R 4 Data Science Quick Reference: A Pocket Guide to APIs, Libraries, and Packages

© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2022

T. MailundR 4 Data Science Quick Referencehttps://doi.org/10.1007/978-1-4842-8780-4_6

6. Pipelines: magrittr

Thomas Mailund¹  

(1)

Aarhus, Denmark

 

Data analysis consists of several steps, where your data moves through different stages of transformations and cleaning before you finally get to model construction. In practical terms, this means that your R code will consist of a series of function calls where the output of one is the input of the next. The pattern is typical, but a straightforward implementation of it has several drawbacks. The Tidyverse has for many years provided a “pipe operator” to alleviate this, and with R 4.1, there is also a built-in ...