R Data Science Quick Reference: A Pocket Guide to APIs, Libraries, and Packages

© Thomas Mailund 2019

Thomas MailundR Data Science Quick Referencehttps://doi.org/10.1007/978-1-4842-4894-2_7

7. Manipulating Data Frames: dplyr

Thomas Mailund¹ 

(1)

Aarhus, Denmark

 

The dplyr package resembles the functionality in the purrr package, but it is designed for manipulating data frames. It will be loaded with the tidyverse package , but you can also load it using

library(dplyr)

The usual way that you use dplyr is similar to how you use purrr. You string together a sequence of actions in a pipeline, with the actions separated by the %>% operator. The difference between the two packages is that the purrr functions work on sequences, while the dplyr functions work on data frames.

This package is huge and more functionality is added in each new ...