30 Annotated bibliography
No single book can cover everything there is to know about a topic. I encourage you to read other texts on data visualization to deepen your understanding and to develop your technical skills in making figures. Here, I provide a limited selection of books that I have personally found interesting, thought provoking, or helpful. Books listed in Section 30.1 are the most similar in scope to the present book, and may provide complementary or alternative perspectives on the topics I have covered. Books listed in Section 30.2 address the important topic of how to make visualizations using programming approaches and available software libraries. The remaining sections list other books that will expand your knowledge of data visualization and help you communicate with visuals and data.
30.1 Thinking about data and visualization
The following books discuss the thought processes and decision making required for turning data into visualizations. They serve as introductory texts into how to choose what visualizations to make and what pitfalls to look out for.
Alberto Cairo. “The Truthful Art.” New Riders, 2016.
Excellent all-around introduction to data visualization, in particular for journalists. The book covers many important concepts of data visualization, such as how to visualize distributions, trends, uncertainty, and maps. In many chapters, the book also serves as an introduction to basic statistical principles, explaining concepts such as population, sample, and confidence level.
Stephen Few. “Show Me the Numbers.” Analytics Press, 2012.
A book about data visualization for the business professional. It is similar in scope and target audience to the book by Nussbaumer Knaflic (see below). However, Few’s book contains more material and covers many topics in more depth. At the same time, the book is not as well written and as carefully produced as the Nussbaumer Knaflic book.
Cole Nussbaumer Knaflic. “Storytelling with Data.” John Wiley & Sons, 2015.
A well written and carefully produced book on how to turn data into visuals. The book’s primary audience are people making business graphics, and the book is excellent for the topics it covers. However, the book is missing many topics of importance to scientists, such as the visualization of distributions, trends, or uncertainty.
30.2 Programming books
The following books are all how-to books that teach programming approaches to data visualization.
Kieran Healy. “Data Visualization: A Practical Introduction.” Princeton University Press, 2018.
Introduction to using ggplot2 for data visualization. Recommended as follow-up after Wickham and Grolemund’s “R for Data Science” (see below).
Scott Murray. “Interactive Data Visualization for the Web: An Introduction to Designing with D3, 2nd Edition.” O’Reilly Media, 2017.
Jake VanderPlas. “Python Data Science Handbook: Essential Tools for Working with Data.” O’Reilly Media, 2016.
Introduction to using the programming language Python for data science. Has extensive material on data visualization using Python’s Matplotlib and Seaborn.
Hadley Wickham, Garrett Grolemund. “R for Data Science.” O’Reilly Media, 2017.
All-around introduction to using the programming language R for data science. Contains several chapters on using ggplot2 for data visualization.
30.3 Statistics texts
Introductory texts in statistics will generally contain material on data visualization, covering topics such as scatter plots, histograms, box plots, and line graphs. There are many such texts that could be listed. Here, I mention just a few recent additions that are worth a closer look.
David M. Diez, Christopher D. Barr, Mine Çetinkaya-Rundel. “OpenIntro Statistics, 3rd Edition.” OpenIntro, Inc., 2015.
Open source introductory statistics text book. The entire book is freely available, as are the LaTeX files and R code used to compile the book and make the figures.
Susan Holmes, Wolfgang Huber. “Modern Statistics for Modern Biology.” 2018.
A statistics text that emphasizes computational tools needed for modern biology. The entire book is freely available, and R code for all examples is provided.
30.4 Historical texts
The books in this section are of interest primarily for historical reasons. They were influential at the time of their publication, but similar material can now be found elsewhere or in more modern form.
William S. Cleveland. “Visualizing Data.” Hobart Press, 1993.
Companion book to “The Elements of Graphing Data” by the same author (see below). This one is more mathematical and doesn’t talk about human perception.
William S. Cleveland. “The Elements of Graphing Data, 2nd Edition.” Hobart Press, 1994.
One of the first books about information design written for statisticians. The book contains many examples of scatter plots, line graphs, histograms, and boxplots, and it discusses them in the context of data analysis and statistical modeling. It also popularized the Cleveland dot plot.
Edward R. Tufte. “Envisioning Information.” Graphics Press, 1990.
This book popularized the concept of the small multiple.
Edward R. Tufte. “The Visual Display of Quantitative Information, 2nd Edition.” Graphics Press, 2001.
First published in 1983, this book has been highly influential in the field of data visualization. It introduced concepts such as chart junk, data-to-ink ratio, and sparklines. The book also showed the first slopegraph (but didn’t name it). However, the book also contains numerous recommendations that have not stood the test of time. In particular, it recommends an excessively minimalistic plot design.