Staring bleary-eyed at a spreadsheet of data and a report that still needs visual content, have you ever felt a bit underwhelmed at the prospect of using a simple graphic?
Sure, a bar graph or histogram or line chart would show the data… but sometimes they seem, um… well… a little boring?
It’s easy to be allured by the visual feast of data presentation options. When choosing visual representations of data, it’s tempting to forgo standard graphic fare for goodies we think will be more satisfying to the aesthetic palate.
Which would you choose – a bowl of raw shredded coconut or a slice of coconut cream pie?
However, in some cases the best pies are desserts, not charts.
In this post, we’ll discuss several examples of ways to represent data – including pie charts, figures, and maps – that can be extremely effective in the right circumstances. But it’s worth understanding in what situations they can be difficult for viewers to digest.
Pie Charts. Pie charts can be used to great effect when comparing a small number of categories (between 2 and 4) that make up a whole. But beyond four categories, pie charts become difficult for viewers to interpret easily and quickly. Why? First, viewers have to ascertain which color goes with which category, then assign a relative percentage to it, and remember this information relative to other categories before the whole picture makes sense. To show comparisons among four or more categories, a bar graph or simple table conveys the same information in a more visually straightforward way.
Pictographs / 1-D, 2-D, 3-D Figures. Darrell Huff’s classic text How to Lie With Statistics offers an excellent overview of the potential perils of using pictographs and figures to compare data. Cognitive research shows we are prone to systematically underestimate some visual dimensions.
For example, horizontal lines are usually perceived as shorter than vertical ones, even if they are actually the same length, and the human eye also has a difficult time accurately gauging area and volume from many graphs.
Pictographs and figures can be extremely effective in illustrating relative magnitudes when the vertical and horizontal proportions of the figure are consistent and include a clearly labeled baseline. For a terrific read on the psychology of data visualization, check out Graph Design for the Eye and Mind by Stephen Kosslyn.
Maps. Geographic data + other data = intense temptation to map. Plain and simple, the conclusions data consumers will reach largely depend on how specific data categories are within the map.
Take for example the familiar “Red State/Blue State” maps that seem to be everywhere in recent presidential election years. Based on the simple dichotomous color categories, it would be relatively easy for a casual viewer to infer that states (and their residents) are wholly Republican/conservative or Democratic/liberal. By contrast, these maps by Dr. Robert Vanderbei use presidential election returns data at a different level of specificity (county-level rather than state-level), which elicits a more nuanced impression. The best maps make data easy for viewers to interpret but resist overstating or oversimplifying.
In my next post, we’ll discuss how to present the results of fancy statistical tests using clear, straightforward graphics (the culinary equivalent: putting kale into chocolate cake and having everyone ask for the recipe).
Stay tuned – it’s sure to be delicious!
Data Visualization Lesson 1: Examine the Y-Axis
Data Visualization Lesson 2: Think of Grandma
Data Visualization Lesson 3: Abela’s Rubric
Data Visualization Lesson 5: Ninth Grade Algebra Wasn’t Worthless After All
Data Visualization Lesson 6: The Ultimate List of Dos and Don’ts