Data Analysis & Visualization in R for Environmental Studies: Lab 4, Modeling discrete count data. This lab demonstrates ways to model the response of count data to multiple independent variables, first with Poisson regression using glm(), then with other alternative models using glm.nb(), zeroinfl(), and hurdle().

Data Analysis & Visualization in R for Environmental Studies: Lab 3: Logistic Regression using glm(). This lab introduces the glm() function, first with an example of simple linear regression but then with an extended example of multiple logistic regression.

Data Analysis & Visualization in R for Environmental Studies: Lab 2: More tidying & ggplot. In this lab, more functions from dplyr (part of the tidyverse) are used as well as a general introduction to ggplot.

Data Analysis & Visualization in R for Environmental Studies: Lab 1, Workflow and Tidying.

Data Analysis & Visualization in R for Environmental Studies: Introduction. This page introduces a set of computer lab activities created for students at The Evergreen State College.