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The purpose of data visualization is to present information graphically in such a way that it is clear, efficient and easy to understand. Achieving this goal will depend largely on the story we want the data to tell and how it is told.
In an article by Jonathan A. Schwabish, published in the Journal of Economic Perspectives, the author mentions a series of basic principles to present economic information visually so the story we are being told is quickly understood. These principles can be summed up as:
Show the data: for the viewer to understand the story told by the chart, we have to show the data that it is based on. This does not mean that we have to show all the data; only those that are needed to understand the story.
Remove any distractions: avoid using an unnecessary amount of colors, different fonts, too many visual elements, marks, notes, etc.
Combine the text and charts: the text should not be a separate element that describes the elements that appear in the chart. Ideally, it should complement the visual elements and be completely integrated in the visualization.
On the other hand, Edward Tufte, professor of statistics and political science at Yale University and a well-known name in the field of data visualization, along with Stephen Few, provided a list of key points for the generation of data visualization. The list was originally published in their book The Visual Display of Quantitative Information, published in 1983, and a summary can be found in this Wikipedia entry.
If we ignore these principles, our visualizations will be confusing, overloaded and ambiguous.
Next, we will show some examples of poor visualization and the correction proposed by Schwabish in his article.
This first example combines several poor practices in an attempt to associate economic data with educational data (the use of different colors without a defined criterion, a vertical scale that does not begin with 0, text separated from the chart)
The following visualization corrects these problems and tells the story in a manner that is visually more attractive and direct
The next example shows the poor use of pie charts for the comparison of economic data
The quantitative comparison of the amounts shown is complicated at first view. In addition, there are texts of different sizes spread out throughout the chart. The correction below tells the same story much more efficiently
It is now very easy to compare the amounts visually. Moreover, the text has been moved and is now easier to read, while the percentage symbols have been removed and their interpretation has been included in the heading.
More examples of poor data visualizations can be found at this link. And some examples of very good visualizations can be found here.
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