R for Data Science

Chapter 16. Vectors

Introduction

So far this book has focused on tibbles and packages that work with them. But as you start to write your own functions, and dig deeper into R, you need to learn about vectors, the objects that underlie tibbles. If you’ve learned R in a more traditional way, you’re probably already familiar with vectors, as most R resources start with vectors and work their way up to tibbles. I think it’s better to start with tibbles because they’re immediately useful, and then work your way down to the underlying components.

Vectors are particularly important as most of the functions you will write will work with vectors. It is possible to write functions that work with tibbles (like in ggplot2, dplyr, and tidyr), but the tools you need to write such functions are currently idiosyncratic and immature. I am working on a better approach, https://github.com/hadley/lazyeval, but it will not be ready in time for the publication of the book. Even when complete, you’ll still need to understand vectors; it’ll just make it easier to write a user-friendly layer on top.

Prerequisites

The focus of this chapter is on base R data structures, so it isn’t essential to load any packages. We will, however, use a handful of functions from the purrr package to avoid some inconsistences in base R.

library(tidyverse)
#> Loading tidyverse: ggplot2
#> Loading tidyverse: tibble
#> Loading tidyverse: tidyr
#> Loading tidyverse: readr
#> Loading tidyverse: purrr
#> Loading tidyverse: dplyr ...