Spatial Data Analysis with R

01:450:320 | Spring 2026 | Updated on: 2026-03-30

Instructor: Lei Song
Teaching Assistant: Md Arafat Hassan
Time: Mon and Wed, 3:50PM - 5:10PM
Location: Lucy Stone Hall (LSH-B266), Livingston campus

Important notes

Course Materials: All lecture materials and assignments will be hosted exclusively on this site. Canvas will be used only for posting grades.

Communication: We will use Google Chat for all course-related correspondence, including both direct messages and group discussions.

Need Help?

Office Hours (PLEASE put your name in the appointment schedule):

Lei Song:

• Wed 1:50PM - 3:50PM (or by appointment), SCHEDULE
• Lucy Stone Hall (B249), Livingston campus.

Md Arafat Hassan:

• Mon 2:30PM to 3:30PM, SCHEDULE
• Lucy Stone Hall (B272), Livingston campus.

Technical Issues: Post in the Google Chat.

Getting start!

Please review the Syllabus to familiarize yourself with the course requirements and follow the Tutorials to complete your initial setup.

• Syllabus (Important)
• Assessment Criteria (Important)
• Software installation
• Git/GitHub

Resources

These resources are helpful references that you will return to throughout the semester.

• Helpful functions
• Cheatsheets

Course materials

The class materials were designed by Lyndon Estes and Lei Song.

• Unit 1 - Fundamentals of R programming
  • Module 1 - Reproducibility and related R skills
  • Module 2 - R and R fundamentals
  • Module 3 - Data preparation and operation
  • Module 4 - Data manipulation and visualization
  • Appendix - Practice answers of Unit 1
• Unit 2 - Handling and analyzing spatial data
  • Module 1 - Vector data
  • Module 2a - Raster data, part 1
  • Module 2b - Raster data, part 2
  • Appendix - Practice answers of Unit 2
• Unit 3 - Final projects

Course Schedule

(Subject to change)

Weekly homework page: https://lei-song.shinyapps.io/SDA320-homework/

Week	Date	Topic	Slides	Deadlines
UNIT 1: FUNDAMENTALS OF R PROGRAMMING
1	Jan 21	Welcome, setup, Git & GitHub	Class 01
2	Jan 26, 28	Reproducibility, R package and R markdown	Class 02, Class 03	Assignment 1 (Jan 30)
3	Feb 2, 4	R data structure and environments	Class 04, Class 05
4	Feb 9, 11	Basic data operation and control flow	Class 06, Class 07	Assignment 2 (Feb 13)
5	Feb 16, 18	tidyverse, working with data	Class 08, Class 09
6	Feb 23, 25	Data manipulation and visualization	Class 10, Class 11	Assignment 3 (Feb 27)
UNIT 2: HANDLING AND ANALYZING SPATIAL DATA
7	Mar 2, 4	Woking with vectors	Class 12, Class 13
8	Mar 9, 11	More on vectors	Class 14, Class 15	Assignment 4 (Mar 13)
9	Mar 16-20	SPRING BREAK - NO CLASS
10	Mar 23, 25	Working with rasters	Class 16, Class 17
11	Mar 30, Apr 1	More on rasters	Class 18, Class 19	Assignment 5 (Apr 3)
UNIT 3: FINAL PROJECT
12	Apr 6, 8	Final project setup	Class 20, Class 21	Project overview (Apr 10)
13-15	Apr 13 onwards	FINAL PROJECT CONSULTATION		Project oral defense (Apr 13)
16	May 4	Final project presentation	Your slides!	Final Project (May 11)

More resources

The following resources are helpful for deeper exploration. You may find them increasingly useful as the semester progresses and your R proficiency grows.

Books etc

• The R Inferno
• Efficient R Programming
• Swirl Stats and the swirl package

The intertubes

R general

• CRAN, particularly an Introduction to R the Task view Analysis of Spatial Data
• R Exercises
• ROpenSci
• Kelly Black’s R tutorial
• Creating reproducible examples (read this before posting to one of the mailing lists)
• R mailing lists/listservs
• Speeding up R
• tidyverse tutorial video

Advanced analytics

• Generalized Additive Models
• Regression Trees, Random Forests, Gradient Boosting
• Support Vector Machines
• Change point detection
• Deep learning in R, 1
• Deep learning in R, 2
• R and DuckDB

Reproducibility

• Add citations to an Rmarkdown document
• Rmarkdown cheatsheet
• Tools for Reproducible Research
• manuscriptPackage: An R package that provides a package template for writing a Latex manuscript using Rmarkdown, complete with citations.

R versus python

This is a big topic, but here are a few links to get started. Lately the two seem to be converging in terms of usage (i.e. there is a trend towards using both together, read the latest news from Rstudio–see the first link)

• Posit – Why RStudio Is Changing Its Name
• R Vs Python: What’s the Difference?
• R vs. Python: Which is a better programming language for data science?
• Why I’ll stick with R
• Why not both?

R spatial

• A short R spatial tutorial, with some good examples for connecting to Google Maps.
• Another R spatial tutorial, with ggplot2 and tmap

Blogs/twitter to follow

• R-Ladies
• R-bloggers
• R-spatial
• @RLangTip

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Last updated on 2026-03-30 | Spatial Data Analysis with R (SDAWR) 2026