Design, Problem Solving, and Good Taste

Subway

Image: A Case for Spaceships (Jure Triglav)

I ran across this essay recently on the role of design standards for scientific data visualization. The author, Jure Triglav, draws his inspiration from the creation and continued use of the NYCTA Graphics Standards, which were instituted in the late 1960s to unify the signage for the New York City subway system. As the author puts it, the Graphics Standards Manual is “a timeless example of great design elegantly solving a real problem.” Thanks to the unified iconography, a traveler on the New York subway knows exactly what to look for to navigate the subway system, no matter which station they may be in. And the iconography is beautiful, too.

Unimark

Unimark, the design company that designed the Graphics Standards.
Aren’t they a hip, mod looking group? And I’m jealous of those lab coats.
Image: A Case for Spaceships (Jure Triglav)

What works to clarify subway travel will work to clarify the morass of graphs and charts that pass for scientific visualization, Triglav argues. And we should start with the work of the Joint Committee on Standards for Graphical Presentation, a group of statisticians, engineers, scientists, and mathematicians who first adopted a set of standards in 1914, revised in 1936, 1938, and 1960.

I agree with him — mostly.

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Exploring Graphical Perception in ggplot2

NewImage

A new article that I’ve posted to the Win-Vector blog, on applying principles from William Cleveland’s The Elements of Graphing Data in ggplot2.

I was flipping through my copy of William Cleveland’s The Elements of Graphing Data the other day; it’s a book worth revisiting. I’ve always liked Cleveland’s approach to visualization as statistical analysis. His quest to ground visualization principles in the context of human visual cognition (he called it “graphical perception”) generated useful advice for designing effective graphics.

I confess I don’t always follow his advice. Sometimes it’s because I don’t agree with him, but also it’s because I use ggplot for visualization, and I’m lazy. I like ggplot because it excels at layering multiple graphics into a single plot and because it looks good; but deviating from the default presentation is often a bit of work. How much am I losing out on by this? I decided to do the work and find out.

Details of specific plots aside, the key points of Cleveland’s philosophy are:

  • A graphic should display as much information as it can, with the lowest possible cognitive strain to the viewer.
  • Visualization is an iterative process. Graph the data, learn what you can, and then regraph the data to answer the questions that arise from your previous graphic.

Of course, when you are your own viewer, part of the cognitive strain in visualization comes from difficulty generating the desired graphic. So we’ll start by making the easiest possible ggplot graph, and working our way from there — Cleveland style.

Read the rest of the post here.

Enjoy.