Week 3

Transforming data with {dplyr}

Overview

This week includes a review of data visualization and mapping with {ggplot} review, an introduction to data transformation with {dplyr}, and an initial discussion of using {dplyr} with sf objects.

Key Objectives

• Review the concept of “tidy”, analysis-friendly data
• Introduce the “verbs” of data wrangling with {dplyr}
• Practice subsetting rows with dplyr::filter() or dplyr::slice()
• Practice subsetting columns with dplyr::select()
• Practice using {tidyselect} helpers including where(), all_of(), any_of(), and starts_with()
• Practice creating new variables with dplyr::mutate() or dplyr::summarise()

Prepare

Required readings

• Ch. 3 Data Structure in Crystal Lewis Data Management in Large-Scale Education Research, 1st ed. (Chapman & Hall, 2024), https://datamgmtinedresearch.com/.
• Ch. 5 Data transformation in Hadley Wickham, Garrett Grolemund, and Mine Çetinkaya-Rundel R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, 2nd edition. (Sebastopol, CA: O’Reilly Media, 2023), https://r4ds.hadley.nz/.
• Ch. 3 Attribute data operations in Robin Lovelace, Jakub Nowosad, and Jannes Muenchow Geocomputation with R, 2nd ed. (CRC Press, 2025), https://r.geocompx.org.

Optional readings

• Ch. 5 Attributes and Support in Edzer Pebesma and Roger Bivand Spatial Data Science (CRC Press, 2023), https://r-spatial.org/book/.
• dplyr base R vignette for the {dplyr} package comparing dplyr functions to their base R equivalents.

Participate

🖥️ Transforming data with {dplyr} and {tidyr}

Practice

🛠️️ Exercise 03