Further Reading — Chapter 5: Choosing the Right Chart

Tier 1: Essential Reading

These sources directly underpin this chapter's core content. They are the intellectual foundation of the chart selection framework.

Few, Stephen. Show Me the Numbers: Designing Tables and Graphs to Enlighten. 2nd edition. Analytics Press, 2012. The most systematic treatment of chart type selection for business audiences. Few's approach — classify the question first, then select the chart — is the philosophical foundation of the decision framework in this chapter. Chapters 4 through 8 walk through comparison, distribution, relationship, and composition charts with extensive examples from business contexts. Practical, opinionated, and grounded in perception science.

Wilke, Claus O. Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures. O'Reilly Media, 2019. The best modern reference for matching data type to chart type. Part I ("From Data to Visualization") devotes individual chapters to visualizing amounts, distributions, proportions, relationships, and uncertainty — a parallel structure to the question types in this chapter. Wilke's writing is precise and his examples (rendered in R/ggplot2, but the principles are universal) are excellent. Freely available online at clauswilke.com/dataviz.

Schwabish, Jonathan. Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks. Columbia University Press, 2021. An excellent chart-type taxonomy organized by communication goal. Schwabish provides a visual catalog of over 80 chart types with clear guidance on when each is appropriate and when it is not. Particularly strong on the "wrong chart" patterns and their fixes. The book includes a one-page chart chooser graphic that is worth printing and posting.

Tier 2: Recommended Resources

These extend the chapter's material into deeper theory, alternative frameworks, and practical tools.

Cleveland, William S. The Elements of Graphing Data. Revised edition. Hobart Press, 1994. The classic empirical work on graphical perception that underlies the encoding hierarchy (Chapter 2) and informs the chart selection framework. Cleveland's taxonomy of chart types — organized by the perceptual task they require — is the scientific basis for ranking chart effectiveness. Dense but rigorous. If you want to understand why a bar chart is better than a pie chart for comparison, this is the primary source.

Cleveland, William S., and Robert McGill. "Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods." Journal of the American Statistical Association 79, no. 387 (1984): 531-554. The seminal paper establishing the empirical ranking of visual encoding channels. The experiment asked subjects to judge proportions using different chart types (bar, stacked bar, pie, treemap) and measured error rates. The results directly inform the chart selection matrix: chart types that use position and length (bars) outperform those that use angle and area (pies, treemaps).

Tufte, Edward R. The Visual Display of Quantitative Information. 2nd edition. Graphics Press, 2001. The foundational text on data visualization design. While Tufte does not provide a formal chart selection framework, his principles — data-ink ratio, small multiples, chartjunk — shape the "Context Matters" section of this chapter and provide the intellectual scaffolding for Part II. Essential reading for anyone who makes charts.

Munzner, Tamara. Visualization Analysis and Design. CRC Press, 2014. A comprehensive academic treatment of visualization design as a systematic process. Munzner's "What-Why-How" framework — what data do you have, why are you visualizing, how will you encode — is a more formal version of the two-input model in this chapter. Chapter 5 (marks and channels) and Chapter 7 (spatial layout) are particularly relevant. Rigorous and thorough; best suited for readers who want the academic depth behind the practitioner guidance.

Knaflic, Cole Nussbaumer. Storytelling with Data: A Data Visualization Guide for Business Professionals. Wiley, 2015. Provides a practitioner's perspective on chart selection for business audiences. Knaflic's emphasis on choosing "the right chart for the right audience" — and her detailed treatment of when to use each chart type — complements the more systematic framework in this chapter. Chapter 2 ("Choosing an Effective Visual") is directly relevant. Highly accessible and full of concrete examples.

Chart Selection Tools and References

Seminal Chart Type References

These sources are worth knowing because they introduced or formalized specific chart types discussed in this chapter.

Tukey, John W. Exploratory Data Analysis. Addison-Wesley, 1977. Introduced the box plot (box-and-whisker plot) and formalized the stem-and-leaf display. Tukey's broader contribution — the philosophy that you should look at data before modeling it — is the intellectual ancestor of the "distribution" question type.

Bertin, Jacques. Semiology of Graphics: Diagrams, Networks, Maps. Translated by William J. Berg. University of Wisconsin Press, 1983 (original French edition 1967). The theoretical foundation for visual encoding, visual variables (position, size, shape, value, color, orientation, texture), and the mapping from data types to visual channels. Bertin's framework is the ancestor of the data-type classification in Section 5.2 and the encoding decisions that underpin the chart selection matrix.

Heer, Jeffrey, and Michael Bostock. "Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design." CHI 2010. Replicated and extended Cleveland and McGill's graphical perception experiments using crowdsourced data. Confirmed the encoding hierarchy and extended it to additional chart types including treemaps and bubble charts. Provides the empirical evidence for ranking chart types by perceptual accuracy.