Data Wrangling with {dplyr}

Artwork by @allison_horst

Almost always when you are given data to analyze, it will not be in a format in which you are immediately able to create visualizations, perform modelling, generate tables etc. In other words, it needs to be wrangled into the right format. The dplyr package has a very powerful set of functions for just this, and today we will be covering the core dplyr “verbs” that allow you to transform your data with optimal specificity and efficiency.


Slides


Further Reading

  1. R for Data Science chapter on data transformation

  2. Tutorial on tidyselect by Ted Laderas

  3. Flipbooks on data wrangling and summarizing by Gina Reynolds

Department of Psychology

This bootcamp gives a gentle introduction to R and RStudio, transforming and visualizing data with the tidyverse, and the basics of R Markdown.