Data Visualization Using R: A Short Course

Through a series of worked examples, you will learn how to build plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics covered include plotting continuous and categorical variables; layering information on graphics; faceting grouped data to produce effective “small multiple” plots; transforming data to easily produce visual summaries on the graph such as trend lines, linear fits, error ranges, and boxplots; creating maps, together with simpler alternatives to maps for country – or state – level data.

We will also cover cases where we are not working directly with a dataset but rather with estimates from a statistical model. Using these tools, we will then explore the practical process of refining plots to accomplish common tasks such as highlighting key features of the data, labeling particular points, annotating plots, and changing their overall appearance. Finally, we will examine some strategies for presenting graphical results in different formats (such as in print, online, or in slides) and to different sorts of audiences.

At the end of the course, you will:

• Understand the basic principles behind effective data visualization.
• Have a practical sense for why some graphs and figures work well while others may fail to inform or actively mislead.
• Know how to create a wide range of plots in R using ggplot2.
• Know how to refine plots for effective presentation.

To participate in the hands-on exercises, you are strongly encouraged to use a laptop computer with the most recent version of R installed, together with the tidyverse library. You are also encouraged to download and install RStudio, a front-end for R that makes it easier to work with. This software is free and available for Windows, Mac, and Linux platforms. Basic familiarity with R is highly desirable, but even novice R coders should be able to follow the presentation and do the exercises.

If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.

This course is for anyone who wants to learn how to produce, refine, and present effective visualizations generated from datasets, summary tables, or the output of statistical models.

1. Basic principles of data visualization

• Why look at data?
• Beyond “good taste” in graphics
• Object perception and misperception
• Encoding data graphically

2. Using ggplot2 and R

• How to think about R
• How to think about ggplot

3. Understanding the grammar of graphics

• Mapping data values to plot aesthetics
• Building plots layer by layer

4. Plots of one, two, or more continuous or categorical variables

5. Grouped data, Faceting, and Small Multiples

6. Plots of estimates and effects

• Plotting tables of results
• Plotting directly from models

7. Plots in space and time

• Drawing maps, and their alternatives
• Animating plots

8. Refining plots for presentation

• Adding annotations or highlighting features
• Keys and labels
• Controlling overall appearance with themes
• Redrawing bad plots

“The instructor is personable and droll. The effort put into his slides and his “story” is incredible, and shines through on every slide. He has thought through every slide (even digressions), and so he has firm command of the direction he wants to move his class in. Obviously, he is beyond expert to elite-level in his knowledge of this domain, and the set of worked examples he has provided will serve students in good stead for a long time.”
  Michael Marsiske, University of Florida

“I really enjoyed the ability to see what the instructor was doing (e.g., his keystrokes and the way he could use a spotlight to show where his mouse was). The flow of the topics was very natural and logical, and the instructor’s presentation style was very engaging and easy to follow.”
  Alicia Jaramillo-Underwood, Centers for Disease Control and Prevention

“I appreciated the concrete examples of high and low quality data visualization and continuous rationale throughout. I could tell the instructor was very knowledgeable.”
  Laura Faith, HSR&D

“This was a fantastic course all around! The instructor was very knowledgeable and kept the content/pace both interesting and engaging. It was really great to dive into working with code and examples.”
  Tim Hannigan, University of Alberta

“The fundamental approach that explained what inputs ggplot is looking for, as well as decoding all the different potential inputs in a stepwise fashion, was an incredible way to learn, and also will help when I come back later.”
  Atu Agawu, Children’s Hospital of Philadelphia