For teams that code using the R programming language, the most familiar tools are often part of “base R” meaning that those functions and packages come pre-loaded when R is installed. Relatively recently the tidyverse has emerged as a comprehensive suite of packages that can complement base R or serve as an alternative for some tasks. This includes packages like dplyr and tidyr as well as the perhaps infamous pipe operator (%>%) among many other tools. This workshop is aimed at helping participants use the tidyverse equivalents of fundamental data wrangling tasks that learners may be used to performing with base R.
The development of this training material is supported through the Long Term Ecological Research Network Office (LNO) (NSF award numbers 1545288 and 1929393) and the National Center for Ecological Analysis and Synthesis, UC Santa Barbara.
Citation: Nick Lyon, Angel Chen, and Julien Brun. 2023. Coding in the tidyverse. LNO Scientific Computing Team.
This work is licensed under a Creative Commons Attribution 4.0 International License.
