Declutter your R workflow with tidy tools
- Published
- Accepted
- Subject Areas
- Data Mining and Machine Learning, Data Science
- Keywords
- tidyverse, dplyr, tidy tools, workflow, tidytext, piping, tidyr, ggplot2, base R, readr, pipe, R
- Copyright
- © 2017 Ross et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2017. Declutter your R workflow with tidy tools. PeerJ Preprints 5:e3180v1 https://doi.org/10.7287/peerj.preprints.3180v1
Abstract
The R language has withstood the test of time. Forty years after it was initially developed (in the form of the S language) R is being used by millions of programmers on workflows the inventors of the language could never have imagined. Although base R packages perform well in most settings, workflows can be made more efficient by developing packages with more consistent arguments, inputs and outputs and emphasizing constantly improving code over historical code consistency. The universe of R packages known as the tidyverse, including dplyr, tidyr and others, aim to improve workflows and make data analysis as smooth as possible by applying a set of core programming principles in package development.
Author Comment
This is part of the 'Practical Data Science for Stats' Collection.