CSE 631: Principles & Practice of Data Visualization

This course will give students a foundation in the principles and practice of data visualization, particularly as applied to scientific and technical data. We will have weekly lectures, covering a wide variety of topics including human perception, color theory, principles of visual design, etc. We will also have weekly hands-on laboratory sessions in which students will have the opportunity to put the lecture material into practice.

Lab sessions will largely use the open-source R and ggplot tools, though others will be featured as well.

We taught a much smaller version of this course back in 2014; as before, we guarantee that students taking this class will see a positive impact on evolutionary fitness.

Tuesdays (lectures) & Thursdays (labs), 16:00 – 17:30

Note: Topics and speakers may change through the term as we adjust our course plan.

Week Date Topic Materials Readings
1 9/27/16 Introduction: data/ink, why graphs Slides  
1 9/29/16 LAB: R, basics of ggplot 01-intro_lab.html, 01-intro_lab.Rmd, 2016 ggplot Data Jamboree materials  
2 10/4/16 Visual perception Jackie's Slides | Steven's Slides Cleveland & McGill 1984, Heer & Bostock 2010, Cleveland et al. 1982, Elliott 2016, Kosara 2016
2 10/6/16 LAB: How to tweak things in ggplot 02-ggplot-tweaks.html (Rmd), addm-since-2006.csv (citation)  
3 10/11/16 Color Slides  
3 10/13/16 LAB: Color palletes, save output (PDF/PNG/etc) 03-ggplot_colors.Rmd, 03-ggplot_colors.html, Steve's ggsave slides  
4 10/18/16 Graphs Jackie's Slides | Steven's Slides  
4 10/20/16 LAB: visualizing distributions (box plots, etc.) & error bars
  1. 04-lab (html | Rmd)
  2. Posid, et al. 2015
  3. janitor package
  4. Magnusson 2012
Weissgerber 2015, Krzywinski 2013, Krzywinski 2014
5 10/25/16 Fonts & Tables Fonts | Tables  
5 10/27/16 LAB: tidyr, pixidust 05-lab.Rmd, 05-lab.html; dplyr Babynames Drill  
6 11/1/16 Maps Slides Gamio 2016, Cartonerd 2016, vis4 2016, Hennig 2016
6 11/3/16 Lab: TBD    
7 11/8/16 Networks & Trees Slides  
7 11/10/16 LAB: Robin, Sci2 druker.cleaned.scopus  
8 11/15/16 Multiple dimensions & Dimensionality Reduction Slides Sedlmair et al. 2012, Sedlmair et al. 2013, Brehmer et al. 2014
8 11/17/16 LAB: high dimensional data viz (heatmaps, parallel coords, etc.) html, Rmd  
9 11/22/16 Ted Laderas: Lecture and Lab combo Git Repo  
9 11/24/16 Thanksgiving - No Class    
10 11/29/16 HW presentations; Copyright    
10 12/1/16 Ethics Slides  
11 12/6/16 Guest Lecture: Dr. Marie-Pierre Hasne, "Scientific Representations: A Philosophical Perspective" Slides  
11 12/8/16 Snowpocalypse 2016    
12 12/13/16 Final presentations!    
12 12/15/16 Final Presentations!    

There will be regular homework assignments, typically as part of lab sessions. There will also be a final term project; more details about which will be forthcoming.

Assignment 1

Find two example visualizations (one positive, one negative) from your field; explain why you consider them to be positive or negative. Send your examples to Steven (with "[CS631]" in the subject line) by 10/6.

Assignment 2

This assignment has several parts:
  1. Find two example visualizations illustrating both positive and negative use of preattentive features or Gestalt behavior. These can be two additional visualizations, or can be fresh interpretations of your examples from Assignment 1.
  2. Pick a plot that you are currently working on, and discuss why you chose the current format, what tools you are using, and what motivated your choice of format and tool.

    Note: there is no "right" or "wrong" answer for this! The purpose of this part of the assigment is to a) give you practice in thinking analytically about visualization, and b) help us learn about what your current visualization practice looks like so that we can tailor our labs and lectures accordingly.

  3. For Thursday, 10/13: Bring in the data that you are thinking of using for the final project. We will look at it with you during the Thursday lab, so we (and you!) can start thinking about how much data cleaning and wrangling will need to be done.

Parts 1 and 2 should be completed by the start of business Monday, 10/10 (so that we have time to collate everybody's responses before class). Part 3 will be for class on 10/13.

Assignment 3

Pick a journal from your field, and find their submission requirements for figures. Do they specify a file format? Raster vs. vector? Resolution? Colors?

Assignment 4

In week 4's lab session on visualizing distributions, we began working with data from the article by Posid et al., but as we discovered, an hour wasn't nearly enough time to explore that data set. For this assignment, you will continue working with that data. Please prepare the following:

  1. A prose description of the question you want to answer, comparison you want to facilitate, difference you want to highlight, etc.
  2. A visualization that addresses that question, comparison, etc., along with an explanation of why you designed it the way you did.
By Thursday, 10/27: send us your writeup and visualization.

Assignment 5

Find a map that predicts the election! Choose an interesting set of circumstances (e.g. if only Men voted); detail what those circumstances are. How was the data gathered, cleaned and filtered? Was the data manipulation ethically sound?

Discuss how the data was visualized. Did they use a choropleth map? Symbolic map? Was the representation ethically sound?

Assignment 6

An investigator carried out a survey about parents' level of knowledge regarding a variety of disorders, stratified by ethnicity. She comes to you asking for help visualizing the data; while the data set is not particularly large, there are a several different variables at work.

In this assignment, you will develop a scientific question about this data, and then design & build a visualization that addresses that question. The first step (developing the question) is the most important, and will guide the second step. Note that this is real data, so please do not share it with others.

To complete this assignment, prepare three PowerPoint slides, following this structure:

  1. Briefly describe the scientific question you want to answer
  2. Briefly describe the goals of your visualization: what relationship/difference/trend/etc. you wish to illuminate, what visual encodings you will use, and so on.
  3. Your visualization itself
If you wish to include multiple versions, or show how your visualization evolved as you worked with the data, you may include this information in a fourth slide.

In addition to your slides, please also include an Rmd file (if applicable) containing the code you used to generate your figure.

This assignment is due Tuesday, November 29. We hope to share your results with the original investigator, so please do your best work and try not to be late. :-)

We will be drawing material from a wide variety of sources for this course; as such, there is no single, required text book per se. However, we will frequently refer to Nathan Yau's Data Points, and highly recommend that anybody taking this class have a copy. Additionally, many lab sessions and some assignments will use exercises from Nathan Yau's Visualize This.

Useful books

Related Topics

Websites of note

Alison Hill


Jackie Wirz


Steven Bedrick


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