In this course, you will learn to work within the free and open-source R environment with a specific focus on working with and analyzing spatial data. We will cover a wide variety of data and spatial data analytics topics, and you will learn how to code in R along the way. This course is designed for someone with some prior GIS knowledge. For example, you should know the basics of working with maps, map projections, and vector and raster data. You should be able to perfrom common spatial analysis tasks and make map layouts. If you do not have a GIS background, I would recommend checking out our Introduction to GIScience class. We do not assume that you have any prior experience with R or with coding. So, don't worry if you haven't developed these skillsets yet. That is a major goal in this course.

Backgound material will be provided using code examples, videos, and presentations. Most of the data will be provided so that you can follow along. We have also provided assignments and a term project. Data for the assignments are provided in the Sequencing and Resources section. This section also includes a suggested sequence for working through the material. Feel free to point out issues or provided suggestions.

After completing this course you will be able to:

- prepare, manipulate, query, and generally work with data in R
- perform data summarization, comparisons, and statistical tests
- create quality graphs, map layouts, and interactive web maps to visualize data and findings
- present your research, methods, results, and code as web pages to foster reproduceable research
- work with spatial data in R
- analyze vector and raster data to answer a question with a spatial component
- make spatial models and predictions using regression and machine learning
- code in the R language at an intermediate level

- Introduction and Setup
- R Language Part I
- Data Queries and Manipulation with dplyr
- Working with Strings and Factors
- R Language Part II
- Data Summarization and Statistics
- R Markdown
- Graphs with ggplot2 Part I
- Graphs with ggplot2 Part II
- Working with Spatial Data in R
- Maps with tmap
- Additional Map Examples
- Interactive Maps with Leaflet
- Vector-Based Spatial Analysis
- Raster-Based Spatial Analysis
- LiDAR and Image Analysis
- Machine Learning Background
- Regression and Diagnostics
- Random Forests in R
- Machine Learning with caret

- Sequencing and Resources
- A1: Data Queries and Manipulation
- A2: Functions and Loops
- A3: Data Summarization and Statistics
- A4: R Markdown Webpage
- A5: Aesthetic Mappings
- A6: Graph Design
- A7: Map Layout Design
- A8: Leaflet Interactive Web Map
- A9: Vector-Based Spatial Analysis
- A10: Conservative and Liberal Raster Models
- A11: Linear Regression
- A12: Regression, GWR, and Diagnostics
- A13: Proabilistic Prediction with RF
- A14: Classification with caret
- A15: Regression with caret
- A16: Classification with caret and GEOBIA
- Term Project