R Tutorials in Data Science

 

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.

 

The online tutorials are best if students have no prior background. In particular the first online tutorial, Introduction to R, can be assigned as a first day course activity. The handouts assume that students have a basic prior knowledge of R or Rstudio.

 

Tutorials:

 

  • Working with Dates and Times: This tutorial uses the lubridate package to modify dates.

    Try an Online Tutorial

    View Student Handout (HTML) or get in RMarkdown

    Created by Laura Chihara

    Prerequisites: Some experience with R or RStudio.

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  • 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.

    View Student Handout (HTML) or get in RMarkdown

    Created by Laura Chihara

    Prerequisites: Some experience with R or RStudio and the Introduction to Working with Strings tutorial.

     

  • 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.

    View Student Handout (HTML) or get in RMarkdown

    Created by Shonda Kuiper

    Prerequisites: Some experience with R or RStudio and the Introduction to ggformula or ggplot2 tutorial.

     

    Additional Resources: Chester Ismay has created an online text, Getting used to R, Rstudio and RMarkdown.

     

    This work represents collaborative work across Grinnell College (Shonda Kuiper and Ryan Miller), Lawrence University (Adam Loy) and Carleton College (Laura Chihara) funded by grants from 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.