Book Review: Better Data Visualizations, by Jonathan Schwabish

Updated: Mar 26


Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks (2021), by Jonathan Schwabish (Senior Fellow at the Urban Institute and Founder of PolicyViz) provides a comprehensive introduction to data visualization that has something for everyone, whether you are just starting in data visualization or are a seasoned pro.


Schwabish’s conversational style makes for easy reading and the book is lavishly illustrated in color. He has included over 500 illustrations with most coming from real-world datasets. The book contains three sections: 1) Principles of Data Visualization, 2) Chart Types, and 3) Designing and Redesigning Your Visual, as well as two appendices providing additional resources. Here are four reasons to read this book.


1) Actionable Information


The book’s actionable tips will prove most useful for Schwabish’s primary target audience: scholars, researchers, and graduate students new to data visualization. He summarizes industry best practices concisely and redesigns charts to highlight the benefits of the best practices. “Five Guidelines for Better Data Visualizations” will immediately improve the quality of your graphs and charts, while “The Ten Guidelines of Better Tables” will help you design clear and understandable tables. Following his own advice, Schwabish devotes a chapter to redesigns of a wide range of published tables and charts.


2) Exploring Chart Types


The “Chart Types” section is the “meat” of the book and will benefit all readers, including experienced designers. While bar charts, line charts, histograms, and scatterplots are the workhorses of data visualization, many other types of graphs and charts are available. In this section, Schwabish discusses and illustrates more than 80 different graphs and charts.


Schwabish organizes the section into categories based on their use: Comparing Categories, Time, Distribution, Geospatial, Relationship, Part-to-Whole, Qualitative, and Tables. He describes and illustrates the graphics, as well as provides tips on best practices.


As an example, he discusses bar charts in the section on comparing categories. He reviews different types of bar charts: simple bar charts, paired bar charts, stacked bar charts, diverging bar charts, and radial bar charts. He stresses the importance of starting the axis at zero and the need to avoid “breaking the bar” when the dataset contains outliers. He finishes with a discussion of alternatives to bar charts, such as lollipop charts and dot maps. Throughout, he carefully outlines the strengths, weaknesses, and appropriate use of each chart.


Although not part of the book, Schwabish discussed less common chart types during Episode 39 of Cole Nussbaumer Knaflic’s podcast. He quickly identified slope charts, dot plots, and heatmaps as the most underutilized types, but noted that Sankey diagrams also present an interesting option. All are discussed in the book.


3) Data Visualization Style Guides


Schwabish’s discussion of data visualization style guides is a welcome addition to the data visualization literature. Style guides are typically associated with large organizations and branding, but they can greatly aid individuals. They “will make your work more consistent and efficient, and it will build your individual brand as your work stands out.”


The elements of a data visualization style guide are: 1) graph anatomy, 2) color palette, 3) fonts, 4) graph types, 5) exporting images, and 6) accessibility, diversity, and inclusion. The author explores each element in detail, highlighting best practices and including tips from the Urban Institute’s style guide.


Schwabish uses different style guides for the chapters on chart types, providing a subtle and interesting introduction to the topic. This twist may be overlooked by the casual reader. He reinforces the importance of style guides and shows how the choice of a style can affect the look and feel of your data visualization.


4) Additional Resources


Schwabish’s appendices complete the book with a summary of data visualization tools and resources. He documents a wide range of data visualization tools, from drag-and-drop tools to programming languages. He divides his reading references into general data visualization books, historical data visualization books, and books on data visualization tools. And in keeping with many designers’ wish for places to practice their craft, he points to resources where you can join a community and practice your skills.


Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks is an ambitious volume that covers the breadth of data visualization topics. His actionable information quickly brings the reader up to speed and his references support more in-depth follow-up research. His chart types discussion introduces a wealth of options which may not be familiar to even experienced designers. And his approach to style guides will streamline your workflow. There is something for everyone and the author’s interest and enthusiasm are contagious.