4.5 Resources

Here are some helpful pointers on the attitude and process of EDA in R (R Core Team, 2021) and the tidyverse (Wickham, 2019c) (from more general to more specific):

4.5.1 EDA

Background readings

Key references on EDA include the books and papers by John W. Tukey (Tukey, 1969, 1977, 1980). An insightful overview is provided by Behrens (1997).

The broader implications of two distinct cultures in the use of statistical modeling are discussed by Breiman (2001) and the historical background and perspectives on data science are discussed by Donoho (2017).

Specific sources

Links to specific resources:

• Exploratory Data Analysis with R (by Roger D. Peng) provides a book-length treatment of EDA.

• To see a world in grains of sand (by Yihui Xie) illustrates potential solutions to overplotting.

• Data analysis principles (by Karl Broman) maybe a bit cryptic, but true.

R packages

For practical advice on the tidyverse packages dplyr and ggplot2:

• read Chapter 7: Exploratory data analysis (EDA) of r4ds (Wickham & Grolemund, 2017) and complete the corresponding exercises.

• study vignette("dplyr") and vignette("ggplot")

• study the examples at https://dplyr.tidyverse.org and https://ggplot2.tidyverse.org/reference/;

• study the RStudio cheatsheet on data transformation and data visualization

• consult the R Graphics Cookbook (by Winston Chang) or the original ggplot2 book (Wickham, 2016)

• check out various ggplot2 extensions

There are many other R packages that may become useful the context of EDA. Examples include:

• the codebook package automates the description of data frames

• the dlookr package supports various tasks of data diagnosis, exploration, and transformation (including visualizations of missing data or outliers)

• the sjmisc package provides miscellaneous utility functions, supporting data transformation tasks like recoding, dichotomizing or grouping variables

4.5.2 Visualization

Background readings

Given the variety of options, it is often difficult to decide when to use which type of plot (or geom). The landmark publications by Jacques Bertin (e.g., Bertin, 2011) and Edward R. Tufte (Tufte, 2001, 2006; Tufte, Goeler, & Benson, 1990) provide solid advice and many inspiring examples.

Specific sources

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 function 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)

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

• Data Visualization. A practical introduction (by Kieran Healy)

• Fundamentals of Data Visualization (by Claus O. Wilke)

4.5.3 Miscellaneous links

The Simply Statistics blog (by Rafa Irizarry, Roger Peng, and Jeff Leek) provides many insightful and inspiring articles. For instance, the following posts relate to EDA:

• Tukey, Design Thinking, and Better Questions (2019-04-17)

• The Role of Theory in Data Analysis (2018-12-11)

Know your data. Really, really, know it: An article (by Randy Au, 2019-02-15) on what “knowing your data” means in applied contexts

---

[04_explore.Rmd updated on 2022-07-15 18:31:56 by hn.]

References

Behrens, J. T. (1997). Principles and procedures of exploratory data analysis. Psychological Methods, 2(2), 131–160. https://doi.org/10.1037/1082-989X.2.2.131

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

Breiman, L., & others. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical Science, 16(3), 199–231. https://doi.org/10.1214/ss/1009213726

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.

Donoho, D. (2017). 50 years of data science. Journal of Computational and Graphical Statistics, 26(4), 745–766. Retrieved from https://doi.org/10.1080/10618600.2017.1384734

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.

Tukey, J. W. (1969). Analyzing data: Sanctification or detective work. American Psychologist, 2, 83–91. https://doi.org/10.1037/h0027108

Tukey, J. W. (1977). Exploratory data analysis. Reading, MA: Addison-Wesley.

Tukey, J. W. (1980). We need both exploratory and confirmatory. The American Statistician, 34(1), 23–25. Retrieved from https://www.jstor.org/stable/2682991

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

Wickham, H. (2019c). tidyverse: Easily install and load the ’tidyverse’. Retrieved from https://CRAN.R-project.org/package=tidyverse

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

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.