Resources

Image: Library by Ming-Yueh Wang is licensed under CC BY-NC-ND 2.0

If you happen to come across other resources that you find useful, please let us know and we’ll add them to the list.

Using R

  • Atrebas (2019) for doing identical tasks in the data.table and dplyr environments. Very useful if you are familiar with dplyr syntax and want to translate that familiar functionality to data.table syntax.

  • Dowle & Srinivasan (2022) for the data.table website with vignettes by the package authors

  • Holtz (2018) for a gallery of chart designs possible with ggplot2.

  • R-bloggers, a searchable aggregator of R blogs.

  • Stackoverflow Searchable, public platform for coding questions and answers. For R-specific Q&A, add [r] as a search tag. Additional search tags such as [ggplot2] or [data.table] will help narrow your searches involving these packages.

  • RStudio (2022) Links to downloadable help summaries (cheat sheets) for ggplot2 and many other packages. For data.table scroll down to the “Contributed cheatsheets” section.

  • Zumel & Mount (2020) for data science work with a very good chapter on data manipulation that performs every step three ways: a base R solution, a data.table solution, and a dplyr solution.

Data visualization

Books

  • Cairo (2019) for a data journalist’s approach to avoiding lying to an audience (and to one’s self).

  • Cleveland (1993) for a detailed look at matching chart design to the types of variables.

  • Robbins (2013) for a detailed development of principles and practices for creating effective charts, based largely on Cleveland’s work.

  • Tufte (1983) One of the best developments of aligning one’s visual rhetoric to one’s verbal rhetoric.

  • Wainer (1997) Often humorous in presenting do’s and dont’s of data display

  • Wainer (2014) Using evidence and visualization to improve healthcare.

Blogs

  • Evergreen (2023) Intentional reporting and data visualization

  • Few (2021) Visual business intelligence

  • Knaflic (2023) Storytelling with data

  • Layton (2023) for Richard’s blog in which several articles illustrate redesigning a published chart in an engineering education article to better align the logic of a display with the logic of the argument.

Data visualization using R

  • Healy (2019) Hands-on introduction to principles and practices of looking at and prsenting data using R and ggplot2.

  • Machlis (2019) Especially useful for spreadsheet users who want to “graduate” to a reproducible scripted programming language.

References

Atrebas. (2019). A data.table and dplyr tour. Blog post. https://atrebas.github.io/post/2019-03-03-datatable-dplyr/
Cairo, A. (2019). How Charts Lie. W.W Norton and Co.
Cleveland, W. S. (1993). Visualizing Data. Hobart Press.
Dowle, M., & Srinivasan, A. (2022). Data.table. https://rdatatable.gitlab.io/data.table/
Evergreen, S. (2023). Blog. Evergreen Data. https://stephanieevergreen.com/blog/
Few, S. (2021). Blog. Perceptual Edge. https://www.perceptualedge.com/blog/
Healy, K. (2019). Data Visualization: A Practical Introduction. Princeton University Press.
Holtz, Y. (2018). ggplot2 Gallery. https://r-graph-gallery.com/ggplot2-package.html
Knaflic, C. N. (2023). Blog. Storytelling with Data. https://www.storytellingwithdata.com/blog/
Layton, R. (2023). Data Stories. Blog post. https://graphdr.github.io/data-stories/
Machlis, S. (2019). Practical R for Mass Communication and Journalism. CRC Press, Taylor and Francis group.
Robbins, N. (2013). Creating More Effective Graphs. Chart House.
RStudio. (2022). Cheatsheets. https://www.rstudio.com/resources/cheatsheets/
Tufte, E. (1983). The Visual Display of Quantitative Information. Graphics Press.
Wainer, H. (1997). Visual Revelations. Lawrence Erlbaum Assoc.
Wainer, H. (2014). Medical Illuminations. Oxford University Press.
Zumel, N., & Mount, J. (2020). Practical Data Science with R (2nd ed.). Manning Publications Co.