BMI 5/625, Spring 2024

Other Resources

Books of Interest

Visualization

(In no particular order)

• William Cleveland, The Elements of Graphing Data
• Colin Ware, Information Visualization: Perception for Design
• Stephen Kosslyn, Graph Design for Eye and Mind
• Tamara Munzer, Visualization Analysis and Design
• Claus Wilke, Fundamentals of Data Visualization
• Nathan Yau
  • Data Points
  • Visualize This
• Stephen Few, Now You See It: Simple Visualization Techniques for Quantitative Analysis
• Alberto Cairo
  • The Functional Art: An Introduction to Information Graphics and Visualization
  • The Truthful Art: Data, Charts, and Maps for Communication
• Katy Börner, Visual Insights: A Practical Guide to Making Sense of Data
• Joel Katz, Designing Information: Human Factors and Common Sense in Information Design
• CLaus Wilke, Fundamentals of Data Visualization
• Manuel Lima
  • Visual Complexity: Mapping Patterns of Information
  • The Book of Trees: Visualizing Branches of Knowledge
• Edward Tufte
  • The Visual Display of Quantitative Information
  • Envisioning Information
  • Visual Explanations
  • Beautiful Evidence

Related Topics

• Maureen Stone, A Field Guide to Digital Color
• Rudolf Arnheim, Visual Thinking
• Scott McCloud, Understanding Comics: The Invisible Art

R

• Chester Ismay & Albert Y. Kim, Statistical Inference via Data Science: A ModernDive into R and the tidyverse
• Hadley Wickham & Garrett Grolemund, R for Data Science

Additional Readings

Papers

1. Brehmer M, Sedlmair M, Ingram S, Munzner T. Visualizing Dimensionally-reduced Data: Interviews with Analysts and a Characterization of Task Sequences. Proc. Beyond Time & Errors: Novel Evaluation Methods For Information Visualization (BELIV) 2014. p. 1–8.
2. Borland D, Taylor MR. Rainbow color map (still) considered harmful. IEEE computer graphics and applications. 2007 Mar;27(2):14–7.
3. Christensen DL, Baio J, Van Naarden Braun K, Bilder D, Charles J, Constantino JN, et al. Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years–Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2012. MMWR Surveillance summaries : Morbidity and mortality weekly report Surveillance summaries / CDC. 2016 Apr 1;65(3):1–23.
4. Cleveland WS, McGill R. Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods. Journal of the American Statistical Association. 1984;79(387):531–54.
5. Cleveland WS, Diaconis P, McGill R. Variables on Scatterplots Look More Highly Correlated When the Scales Are Increased. Science; 1982;216(4550):1138–41.
6. Dörk M, Feng P, Collins C, Carpendale S. Critical InfoVis: exploring the politics of visualization. Proc. ‘CHI 2013.
7. Elliott, K. “39 studies about human perception in 30 minutes”; presented at OpenVis 2016.
8. Heer J, Bostock M. Crowdsourcing graphical perception: using mechanical turk to assess visualization design. ACM CHI ‘10; 2010. p. 203–12.
9. Kosara R. An Empire Built On Sand: Reexamining What We Think We Know About Visualization. in the Proceedings of the Sixth Workshop on Beyond Time and Errors on Novel Evaluation Methods for Visualization (BELIV ‘16) 2016. p. 162–8.
10. Krzywinski M, Altman N. Points of significance: error bars. Nat Meth. Nature Research; 2013 Oct;10(10):921–2. 
11. Krzywinski M, Altman N. Visualizing samples with box plots. Nat Meth. 2014 Feb;11(2):119–20.
12. Shneiderman B. Research agenda: Visual overviews for exploratory search. Information Seeking Support Systems. 2008;11:4. 
13. Sedlmair M, Meyer M, Munzner T. Design Study Methodology: Reflections from the Trenches and the Stacks. IEEE Transactions on Visualization and Computer Graphics. 2012 Dec;18(12):2431–40.
14. Sedlmair M, Tatu A, Munzner T, Tory M. A Taxonomy of Visual Cluster Separation Factors. Computer Graphics Forum. 2012;31(3pt4):1335–44. 
15. Sedlmair M, Munzner T, Tory M. Empirical Guidance on Scatterplot and Dimension Reduction Technique Choices. IEEE Transactions on Visualization and Computer Graphics. 2013 Dec;19(12):2634–43.
16. Weissgerber TL, Milic NM, Winham SJ, Garovic VD. Beyond bar and line graphs: time for a new data presentation paradigm. PLoS Biol. Public Library of Science; 2015 Apr;13(4):e1002128.

Blog posts, etc.

1. The Why Axis’s “Mind the Gap: An Economic Chart Remake”, by Jon Schwabish
2. Notes on the Arabic FedEx logo.
3. Jenny Cham’s delightful “sketchnotes” from a 2014 seminar by the incomparable Tamara Munzner.
4. Randy Olson’s illustration of Small Multiples vs. Animated GIFs.
5. Visual.ly’s Color Emotion Guide
6. I am very interested in William Playfair and the history of data visualization:
   • Atlas Obscura, The Scottish Scoundrel Who Changed How We See Data
   • Lauren Klein, Repairing William Playfair at the MLA
   • Georgia Tech’s Digital Humanities Lab, Repairing William Playfair
7. Neil Richards, When is a visualisation a call to action?
8. Carl Zimmer (yes, the Carl Zimmer) on genome graphs: As DNA reveals its secrets, scientists are assembling a new picture of humanity, with numerous examples of creative new ways to visualize genetic variation.
9. Ben Jones, “From Simple Tables to Multiple Vizzes”: an illustration of ten different ways to visualize the same table. What different stories pop out from the different visualizations?
10. Kristoffer Magnusson, How to tell when error bars correspond to a significant p-value
11. Kyle Walker, Should you visualize data with a map or a chart? Thoughts from teaching introductory geography
12. Lazaro Gamio (Washington Post), Election maps are telling you big lies about small things
13. Cartonerd, The NYT election map
14. vis4.net, Why we didn’t use a cartogram in the Brexit map
15. Benjamin Hennig, Cartogram Special – Brexit
16. Tom Pearson (Financial Times), Communicating with data — How the FT explained Brexit: “From early sketch to final dashboard, FT data visualisation experts explain the process”

Websites of Note

• Visualising Data’s “Resources” index’
• Matt Brehmer’s Monster List Of Resources (from UBC)
• OHSU Communications Office (Graphic Design services, etc.)
• HelpMeViz (crowdsourced data visualization help)
• Blogs:
  • The Functional Art
  • FlowingData
  • The Why Axis
  • eagereyes
  • Visua.ly’s blog
  • Junk Charts
  • WTF Visualizations
  • setosa.io (nifty d3 demos!)
• References & Examples
  • R Graph Compendia/Catalogs:
    • A Compendium of Clean Graphs in R, by Eric-Jan Wagenmakers and Quentin F. Gronau (note: in “Base R”)
    • R Graph Catalog, by Joanna Zhao and Jennifer Bryan (note: in ggplot2)
  • Markdown Tutorial
  • Summer 2016 ggplot OHSU Data Jamboree
  • A useful tutorial on using the Seaborn Python library for visualizing distributions.
  • A Dramatic Tour through Python’s Data Visualization Landscape (including ggplot and Altair)
  • The Washington Post’s “demographic tug-of-war” visualization is a lovely illustration of how not to appropriately use color saturation and hue to encode information, and also has misleading axes (both in unit spacing and direction)!
  • The New York Times’ interactive “The 1,024 Ways Clinton or Trump Can Win the Election” is a great use of interactivity as a data exploration tool.
  • Also from the NYT, “Your Surgeon Is Probably a Republican, Your Psychiatrist Probably a Democrat” makes excellent use of a graphs and charts to tell a very clear story. Also note their detailed description at the end of the article of how the data were collected!
  • The Washington Post’s “One Hundred Years of Hurricanes” is a nice example of small-multiples cartography along with color.
  • The New York Times’ “What Good Marathons and Bad Investments Have in Common” illustrates a good use of contrast and preattentive processing (plus is a very interesting story!).
  • From Our World In Data, a nice example of using color and saturation to highlight certain information, in a terrifying plot about Life Expectancy vs. Health Expenditure Over Time.
  • Myriahedral map projections are interesting.
  • Tilegrams are a good alternative to a cartogram.
  • We do not recommend making bivariate chloropleth maps, but if you must, here’s a good tutorial on how to do it and what some of the considerations are.
• Resources & Tools:
  • ColorBrewer
  • Adobe Kuler
  • Colorgorical
  • Photochrome
  • Lyra
  • Packages:
    • janitor
    • Vincent (Python-to-Vega generation library)
    • mpld3, an implementation of matplotlib on top of d3.js.
  • Are we missing a favorite of yours? Let us know!