2.8 Resources

As they help us to make sense of data, visualizations appear in most chapters of this book (e.g., Chapter 4 on Exploring data). Similarly, the r4ds textbook by Wickham & Grolemund (2017) introduces visualizations in Chapter 3: Data visualization and provides further information and examples in Chapter 7: Exploratory data analysis (EDA).

This section contains links to general resources on visualization and to the ggplot2 package.

2.8.1 General publications and recommendations

Articles and books (in print and online)

The most comprehensive reference on the R package ggplot2 (Wickham et al., 2021) is Hadley Wickham’s book ggplot2: Elegant graphics for data analysis (Wickham, 2016). The package provides an implementation of The Grammar of Graphics (Wilkinson, 2005), which develops a language and philosophy of data visualisation.

Articles, books, or scripts on data visualization include many classic articles or chapters [e.g., Cleveland & McGill (1985); Friendly2008]. The landmark books by Edward R. Tufte (Tufte, 2001, 2006; Tufte, Goeler, & Benson, 1990) and Jacques Bertin (e.g., Bertin, 2011) provide solid advice and many inspiring examples.

More recent publications that are geared to the needs of aspiring data scientists include:

Data Visualization. A practical introduction (Healy, 2018) is beautiful, informative, and elegant.
Fundamentals of Data Visualization (Wilke, 2019) provides many instructive examples and helps distinguishing good from bad and ugly graphs.
R Graphics Cookbook (Chang, 2012) provides hands-on advice on using ggplot2 and many useful recipes for data transformation.
Data Visualization with R (Kabacoff, 2018) relies heavily on the ggplot2 package, but also covers other approaches.

Guidelines and principles

More specific resources on the principles of data visualization (with many beautiful or bizarre examples) include:

Various books (e.g., Cairo, 2012, 2016) and The functional art weblog (by Alberto Cairo)
Various books (e.g., Yau, 2011, 2013) and the Flowing data site (by Nathan Yau)
The principle of proportional ink and the Calling bullshit weblog (by Carl T. Bergstrom and Jevin West)
Data visualization principles (by Rafael A. Irizarry)
Data visualization: Basic principles (by Peter Aldhous)

For those who prefer watching video clips:

Essentials of data visualization provides an 8-part miniseries on thinking about drawing data and communicating science

Online collections and applications

Inspiration and tools for many types of visualizations can be found at (from specific to general):

r/dataisbeautiful at reddit.com
R Graph Gallery
Google Charts
DataVizProject
The Data Visualisation Catalogue
From Data to Viz: A decision tree for selecting graphical representations, with links to code and lists of common caveats to be avoided
The Pudding uses visual essays to explain ideas debated in culture (especially pop culture)

2.8.2 Specific topics

Helpful pointers to resources on specific topics include:

Using ggplot2

The following links provide additional information on using ggplot2:

study the vignette("ggplot") and the documentation for ggplot and various geoms (e.g., geom_);
study https://ggplot2.tidyverse.org/reference/ and its examples;
study the RStudio cheatsheet on data visualization:

Figure 2.8: Data visualization with ggplot2 summary from RStudio cheatsheets.

Top 50 ggplot2 Visualizations helps choosing the right charts for specific goals and tasks and shows how to create them by using ggplot2
ggplot2 Extensions expand the range and scope of ggplot2 even further
A ggplot2 Tutorial for Beautiful Plotting in R (by Cédric Sherer, 2019-08-05) provides an introduction that focuses on aesthetics and recruits many packages that help boosting the appearance of plots

2.8.2.1 Colors in R

The grDevices component of R comes with many options and tools for selecting and modifying colors:

Call colors() or demo("colors") in the Console to view the in-built colors of R.

See Appendix D for a primer on using colors in R and Section D.7 for corresponding resources and links.

2.8.2.2 Explanation: Same stats, different data

The introductory data examined above (in Section 2.1 and plotted in Figure 2.2) is known as Anscombe’s quartet (Anscombe, 1973) and is included in R as anscombe in the datasets package (R Core Team, 2021). It contains four sets of x-y coordinates which have the same statistical properties (regarding mean, SD, correlation, regression line), although their raw data values are quite different. (See ?datasets::anscombe for details.)

References

Anscombe, F. J. (1973). Graphs in statistical analysis. The American Statistician, 27(1), 17–21. https://doi.org/10.2307/2682899

Bertin, J. (2011). Semiology of graphics: Diagrams, networks, maps (Vol. 1). ESRI Press.

Cairo, A. (2012). The functional art: An introduction to information graphics and visualization. Berkeley CA: New Riders.

Cairo, A. (2016). The truthful art: Data, charts, and maps for communication. Berkeley CA: New Riders.

Chang, W. (2012). R graphics cookbook: Practical recipes for visualizing data (2nd ed.). Retrieved from https://r-graphics.org/

Cleveland, W. S., & McGill, R. (1985). Graphical perception and graphical methods for analyzing scientific data. Science, 229(4716), 828–833. https://doi.org/10.1126/science.229.4716.828

Healy, K. (2018). Data visualization: A practical introduction. Retrieved from https://socviz.co/

Kabacoff, R. (2018). Data visualization with R. Retrieved from https://rkabacoff.github.io/datavis/

Locke, S., & D’Agostino McGowan, L. (2018). datasauRus: Datasets from the datasaurus dozen. Retrieved from https://CRAN.R-project.org/package=datasauRus

Matejka, J., & Fitzmaurice, G. (2017). Same stats, different graphs: Generating datasets with varied appearance and identical statistics through simulated annealing. Proceedings of the 2017 CHI conference on human factors in computing systems, 1290–1294. https://doi.org/10.1145/3025453.3025912

R Core Team. (2021). R base: A language and environment for statistical computing. Retrieved from https://www.R-project.org

Tufte, E. R. (2001). The visual display of quantitative information (2nd ed.). Cheshire, CT: Graphics Press.

Tufte, E. R. (2006). Beautiful evidence (Vol. 1). Cheshire, CT: Graphics Press.

Tufte, E. R., Goeler, N. H., & Benson, R. (1990). Envisioning information (Vol. 126). Cheshire, CT: Graphics Press.

Wickham, H. (2016). ggplot2: Elegant graphics for data analysis (2nd ed.). Retrieved from https://ggplot2-book.org/

Wickham, H., Chang, W., Henry, L., Pedersen, T. L., Takahashi, K., Wilke, C., … Dunnington, D. (2021). ggplot2: Create elegant data visualisations using the grammar of graphics. Retrieved from https://CRAN.R-project.org/package=ggplot2

Wickham, H., & Grolemund, G. (2017). R for data science: Import, tidy, transform, visualize, and model data. Retrieved from http://r4ds.had.co.nz

Wilke, C. O. (2019). Fundamentals of data visualization: A primer on making informative and compelling figures. Retrieved from https://clauswilke.com/dataviz/

Wilkinson, L. (2005). The grammar of graphics (2nd edition). Springer.

Yau, N. (2011). Visualize this: The FlowingData guide to design, visualization, and statistics. Hoboken, NJ: John Wiley & Sons.

Yau, N. (2013). Data points: Visualization that means something. Hoboken, NJ: John Wiley & Sons.