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:

• Tidy Data: In this tutorial, we focus on how to properly format data sets so that they can be easily analyzed.

  Try an Online Tutorial

  View Student Handout (HTML) or get in RMarkdown

  Happy Planet Index (hpi-tidy) Dataset (.CSV)

  UBSprices Dataset (.CSV)

  rcp-polls Dataset (.CSV)

  airline-safety Dataset (.CSV)

  Created by Adam Loy

  Prerequisites: Some experience with R or RStudio such as the online tutorial, Introduction to R.

   

• Data Wrangling: An Introduction to dplyr: In this tutorial, we introduce data wrangling (data manipulation) techniques.

  Try an Online Tutorial

  View Student Handout (HTML) or get in RMarkdown

  Colleges2015 Dataset (.CSV)

  Created by Adam Loy

  Prerequisites: Some experience with R or RStudio such as the online tutorial, Introduction to R.

   

• Merging Data: In this tutorial, we merge various data tables.

  Try an Online Tutorial

  View Student Handout (HTML) or get in RMarkdown

  Orders Dataset (.CSV)

  Customers Dataset (.CSV)

  Created by Adam Loy

  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 .

  Try an Online Tutorial

  Student Handout (HTML) or get in RMarkdown

  Ames Housing Dataset (.CSV)

  Prerequisites: Some experience with R or RStudio. The Introduction to dplyr tutorial is helpful, but not required.

  Data provided by the Journal of Statistics Education Data Archive (search Ames Housing).

   

• Data Visualization: An Introduction to ggplot2: This activity is designed to help students understand the basic components of the ggplot2 package in order to create graphs.

  Try an Online Tutorial

  View Student Handout (HTML) or get in RMarkdown

  Ames Housing Dataset (.CSV)

  Prerequisites: Some experience with R or RStudio. The Introduction to dplyr tutorial is helpful, but not required.

  Data provided by the Journal of Statistics Education Data Archive (search Ames Housing).

   

• Introduction to Creating Maps with ggplot2: This activity focuses on how to use the maps and ggplot2 packages to create basic state, country and world choropleth maps.

  Try an Online Tutorial

  View Student Handout (HTML) or get in RMarkdown

  State Population Dataset

  Prerequisites: Some experience with ggplot2 and dplyr

   

• Making Maps with Shapefiles: This activity breifly introduces shapefiles.

  View Student Handout (HTML) or get in RMarkdown

  Dataset: will be available in GitHub

  Prerequisites: Creating maps with ggplot2

   

• Manipulating Text: This tutorial introduces many of the techniques needed for working with text data.

  Try an Online Tutorial

  View Student Handout (HTML) or get in RMarkdown

  A more complete list of stringr commands (.pdf)

  Created by Laura Chihara

  Prerequisites: Some experience with R or RStudio.

• 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.

 

• 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.