Infrequent plot types: gauge, mosaic, and word cloud

Gauge charts typically show up in dashboards. We spotted a gauge chart on Berwin Leighton ArbDiversity 2016 [pg. 16]. All it really tells the reader is that 93% of the respondents believe arbitrator expertise is important. One downside of gauge charts is that they while they stress an important piece of data, they leave out quite a bit more data. Bear in mind also that a bar chart can convey the same information. And, since it takes quite a bit more code to produce this seemingly-simple gauge, a practical person would stick with the good ‘ol bar chart.

Pepper Hamilton PrivateFunds 2016 [pg. 7] offers a variant of a mosaic plot. Mosaic charts take data points, convert them into percentages and map them as a boxy bar chart. Some people describe mosaic charts as variable-width stacked column charts. The chart type goes by many other names: marimekko chart, matrix chart, stacked spinogram, spineplot, olympic or submarine chart, Mondrian diagram, or even just mekko chart. The rectangles in it completely fill the space and their volumes are proportionate. This particular specimen more resembles an area plot where the volumes of the rectangles are proportionate to their percentages, but they don’t fill the larger rectangle. As a side observation, it seems odd to tilt the label in the upper left box and odder still to drop down the 5% label.

Clifford Chance Crossborder 2012 [pg. 22] nestles a !word cloud plot in the lower right corner. A word cloud presents text only. The size of each word corresponds to its relative frequency. The configuration and location of the words has no meaning, but generally the largest words — the most frequent — sit toward the middle. Nor does the color scheme convey information. Before you can produce a word cloud you have to do a fair amount of massaging the text, such as dropping unimportant words, lower-casing words, and (often) stemming words.

Rarely-seen plots in law firm survey reports

Having considered the most common types of plots, we turn to rarely-seen types. In this first set, we will see examples of bump, segment and parliament plots.

Morrison Foerster GCDisruption 2017 [pg. 7] introduces a bump chart (also known as a slope graph, bipartite graph or Tufte chart). A typical bump chart has two columns of data. Lines extend from points on the left column to their counterpart on the right column. The slope of those lines convey the degree of change. Here, only two of the five issues changed position, and this unusual chart type emphasizes the change. Any time there is a change of rankings or position year-over-year, a bump chart might be pressed into service.

Pepper Hamilton PrivateFunds 2016 [7] gives an example of a waffle plot. These are essentially square pie charts, but instead of wedges of a circle, groups are represented by sets of squares. Waffle plots shine for showing parts-to-whole contributions, highlighting the individual points that make up the larger whole. They are problematic when exact percentages are vital.

This waffle chart breaks assets under management (AUM) of the participants into six ranges. It colors the number of rectangles in the plot in proportion to how many respondents are in each range. So, for example, since there is only one block, in the lower right, colored blue and matching the legend of “More than $10bn,” only one percent of the respondents had that amount of assets under management.

A segment plot, such as the one seen in Morrison Foerster GCsup 2017 [pg. 9], conveys ranges of data. In the example, the left end of the segment starts at the percentage who spend “Substantial Time” on an issue and ends on the right at the percentage who regard it as “Very Important.” Thus, at a glance the reader can compare positions and values for all five issues.

Ranges are really bar charts that do not have a single lowest point on an axis; they are “floating” bars that extend perpendicular to levels on the y axis and extend for different amounts (lengths) on the x axis.