Effective visual communication

A tutorial on effective visual communication for the quantitative scientist.

What is EVC?

Effective visual communication is a core competency for pharmacometricians, statisticians, and more generally any quantitative scientist. It is essential in every step of a quantitative workflow, from scoping to execution and communicating results and conclusions. With this competency, we can better understand data and influence decisions towards appropriate actions. Without it, we can fool ourselves and others and pave the way to wrong conclusions and actions. The goal of this tutorial is to convey this competency.

The three laws

We posit three laws of effective visual communication for the quantitative scientist:

• have a clear purpose,
• show the data clearly,
• and make the message obvious.

Cheat sheet and hands-on tutorial

We defined the three laws of effective visual communication to provide overarching principled advice that can serve as a guiding star towards effective visual communication for the quantitative scientist. A concise Cheat Sheet, distills more granular recommendations for everyday practical use. Finally, these laws and recommendations are illustrated in four case studies. The aim of this site is to provide both the code, data and examples as stand alone posts. We hope that it proves useful for putting the three laws into practice.

To further ease implementation in practice, it helps to distill the three laws in to even more detailed recommendations and illustrate them concretely. This is why we introduced a cheat Sheet. This single-page reference sheet is an integral part of this tutorial, carefully designed as a concise and accessible resource for everyday practical use. Yet, it draws from a wide range of sources including (Bonate 2014; Jean L. Doumont 2002; Cairo 2016; Few 2012; Ware 2004; B. Wong 2010, 2011; J. L. Doumont 2009; Heer and Bostock 2010; Tufte 1986, 1997; Gelman and Unwin 2013; D. M. Wong 2010; Wainer 1984; Munzner 2015; Cleveland 1985; Cleveland and McGill 1985, 1987; Wilkinson 2005; Tukey 1977; Duke et al. 2015; Robbins 2012).

Were to find out more

The full version is available along with corresponding programming code in R.

The full published tutorial can is available and online at CPT:PSP here.

A pre-print of the complete tutorial can also be found at: https://arxiv.org/abs/1903.09512

References

Bonate, Peter L. 2014. Be a Model Communicator and Sell Your Models to Anyone. Published by Peter Bonate. https://www.amazon.com/Be-Model-Communicator-Models-2014-10-06/dp/B01LP3SHNM.

Cairo, Alberto. 2016. The Truthful Art: Data, Charts, and Maps for Communication. 1st ed. Thousand Oaks, CA, USA: New Riders Publishing.

Cleveland, William S. 1985. The Elements of Graphing Data. Belmont, CA, USA: Wadsworth Publ. Co.

Cleveland, William S., and Robert McGill. 1985. “Graphical Perception and Graphical Methods for Analyzing Scientific Data.” Science 229 (4716): 828–33. https://doi.org/10.1126/science.229.4716.828.

———. 1987. “Graphical Perception: The Visual Decoding of Quantitative Information on Graphical Displays of Data.” Journal of the Royal Statistical Society Series A 150 (3): 192–229. http://www.jstor.org/stable/2981473.

Doumont, J. L. 2009. Trees, Maps, and Theorems: Effective Communication for Rational Minds. Principiae. https://books.google.com/books?id=O2dFPgAACAAJ.

Doumont, Jean L. 2002. “The Three Laws of Professional Communication.” IEEE Transactions on Professional Communication 45 (4): 291–96. https://doi.org/10.1109/TPC.2002.805164.

Duke, Susan P., Fabrice Bancken, Brenda Crowe, Mat Soukup, Taxiarchis Botsis, and Richard Forshee. 2015. “Seeing Is Believing: Good Graphic Design Principles for Medical Research.” Statistics in Medicine 34 (22): 3040–59. https://doi.org/10.1002/sim.6549.

Few, Stephen. 2012. Show Me the Numbers: Designing Tables and Graphs to Enlighten. 2nd ed. USA: Analytics Press.

Gelman, Andrew, and Antony Unwin. 2013. “Infovis and Statistical Graphics: Different Goals, Different Looks.” Journal of Computational and Graphical Statistics 22 (1): 2–28. https://doi.org/10.1080/10618600.2012.761137.

Heer, Jeffrey, and Michael Bostock. 2010. “Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design.” In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 203–12. CHI ’10. New York, NY, USA: ACM. https://doi.org/10.1145/1753326.1753357.

Munzner, Tamara. 2015. Visualization Analysis and Design. AK Peters Visualization Series. CRC Press. https://books.google.de/books?id=NfkYCwAAQBAJ.

Robbins, Naomi B. 2012. Creating More Effective Graphs. Wiley. https://www.amazon.com/Creating-Effective-Graphs-Naomi-Robbins/dp/0985911123.

Tufte, Edward R. 1986. The Visual Display of Quantitative Information. Cheshire, CT, USA: Graphics Press.

———. 1997. Visual Explanations: Images and Quantities, Evidence and Narrative. Cheshire, CT, USA: Graphics Press.

Tukey, John W. 1977. Exploratory Data Analysis. Addison-Wesley.

Wainer, Howard. 1984. “How to Display Data Badly.” The American Statistician 38 (2): 137–47. https://doi.org/10.1080/00031305.1984.10483186.

Ware, Colin. 2004. Information Visualization: Perception for Design. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.

Wilkinson, Leland. 2005. The Grammar of Graphics (Statistics and Computing). Berlin, Heidelberg: Springer-Verlag.

Wong, Bang. 2010. “Points of View: Gestalt Principles (Part 1).” Nature Methods 7: 863–63.

———. 2011. “Points of View: Simplify to Clarify.” Nature Methods 8 (8): 611. https://www.nature.com/articles/nmeth.1660.

Wong, D. M. 2010. The Wall Street Journal Guide to Information Graphics: The Dos and Don’ts of Presenting Data, Facts, and Figures. W.W. Norton & Company. https://books.google.com/books?id=RmaJPgAACAAJ.