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Something is Rank in the State of Data

Determining the best way to visualize ranked lists.

Exports and imports of Scotland from Christmas 1780 to Christmas 1781. From “The Commercial and Political Atlas”, 1786 (3rd edition, 1801) by William Playfair.

(Note: This article was originally published in Sparks of Innovation: Stories from the HCIL.)

In 1786, William Playfair (1759–1823) invented the bar chart, which conveys values using the length of a rectangle. He did this to help members of the British parliament — many of them illiterate — understand complex data without the need for actual numbers. The bar chart has since become one of the most prolific and familiar types of statistical data graphics, and is a staple in many infographics. Bar charts are commonly used to visualize many items side by side, such as the gross domestic product of countries, the unemployment rate in U.S. states, or the enrollment in different academic units at a university. Such lists are often sorted, and we thus refer to them as “ranked lists” and their visualization as “ranked-list visualization.”

Horizontal bar chart showing a sorted list of 150 countries (scroll bars are used to see the full visualization).

However, while bar charts remain the dominant form of ranked-list visualization, they have an important drawback. As the picture above suggests, showing a long ranked list typically requires scrolling, as the whole list won’t easily fit on screen at the same time. For this reason, visualization experts have in recent years proposed several alternatives to bar charts.

Treemap showing a list of 150 countries, where the surface area of each rectangle corresponds to its value. Blue values are positive, red are negative.

For example, treemaps were originally designed for hierarchies, but are often used for ranked lists. In fact, their popularity is somewhat surprising since assessing the area of a rectangle is known to be more difficult than its length.

Packed bubble charts use circles that are packed tight, their area conveying the value of each item. However, their layout is entirely random.

Wrapped bars of 150 items. Positive values are blue, negative red.
Packed bars that only show the largest (blue) and smallest (red) values in the center around the origin. Additional bars (gray) are packed into the same rows to the left and right of the original two columns.
Piled bars for 150 values. Red hues are used for negative values and blue for positive. All bars use a common baseline, but are arranged so that larger bars are drawn behind smaller bars.
Zvinca plot for 150 values. Blue dots are positive, red are negative.

In summary, our recommendation is that designers pick simple bar charts if accuracy is more important than speed, that treemaps can be surprisingly effective (especially for large datasets), and that wrapped bars provide the best middle ground between accuracy and completion times.


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