Summary
You can apply these guidelines to determine if a chart or visualization is “good” or not.
- The visualization has no substantive issues: The underlying data is sound and accurate
- The visualization has no perceptual issues: Nothing in the visualization distorts the interpretation (e.g. truncated y-axis in a bar chart, dual y-axes, pie chart slices that are indistinguishable from each other)
- The visualization uses honesty and good judgment: The creator isn’t lying or misleading + the creator considers the audience and their needs
- The visualization uses good aesthetics: The visualization adheres to graphic design principles
This list comes from chapter 1 of Kieran Healy’s book Data Visualization: A Practical Introduction (which is free online here!).
Resources
How to select the appropriate chart type
Many people have created many useful tools for selecting the correct chart type for a given dataset or question. Here are some of the best:
- The Data Visualisation Catalogue: Descriptions, explanations, examples, and tools for creating 60 different types of visualizations.
- The Data Viz Project: Descriptions and examples for 150 different types of visualizations. Also allows you to search by data shape and chart function (comparison, correlation, distribution, geographical, part to whole, trend over time, etc.).
- The Chartmaker Directory: Examples of how to create 51 different types of visualizations in 31 different software packages, including Excel, Tableau, and R.
- Emery’s Essentials: Descriptions and examples of 26 different chart types.