Although geared primarily toward the production of static graphics for print publications, Dona M. Wong’s The Wall Street Journal Guide to Information Graphics (2010) provides a wealth of salient and time-honored tips and guidelines that any student of data visualization would be well-advised to follow. At the heart of Wong’s book is the notion that data integrity trumps all else, and no matter how aesthetically pleasing or visually powerful an information graphic may be, if it doesn’t communicate clear and accurate data to the reader/user, it doesn’t do its job.
In the first two chapters of The WSJ Guide, Wong, a former student of data viz extraordinaire Edward Tufte, addresses the topic of charting. From a theoretical standpoint, Wong lays out four principle steps to the charting process:
- Research: Find your data source, and ensure that it’s timely, authoritative and free of bias.
- Edit: Figure out what the data says (essentially, determine what your story is), and conceive of how best to boil that data down in a way that’s simple enough for your intended audience to understand without skewing its meaning.
- Plot: Determine the appropriate chart type for your data (e.g. bar, column, line, pie, stacked bar, etc.), choose the right settings (scale, increments, axes, etc.), labeling the chart (e.g. legends and source lines) and pick the best color and typography combinations to accentuate your key message.
- Review: When you’re done, ask yourself the following questions: Does the data match up with what external sources say? Are there any outliers? Does the chart make sense? What would the average user/reader think upon first seeing the chart?
Regarding the finer points of charting, Wong does an excellent job at pointing out the various dos and don’ts of the presentation process. She sets forth clear guidelines about when to use what type of chart. For example, when dealing with change over time, Wong says to always use a line chart instead of a bar chart, as bar charts should ideally be reserved for comparing several different series of data. Also, Wong asserts, pie charts usually aren’t as good of a choice for displaying complex data as bar or line charts, primarily because they make it harder to discern discrete differences in size (later, she flat-out dismisses the donut pie chart for the same reason). A few of her other tips I found particularly relevant included: (a) avoiding high-contrast color schemes that draw attention away from the data, (b) shying away from icons with high detail so as to avoid visual overload, (c) never, under any circumstances, add cloying shadow or 3D-effects and (d) never rely on zebra patterns, dotted lines or other fancy methods of labeling. “A chart is not a piece of fine art,” Wong says.
Most importantly, Wong sets forth some other general principles to help designers avoid creating misleading charts. For example, when creating a bubble chart, always plot the bubbles by area, not radi. Also, never plot two different data series on noncomparable scales, and when creating bar charts, always start at the zero baseline. Other steps to ensuring data integrity include putting numbers into their appropriate context (comparing apples to apples), holding off on rounding until the end of the data analysis process and avoiding charting predictive numbers alongside actual ones. As Wong so eloquently puts it, “Unlike a misspelled word in a story, one wrong number discredits a whole chart.”
Although the new addition of interactivity to chart design adds another layer of complexity to the visualization process that Wong doesn’t address here, most of the guidelines she sets forth hold true in both static and dynamic mediums. Yet it would be interesting to hear what she has to say about the vexing question of when and when not to add static labels to interactive charts…