Overview of Additional Resources

Below is a list of all resources used in compiling this guide. They all have different strengths and provide useful insights in how to effectively use R.

R for Data Science76

  • seminal overview of how to use R
  • goes into further depth and provides many more examples and exercises on most topics covered

Data Wrangling77

  • good information on data wrangling, scraping, and interacting with APIs including tidycensus

Analyzing US Census Data: Methods, Maps, and Models in R78

  • best information on using tidycensus and dealing with census data

Statistical Inference via Data Science: A ModernDive into r and the Tidyverse79

  • great information linking statistical knowledge with the tidyverse
  • lots of information on how to produce effective visualizations

Ggplot2: Elegant Graphics for Data Analysis80

  • best book for in-depth information on all the possibilities available through ggplot2

bookdown.org

  • center for textbooks such as this one produced using the bookdown package
  • links to books on anything you might want to do using R

References

Altman, Sara, Bill Behrman, and Hadley Wickham. Data Wrangling. Stanford, 2021.
Ismay, Chester, and Albert Y. Kim. Statistical Inference via Data Science: A ModernDive into r and the Tidyverse, 2023.
Walker, Kyle. Analyzing US Census Data: Methods, Maps, and Models in r. CRC Press, 2023.
Wickham, Hadley, Mine Çetinkaya-Rundel, and Garett Grolemund. R for Data Science. 2nd ed., 2023.
Wickham, Hadley, Danielle Navarro, and Thomas Lin. Ggplot2: Elegant Graphics for Data Analysis. 3rd ed. Springer, 2023.

  1. Wickham, Çetinkaya-Rundel, and Grolemund, R for Data Science.↩︎

  2. Altman, Behrman, and Wickham, Data Wrangling.↩︎

  3. Walker, Analyzing US Census Data.↩︎

  4. Ismay and Kim, Statistical Inference via Data Science.↩︎

  5. Wickham, Navarro, and Lin, Ggplot2.↩︎