Week 2

Welcome to GES668 / Get started with spatial data in R

Summary

Review the course syllabus and structure. Get started with spatial data using {sf} and the tidyverse.

Overview

Welcome to Building Spatial Datasets!

Our first session starts with a general introduction to the course and a hands-on introduction to using R for working with spatial data.

Key Objectives

• Provide an overview of the scope and schedule of the course
• Introduce the concept of reproducibility and key reproducible practices for data management, programming, collaborating with colleagues, organizing projects, and tracking work
• Configure RStudio and create a RStudio project
• Introduce the concept of simple features and how the concept is implemented by the {sf} package
• Create a Quarto document to practice using the {tidyverse} and {sf} package

Key Skills

• Creating self-contained projects with RStudio
• Installing packages with install.packages()
• Loading packages with library()
• Using the ? operator to look up function documentation
• Using str() or View() to look at an object
• Using an assignment operator (<- or =) to save an output to a new object
• Creating and editing Quarto documents with RStudio
• Using sf::st_read() to read a sf object from a file path or URL

Prepare

TipGetting ready for the first class

• Install R and RStudio on your computer. If you already have R installed, please check to make sure your installation is up to date. For more details, check out the Prerequisites section from R for Data Science (one of a few books we’ll use in our class).

• Sign up for a GitHub user account. I use GitHub to distribute assignments and you will use GitHub to submit assignments. GitHub is one of the most popular platform for collaborative coding and makes using Git for version control easier. People use GitHub for everything from writing R packages to textbooks!

• Install Git on your computer. One easy option is to install the GitHub Desktop application (which installs Git at the same time). Alternatively, Happy Git and GitHub for the useR details a few different ways to download and install Git on Windows, OSX, or Linux.

• Join our course Discord. I use Discord to share class announcements and encourage students to use the Discord to share links and questions throughout the semester.

• Complete the student survey. This is how you share your GitHub username. I’d also love to know more about what experiences you’re bringing to the class and what you hope to learn.

• If you have not done any programming before, I recommend taking some time to review The Very Basics and Packages and Help Pages (section 2 and 3 from Hands-On Programming with R).

Required readings

• Ch. 1 Introduction in Robin Lovelace, Jakub Nowosad, and Jannes Muenchow Geocomputation with R, 2nd ed. (CRC Press, 2025), https://r.geocompx.org.
• Ch. 2 Geographic data in R in Lovelace, Nowosad, and Muenchow Geocomputation with R.
• Chris Brunsdon and Alexis Comber “Opening Practice: Supporting Reproducibility and Critical Spatial Data Science,” Journal of Geographical Systems 23, no. 4 (October 1, 2021): 477–496, doi:10.1007/s10109-020-00334-2.

Optional resources

Additional readings on working with spatial data in R:

• Introduction to Geospatial Concepts (Data Carpentry)
• Introduction to R for Geospatial Data (Data Carpentry)
• Ch. 3 Geometries in Edzer Pebesma and Roger Bivand Spatial Data Science (CRC Press, 2023), https://r-spatial.org/book/.

Additional readings on reproducible practices:

• Jennifer Bryan “How to Name Files,” May 14, 2015, https://speakerdeck.com/jennybc/how-to-name-files. (or the more recent recorded talk How to name files talk for NormConf 2022)

Participate

🖥️ Welcome to GES 668

🖥️ Introduction to Spatial Data with R

Practice

🛠️️ Exercise 01