Kudos to cartographers, glory to graphic illustrators, and applause to artists of the digital kind: I give thanks and praise that I don’t have to draw original data graphics by hand. (I might be the only person in America who can manage to draw a crooked bar graph using both a ruler and a level).
Modern technology enables researchers to be visual virtuosos. In a matter of seconds – seconds! – software programs can plot hundreds of thousands of data points using colors, textures, patterns, trendlines, and animations. Technology has largely eliminated the need for researchers to learn the nuanced mechanics of visual drafting, scaling, and multidimensional representation. Still, to optimize visual displays of data, it’s important to use the right graphic for the right purpose.
As a creative catalyst, I like using this handy chart about charts (hat tip: Dr. Andrew Abela). In particular, I appreciate how Abela starts explicitly with purpose: is it to show a data composition, distribution, a comparison, or a relationship? While this not an exhaustive list of visual display options, this is a great rubric for thinking about basic, familiar graphics.
Certainly, it is possible to choose the right graphic for the right purpose and still create an atrocious visual. In my next post, we’ll discuss categories of visuals that should be used with caution, and explore the rationale and science behind why.
Data Visualization Lesson 1: Examine the Y-Axis
Data Visualization Lesson 2: Think of Grandma
Data Visualization Lesson 4: The Best Pies are Desserts
Data Visualization Lesson 5: Ninth Grade Algebra Wasn’t Worthless After All
Data Visualization Lesson 6: The Ultimate List of Dos and Don’ts