Further Reading: Layout, Composition, and Small Multiples
Tier 1: Essential Reading
Tufte, Edward R. Envisioning Information. Graphics Press, 1990. Tufte's second book, and the source of the most influential modern treatment of small multiples. Chapter 2 ("Micro/Macro Readings") and Chapter 4 ("Small Multiples") contain the core material. Tufte's claim that small multiples are "the best design principle" appears in this book, and his analysis of Marey's train schedule chart (Case Study 1) is the source of the modern lineage. The book is less systematic than Visual Display but contains some of Tufte's most beautiful examples of multi-chart composition. Essential reading for this chapter.
Tufte, Edward R. The Visual Display of Quantitative Information. 2nd ed. Graphics Press, 2001. Already cited in Chapters 6 and 7, Tufte's first book is also the original source for his discussion of small multiples — though the full development of the idea came in Envisioning Information. Chapter 7 ("Multifunctioning Graphical Elements") is particularly relevant to the layout and composition principles in this chapter.
Wilkinson, Leland. The Grammar of Graphics. 2nd ed. Springer, 2005. Wilkinson's formalization of chart construction as a grammar — with facets as a first-class element of the grammar — is the theoretical foundation for modern small-multiple implementations. The book is technical and dense, but the treatment of facets (Chapter 11) is the direct intellectual source for ggplot2's faceting, seaborn's FacetGrid, and every modern implementation of small multiples as a systematic feature. Read selectively; the introduction and Chapter 11 are the most directly relevant.
Tier 2: Recommended Specialized Sources
Few, Stephen. Information Dashboard Design: Displaying Data for At-a-Glance Monitoring. 2nd ed. Analytics Press, 2013. The standard reference for dashboard composition. Few's treatment of hierarchy, grouping, and the distinction between at-a-glance monitoring and detailed analysis is directly relevant to the dashboard section of this chapter. Pair with the small-multiples material in Tufte for a complete picture of multi-chart layout.
Wickham, Hadley. ggplot2: Elegant Graphics for Data Analysis. 3rd ed. Springer, 2016.
Wickham's book on ggplot2 is the most practical treatment of faceting (small multiples) available. The chapters on faceting explain the mechanics of the grammar-of-graphics approach to small multiples and give specific guidance on when to use facet_wrap vs. facet_grid, shared vs. free scales, and ordering of facets. The principles transfer directly to matplotlib and seaborn, even though the code is R/ggplot2.
Robbins, Naomi. Creating More Effective Graphs. Chart House, 2013. Robbins's book includes extensive discussion of small multiples (which she calls "trellis displays") and their design. Her treatment is practical and opinionated, with clear before-and-after examples. Particularly strong on the question of when small multiples fail and what to use instead.
Wilke, Claus O. Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures. O'Reilly Media, 2019. Wilke's chapter on small multiples (he uses the term "proportional ink" and "multiple panels") complements Tufte with modern examples rendered in ggplot2. The book is freely available at clauswilke.com/dataviz and is a good reference for practitioners working in R or adapting R examples to Python.
Knaflic, Cole Nussbaumer. Storytelling with Data: A Data Visualization Guide for Business Professionals. Wiley, 2015. Knaflic's treatment of chart layout in business communication — particularly her use of the hero-plus-supporting pattern and her discussion of "where to put what" in a presentation — is the most practical source for business-audience composition decisions. Chapter 7 ("Lessons in storytelling") includes specific layout guidance that complements the more theoretical treatment in Tufte.
Cleveland, William S. The Elements of Graphing Data. Revised ed. Hobart Press, 1994. Cleveland's book contains the original empirical work on banking to 45 degrees (which became the aspect-ratio rule for time series) and on the perceptual science of chart layout. Chapter 4 in particular is the source of much modern practice on aspect ratio and panel layout.
Munzner, Tamara. Visualization Analysis and Design. CRC Press, 2014. Munzner's academic treatment of visualization includes extensive discussion of what she calls "juxtaposition" and "partitioning" — systematic ways of arranging multiple views of data. Chapter 12 ("Facet into Multiple Views") is a rigorous treatment of the small-multiples concept and its relationship to other multi-view techniques. Academic in tone but thorough.
Tier 3: Tools and Practical References
| Resource | URL / Source | Description |
|---|---|---|
| matplotlib subplots tutorial | matplotlib.org/stable/tutorials/intermediate/arranging_axes.html | The matplotlib documentation on arranging subplots in a figure. Covers the subplots(), GridSpec, and constrained_layout APIs that implement the composition principles from this chapter in matplotlib. We will cover these in Chapter 13. |
| seaborn FacetGrid documentation | seaborn.pydata.org/tutorial/axis_grids.html | The seaborn documentation on FacetGrid, which is the most convenient way to produce small multiples in Python. Much of the ggplot2 faceting grammar is available through seaborn's API. |
| The Python Graph Gallery (small multiples examples) | python-graph-gallery.com/subplots/ | A collection of small-multiple examples in matplotlib and seaborn. Useful for seeing how the principles from this chapter translate to specific code. |
| Edward Tufte's website | edwardtufte.com | Tufte's personal site, which includes galleries of his favorite examples, essays on specific topics, and details about his workshops. Worth browsing to see how Tufte himself applies the principles he wrote about. |
| The New York Times Graphics Desk | nytimes.com/section/upshot | Not a formal resource, but the NYT's Upshot section is the single best source of examples of well-composed multi-panel figures applied to current events. Study published NYT graphics as teaching examples. |
| Financial Times Visual Vocabulary | ft.com/vocabulary | The FT's data visualization style guide, including discussion of multi-panel layouts and small multiples. Pairs well with NYT study for a comparative view of two major data journalism outlets. |
| Reuters Graphics | graphics.reuters.com | Another excellent source of published examples of multi-panel composition. Reuters often produces highly polished small multiples for election coverage, climate reporting, and financial analysis. |
| BBC Visual and Data Journalism cookbook | bbc.github.io/rcookbook | The BBC's publicly shared R/ggplot2 style file and design principles. Includes specific guidance on faceting and small-multiple composition for news graphics. |
Notes on the Lineage of Small Multiples
Small multiples have a specific intellectual history that is worth knowing. The technique can be traced through a chain of influential works:
E.J. Marey (1880) — The train schedule chart discussed in Case Study 1. Not a small multiple in the modern sense (it was a single integrated chart), but the same spirit of "show many instances of the same relationship in a single coherent visual."
R.A. Fisher and agricultural statistics (early 20th century) — Fisher's work on randomized experiments produced many small-multiple-style figures for comparing treatment effects across agricultural plots. The layouts were basic but the principle of "same chart, different slices" was present.
William Cleveland and Rick Becker (1980s) — Cleveland and Becker's work at Bell Labs on "trellis displays" formalized the small-multiple concept as a systematic technique, and their Trellis Graphics package for S-PLUS was the first widely-used software implementation.
Edward Tufte (1983, 1990) — Tufte's books popularized the small-multiple concept and gave it its modern name. His praise of Marey and his own examples defined what "good small multiples" should look like.
Leland Wilkinson (1999, 2005) — Wilkinson's Grammar of Graphics formalized facets as a systematic element of chart construction, providing the theoretical foundation for modern small-multiple implementations.
Hadley Wickham (2005–present) — Wickham's ggplot2 implemented Wilkinson's grammar in R, making facets a one-line feature and popularizing small multiples as the default answer to many visualization problems.
Every modern visualization library (2010s–present) — matplotlib, seaborn, Plotly, Altair, D3, Vega-Lite, Tableau, Power BI — all support some form of faceting or small-multiple creation, and all trace their support of the feature back through the lineage above.
Understanding this lineage is useful because it helps you see that small multiples are not a "new" technique or a "trendy" one — they are a century-old principle that has been refined and formalized over many iterations. The principles you apply when you make a small multiple today are the same principles that Marey applied in 1880, Cleveland applied in the 1980s, and the NYT applies in their pandemic coverage. The tradition is real and durable.
A note on reading order: If you want one additional source, read Tufte's Envisioning Information — it is the single best treatment of small multiples as a design principle. For practical implementation, Wickham's ggplot2 is the best guide, even if you do your work in Python, because ggplot2's faceting API is the cleanest implementation of the concept. For dashboard design specifically, Few's Information Dashboard Design is the standard reference. All three are worth owning; start with Tufte.