This page presents a series of tutorials and interdisciplinary case studies that can be used in a variety of blended as well as brick-and-mortar courses. The materials can be used in
introductory level data science courses as well as more advanced data science or statistics courses. All the RMarkdown (.rmd) files and datasets for the RTutorials are also available on our GitHub site. The GitHub site also includes more advanced case studies for students who have completed these tutorials.
These materials assume that students have a basic prior knowledge of R or Rstudio, such as the Getting Started With R material provided by Carleton College.
Tidy Data: In this tutorial, we focus on how to properly format data sets so that they can be easily analyzed.
Prerequisites: Some experience with R or RStudio. The Introduction to dplyr tutorial is helpful, but not required.
Data Visualization: An Introduction to ggformula: This activity is designed to help students understand the basic components creating graphs. ggformula is an easier introduction to graphs than ggplot2 .
Introduction to Data Scraping: In this tutorial, we will learn how to read data from a table on a web page into R. Note that the links in these files will need to be updated whenever the website changes.
Prerequisites: Some experience with R or RStudio and the Introduction to Working with Strings
Introduction to Classification Trees: In this tutorial, we will learn how to read data from a table on a web page into R. Note that the links in these files will need to be updated whenever the website changes.
This work represents collaborative work across Grinnell College (Shonda Kuiper), Lawrence University (Adam Loy) and Carleton College (Laura Chihara) funded by grants from the Roy J. Carver Charitable Trust, the ACM, and the Teagle Foundation. This work was also developed through undergraduate research projects with Grinnell students Krit Petrachaianan, Zachary Segall, and Ying Long.