illustrate the key ideas using data from the nycflights13 package and use ggplot2 to help us understand the data.

library(nycflights13)
library(tidyverse)
#> ── Attaching core tidyverse ...