I’ve just finished teaching the Fall 2015 session of 36-721, Statistical Graphics and Visualization. Again, it is a half-semester course designed primarily for students in the MSP program (Masters of Statistical Practice) in the CMU statistics department. I’m pleased that we also had a large number of students from other departments taking this as an elective.

For software we used mostly R (base graphics, ggplot2, and Shiny). But we also spent some time on Tableau, Inkscape, D3, and GGobi.

We covered a LOT of ground. At each point I tried to hammer home the importance of legible, comprehensible graphics that respect human visual perception.

Remaking pie charts is a rite of passage for statistical graphics students

My course materials are below. Not all the slides are designed to stand alone, but I have no time to remake them right now. I’ll post some reflections separately.

Download all materials as a ZIP file (38 MB), or browse individual files:

• Syllabus and Suggested Readings list; our required texts were Cairo’s The Functional Art and Donahue’s Fundamental Statistical Concepts in Presenting Data
• Homeworks:
  • HW1 Legible Graphics
  • HW2 Visual Perception, dataviz to critique, reduced dataset, full dataset
  • HW3 Grammar of Graphics
  • Critique, submission form
• Projects:
  • P1 Graphic Design
  • P2 Interaction Design
  • P3 Research
  • Project Ideas list, Rdata version of Haiti dataset
• Lectures:
  • 01 Introduction slides
  • 02 Legible Graphics slides, R code, R output, addendum
  • 03 Visual Perception slides, R code, R output
  • 04 Grammar of Graphics slides, R code, R output
  • 05 Graphic Design slides, example component graphs (STEM, NonSTEM, Business) and layout
  • 06 Interaction Design slides, Shiny code and data, D3 code and data
  • 07 Visualization Research slides
  • 08 Shiny Lab Session slides
  • 09 Graphics for Statistical Analysis slides, R code, R output, data (tips, ganglion)
  • 10 Mapping slides, R code, R output, data (MT, PA, USA)
  • 11 High-Dimensional Data slides
  • 12 Networks and Trees slides
  • 13 Wrap-up slides, R code, R output
  • NHANES extract dataset used in several lectures

Please note:

• The examples, papers, blogs and researchers linked here are just scratching the surface. I meant no offense to anyone left out. I’ve simply tried to link to blogs, Twitter, and researchers’ websites that are actively updated.
• I have tried my best to include attribution, citations, and links for all images (besides my own) in the lecture slides. Same for datasets in the R code. Wherever I use scans from a book, I have contacted the authors and do so with their approval (Alberto Cairo, Di Cook, Mark Monmonier, Colin Ware, & Robin Williams). However, if you are the creator or copyright holder of any images here and want them removed or the attribution revised, please let me know and I will comply.
• Most of the cited books have an Amazon Associates link. If you follow these links and buy something during that visit, I get a small advertising fee (in the form of an Amazon gift card). Each year so far, these fees have totaled under $100 a year. I just spend it on more dataviz books