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Data Visualization: Best Practices

This guide provides an introduction to best practices, tools, and educational resources for data visualization.
Last Updated: Apr 25, 2022 3:57 PM

Choosing a Chart Type

Selection of graphs from the Data Visualization Catalogue

While some types of charts are more commonly used than others - such as bar charts, line charts, and scatterplots - there are many types of graphs available to choose from. Your choice of a particular chart type for your data visualization may be constrained by the type of data you are working with or the number of variables you want to graph, or you may want to choose your chart type based on what type of pattern you are trying to show (comparison, part-to-whole, hierarchy, etc.). Here are several interactive resources to help you decide on a chart and, in most cases, find a tool to create it.


  • The Data Visualisation Catalogue

    The Data Visualisation Catalogue is a multi-lingual catalog (available in English, Chinese, Russian, and Turkish) that allows you to browse and search for charts, tables, diagrams, and maps by name and by function. Each entry in the catalog includes a description, a breakdown of the "anatomy" of the chart, and a list of tools that will allow you to generate that type of visualization. The site also includes a selection of data visualization resources as well as a blog by the creator of the project, Severino Ribecca.
  • The Data Viz Project

    The Data Viz Project is a Creative Commons-licensed project developed by the infographic design firm ferdio, based in Copenhagen. It consists of a catalog of visualizations that you can browse not only by name and function but also by input (what kind of data/variables you are working with) and "shape" (chart shape - circular, triangular, dots, bars, and more). Each chart page includes a description and a list of high-quality examples along with their sources.
  • From Data to Viz

    From Data to Viz is also a catalog classifying data visualizations by type and function, created by Yan Holz and Conor Healy, but in addition it offers a flowchart for deciding on a chart type based on the kind of data and number of variables you are working with. The site includes a number of "data stories" or short articles with examples of different chart types created using R and real data. Code is included for each chart on the site, and the combination of descriptive catalog with real-life examples makes this a valuable data visualization resource.

 

Color and Accessibility

Accessible Design

When designing a data visualization, the primary goal is to communicate information using visual means, and whether you share your visualizations in print or digital form, designing for accessibility is important. Although color is most frequently mentioned in this context, accessible design goes beyond avoiding color palettes that make charts difficult to read by individuals with color vision deficiencies like red-green colorblindness, or protanopia. There are many ways to make your graphs easier to understand for people with visual impairments or other disabilities, and designing for accessibility can make your data visualizations more readable for all users.

The following articles offer good overviews of the issues involved in designing for accessibility.

Read more about the Web Accessibility Initiative at w3.org.


Color & Contrast

The following tools can help you get started with choosing accessible color palettes and contrast ratios in your visualization.


General Best Practices

In addition to color, there are other aspects of visualizations to consider when designing for accessibility. For additional guidelines, consult the CFPB Design Manual for Data Visualization.

  • Image "alt" tags:
    Always add descriptive text in an "alt tag" when embedding your visualizations in a webpage. Screen readers read alt text out loud for users with visual impairment, so it is important to include a concise but accurate description of a graph.
  • Font and descriptive labels:
    Use a sans-serif font for chart titles and descriptive labels, and consider labeling data directly whenever possible rather than putting values or other information only in a chart legend.

Common Mistakes

Mistakes to Avoid

Andy Kirk's Visualising Data has a wealth of resources for data visualization beginners, but particularly helpful is his "Little of Visualisation of Design" series, which talks about how small design decisions can shape a data visualization in big ways.

From Data to Viz offers a gallery of "caveats" or common mistakes to avoid when creating data visualizations.

Be careful with pie charts: Robert Kosara explains why on his site, Eager Eyes, and offers many other articles on data visualization techniques.


Color Palettes

Certain kinds of color palettes are better suited to different kinds of data. When you are using color to represent a numeric value, you should use a color scale. Color gradients (shades from light to dark within a single hue) can be used to represent sequential data, whereas diverging scales (such as red to blue) are often used to represent numbers to either side of a mid-point.

If you are using color to represent categories rather than sequential or continuous values, you may want to use a categorical color palette. The maximum number of distinct colors you should include in a visualization is 12. Ideally, try to keep categorical color palettes to 6 or fewer colors when possible.


For Fun

Data is Ugly on Reddit

Terrible Maps on Reddit

WTFViz

Data Visualization was created by UB Libraries' 2018-2020 CLIR Postdoctoral Fellow, Rachel Starry. It is currently maintained by Carolyn Klotzbach-Russell. Guide content is licensed CC BY 4.0.

Creative Commons attribution license