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Feb

07

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Cleaning Up Your Charts

Posted by on February 7th, 2014 Posted in: Data Visualization, News, Practical Evaluation


So how are those New Year’s resolutions going?

Many of us like to start the year resolving to clean up some part of our lives. Our diet. Our spending habits. The five years of magazine subscriptions sitting by our recliner.

Here’s another suggestion: Resolve to clean up “chart junk” in the charts you add to PowerPoint presentations or written reports.

Now I can pack information into a bar chart with the best of them. But it is no longer in vogue to clutter charts with data labels, gridlines, and detailed legends. This is not just a fashion statement, either. Design experts point out that charts should make their point without the inclusion of a bunch of distracting details. If the main point of your chart is not visually obvious, you either have not designed it correctly or you are presenting a finding that is not particularly significant.

So the next time you create a chart, consider these suggestions:

  • Use your title to communicate the main point of the chart. Take a tip from newspaper headlines and make your title a complete sentence.
  • Don’t use three-dimensional displays. It interferes with people’s comprehension of charts.
  • Ditch the gridlines or make them faint so they don’t clutter the view.
  • Use contrast to make your point. Add a bright color to the bar or line that carries the main point and use gray or another faint color for the comparison bars or lines.
  • Be careful in picking colors. Use contrasting colors that are distinguishable to people with colorblindness. If your report is going to be printed, be sure the contrast still shows up when presented in black-and-white.
  • Consider not using data labels, or just label the bar or line associated with your main point.
  • Remove legends and apply legend labels inside the bars or at the end of lines.

For more comprehensive information on eliminating chart junk, check out this article:

Evergreen S, Mezner C. Design principles for data visualization in evaluation. In Azzam T, Evergreen S. (eds). Data visualization, part 2. New Directions in Evaluation. Winter 2013, 5-20.

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