Imagine that I give you the 8 numbers at left, and ask you to graph them in a display where you can flexibly uncover patterns. I use this example frequently in data visualization workshops, and the typical result is a deer-in-the-headlights look. And these are smart audiences — college undergraduates, Ph.D. students, MBAs, or business analysts. Most are overwhelmed with options: Bars, Lines, Pies, Oh My. If I instead show the data already in a visualization and ask them to replot it, the audience pivots from being overwhelmed with options, to being unable to imagine the data plotted in any other way.
Visualization quick reference guides (also known as ‘chart choosers’) are a great solution to these problems, abstracting over wonky theories to provide direct suggestions for how to represent data. These guides are typically organized by viewer tasks — does the designer want the viewer to see a ranking, examine a distribution, inspect a relationship, or make a comparison? These guides then use these tasks to categorize (or flowchart) viable alternative designs. Students and practitioners (heck, and researchers) appreciate the way that these tools help them break out of being overwhelmed with options, or fixated on a single possibility.
There are several great task-based chart choosers out there (here’s an example from the Financial Times), so why make a new one? Choosing a visualization based on task can be a helpful constraint when it’s time to communicate a known pattern to an audience. But it can be less useful at the analysis stage before that, where you have only vague notions of potentially important tasks. Early commitment to a visualization suited to a specific task might even cause you to fixate on one pattern, and miss another. And some tasks are vaguely defined. I find ‘See Relationship’ and ‘Make Comparison’ particularly fuzzy. Didn’t Tufte proclaim that everything is a comparison? For analysts, the best visualization format is typically the one that is flexibly useful across tasks, allowing general foraging through possible patterns.
But if not task, what’s another way to organize a chooser? When I decided to set up a new one, I liked the simple objectivity of picking the visualization according to the structure of the data being plotted (though I was recently delighted to be pointed to another chooser with a similar setup).
The small dataset below illustrates the typical types of quantitative data in any excel sheet: categories, ordered categories, and continuous metrics. Once you decide which columns of the dataset to throw together, the chooser (in theory) tells you the best options. I’ll walk through how it works below.
You have a pile of metrics (numbers), perhaps you’d like to bin them by discrete categories (typically, a bar graph), or maybe two categories at the same time, as in a 2-dimensional table (I like Bar Tables for this). Or perhaps you want those metrics organized along a continuous axis (another metric) as when plotting values that change over time (typically, a line graph), and then maybe you’d like to show that binned by discrete categories (typically, a line graph with multiple lines on it). If, instead of absolute values, the metrics should be interpreted as percentages, that typically entails spatially smooshing the graph into pies or stacked bars.