Data Wrangling with {dplyr}

Artwork by @allison_horst

When you are given data to analyze, it will almost always be in a format that makes it hard to create visualizations, perform modelling, and generate tables. In other words, most of the time, it will need to be wrangled into the correct format. The dplyr package has a very powerful set of functions for doing just this. 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.