Data Analysis and Visualization in R

Summer Open
Class Standing
John Withey

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.


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

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
Evans Hall 2617 - Windows / Linux Lab