Data Analysis and Visualization in R

Summer 2023 First Session
Junior - Graduate
Class Size: 15
4 Credits per quarter
Variable credit options, see below
Log in to add this offering to your saved list.
Taught by

Description: 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 R to import, clean, and process data, as well as how to use various R packages such as ggplot2, dplyr, and tidyr to create meaningful visualizations. Students will also be able to choose specific advanced techniques to learn such as multi-level/hierarchical modeling, spatial data analysis, and/or machine learning, which are all essential for understanding and addressing environmental issues. In-person activities will include lectures, workshops, and hands-on computer assignments in the Computer Applications Lab (if available).  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’ or the equivalent experience using R in courses or for work. 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 20 years ago. He teaches MES classes including Research Design & Quantitative Methods (RDQM), Ecological & Social Sustainability (ESS), and Urban Ecology.  He has taught statistical computer labs using R in RDQM, and three ILC’s focused on advanced topics in R. In one current research collaboration he is examining phenological mismatch across multiple trophic levels, with a particular focus on migratory birds. He has also written R code to use with conservation prioritization approaches such as conservation return-on-investment, accounting for evolutionary distinctiveness, using sage grouse as an umbrella species, and incorporating 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.


Summer 2023 Registration

Course Reference Numbers

GR First Session (4): 40111
Jr - Sr First Session (4): 40112

Academic details

Fields of Study
Variable Credit Options

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

Maximum Enrollment
Class Standing


Time Offered
Schedule Evergreen link
see Schedule Evergreen for detailed schedule

First Meeting

Evans Hall 2617 - Windows / Linux Lab