Week 5

Tidying and joining spatial data

Summary

This week we are learning to use {dplyr} and {tidyr} to join tables and switching data between a wide and long format.

Overview

Key Objectives

• Practice joining tables by attribute with {dplyr}
• Practice converting between wide and long data formats with dplyr::pivot_longer() and dplyr::pivot_wider()
• Practice recoding categorical attribute data using the {forcats} package

Prepare

Required readings

• Ch. 6 Data tidying 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. 14 Data cleaning in Crystal Lewis Data Management in Large-Scale Education Research, 1st ed. (Chapman & Hall, 2024), https://datamgmtinedresearch.com/.
• Ch. 1 Local Origins, Ch. 2 A Place for Plant Data, and Ch. 3 Collecting Infrastructures in Yanni Alexander Loukissas All Data Are Local: Thinking Critically in a Data-Driven Society, 2019, doi:10.7551/mitpress/11543.001.0001.

Optional readings

• Catherine D’Ignazio and Lauren Klein “Who Collects the Data? A Tale of Three Maps,” MIT Case Studies in Social and Ethical Responsibilities of Computing (February 5, 2021), doi:10.21428/2c646de5.fc6a97cc.
• Karl W. Broman and Kara H. Woo “Data Organization in Spreadsheets,” The American Statistician 72, no. 1 (January 2, 2018): 2–10, doi:10.1080/00031305.2017.1375989.

Participate

🖥️ Tidying and joining data

Practice

Exercise details to be provided.