Table Design Principles

Let’s begin with some principles of good table design, similar to how we learned about chart design in Chapter 6 and map design in Chapter 7. Jonathan Schwabish, an economist who specializes in creating policy-relevant data visualizations, offers his advice in recent publications about creating tables that communicate well with multiple audiences.14. Here’s a summary of several of his key points, which also appear in Figure 8.1.

  1. Make column headers stand out above the data.
  2. Use light shading to separate rows or columns.
  3. Left-align text and right-align numbers for easier reading.
  4. Avoid repetition by placing labels only in the first row.
  5. Group and sort data to highlight meaningful patterns.
A sample table that illustrates selected design principles.

Figure 8.1: A sample table that illustrates selected design principles.

In addition, Schwabish and others recommend using color to highlight key items or outliers in your data, a topic we’ll discuss later in Chapter 15: Tell Your Data Story.

Overall, the core principles of table design reflect similar concepts we previously discussed in chart and map design. Organize your presentation of the data with the readers’ eyes in mind, to focus their attention on the most important elements of the story, so that they “take away” the most meaningful interpretation of the information. Do the visualization work for them, so that you don’t have to rely on them to draw the same mental connections in their own minds. Remove any clutter or unnecessary repetition that stands in the way of these goals.

Now that you understand key principles of table design, see how several of them are built directly into the Datawrapper tool featured in the next section.


  1. Jon Schwabish, “Thread Summarizing ’Ten Guidelines for Better Tables’,” Twitter, August 3, 2020, https://twitter.com/jschwabish/status/1290323581881266177; Jonathan A. Schwabish, “Ten Guidelines for Better Tables,” Journal of Benefit-Cost Analysis 11, no. 2: 151–78, accessed August 25, 2020, https://doi.org/10.1017/bca.2020.11; Jonathan Schwabish, Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks (Columbia University Press, 2021), https://cup.columbia.edu/book/better-data-visualizations/9780231193115↩︎