Data Analysis and Visualization in R

Summer Open
Class Standing
John Withey

The open-source R programming language is widely used by environmental professionals and researchers at state agencies, NGO’s, and colleges and universities. Students will learn how to use the 'tidyverse' collection of packages in R to import, clean, and process data, as well as how to use 'ggplot2' to create meaningful visualizations. Students will also use specific advanced analyses such as multi-level/hierarchical modeling, model selection, and machine learning, which can be essential for understanding and addressing environmental issues. In-person activities will include lectures, workshops, and hands-on computer assignments in the Computer Applications Lab. Full remote participation by Zoom and using R on CAL computers or a student’s own computer is also possible.

Prerequisite: using R to complete computer labs in the MES course ‘Research Design & Quantitative Methods,’ (RDQM) or the equivalent experience using R in courses or for independent research.  You must be comfortable setting up your working directory, importing data from .csvs, and downloading and installing packages.  An understanding of inferential statistics including simple linear regression is assumed. Undergraduate enrollment permitted with faculty permission.

John Withey (MES Core Faculty) is a terrestrial ecologist with a background in field ornithology, who first learned R programming as a graduate student more than 20 years ago. He has written R code to estimate spring arrival dates of migratory birds, assess conservation return-on-investment and account for evolutionary distinctiveness, and to incorporate climate change projections into planning for U.S. protected areas. He enjoys using a combination of field-based empirical data, ecological modeling, and spatial and quantitative analyses in his work.

Hi-Flex Class Format: This course is offered in a “Hi-Flex” format. The majority of class meetings will be offered in-person, but students can attend those fully in-person, fully online, or a mix of both. The classes that are offered in-person will also include a Zoom link for remote attendees. Some class meetings will be fully remote (on Zoom), although classroom space will be reserved if students prefer to connect from campus. We will do our best to provide comparable experiences for both in-person and remote students. Remote students can expect to engage with one another through breakout rooms, online discussion boards and other collaborative online methods.

CLASS SCHEDULE: First Summer Session, Tuesday and Thursday nights, 6-10pm


Course Reference Numbers
First Session (4): 40108
GR First Session (4): 40109

Academic Details


Undergraduate students are permitted with faculty signature. Please contact the faculty for more details.



Hybrid (Su)

See definition of Hybrid, Remote, and In-Person instruction

Schedule Details