Appendix E: Further Learning Resources

This book is a starting point. This appendix suggests where to go next: books, courses, and communities, almost all of them free. The books cited in the chapters are listed first, by topic; the rest are chosen because they suit researchers who are not programmers.

Books

Getting started and data skills

  • R for Data Science (Wickham et al. 2023), free at r4ds.hadley.nz: the standard introduction to importing, cleaning, transforming, and visualising data with the tidyverse. The natural next book after Part 1 of this one.
  • R for Researchers: An Introduction by Tyson Barrett, free at tysonbarrett.com/Rstats: a short, friendly introduction written for researchers in the social and health sciences, and a good companion to Chapters 1 to 8.
  • ggplot2: Elegant Graphics for Data Analysis (Wickham 2016), free at ggplot2-book.org: everything about ggplot2, for when Chapter 4 leaves you wanting more.
  • Happy Git and GitHub for the useR by Jenny Bryan, free at happygitwithr.com: a gentle, practical guide to git and GitHub with RStudio (Chapter 17).

Statistics

  • Introduction to Modern Statistics (Çetinkaya-Rundel and Hardin 2024), free at openintro-ims.netlify.app: a modern introductory statistics textbook built around simulation and real data, like Chapters 6 and 7.
  • Learning Statistics with R by Danielle Navarro, free at learningstatisticswithr.com: a thorough, readable introduction to statistics for psychology and the social sciences, using R.
  • Statistical Inference via Data Science (ModernDive) by Chester Ismay and Albert Kim, free at moderndive.com: regression and inference with the tidyverse.
  • Regression and Other Stories (Gelman et al. 2020): an excellent guide to building, checking, and interpreting regression models in real research, with a free PDF from the authors.
  • Statistical Power Analysis for the Behavioral Sciences (Cohen 1988): the classic reference on effect sizes and power.

Mixed models, machine learning, and forecasting

Reporting and dashboards

The Big Book of R (bigbookofr.com) indexes hundreds of free R books by topic, from psychology and ecology to economics and text analysis: a good way to find a book for your own field.

Courses and practice

  • Posit Cheatsheets (posit.co/resources/cheatsheets): one- or two-page summaries of dplyr, ggplot2, tidyr, Quarto, Shiny, and more, worth keeping next to you while you work. Several have been translated into other languages by volunteers.
  • The Carpentries (carpentries.org): free, hands-on lessons for researchers, including R for Social Scientists and R for Ecologists, and two-day workshops held at universities around the world.
  • swirl (swirlstats.com): interactive lessons that run inside R itself: install.packages("swirl"), then library(swirl) and swirl().
  • TidyTuesday (github.com/rfordatascience/tidytuesday): a new real dataset every week to practise on, with thousands of shared solutions to learn from.
  • This book’s playground: exercises for every chapter, in the browser and as downloadable projects.

Communities

You do not have to learn alone, and the R community is known for welcoming beginners.

  • Posit Community (forum.posit.co): a friendly forum for questions about R, RStudio, the tidyverse, Quarto, and Shiny.
  • Stack Overflow (stackoverflow.com/questions/tagged/r): the largest archive of answered R questions; search it before asking, and include a small reproducible example when you ask (Appendix D).
  • R-Ladies (rladies.org): a worldwide organisation promoting gender diversity in the R community, with local chapters that run meetings and workshops open to learners.
  • R user groups meet in many cities and universities; the R Consortium lists them at r-consortium.org.
  • R Weekly (rweekly.org): a weekly digest of R news, tutorials, and new packages.
  • useR!, the annual international R conference, posts its talks online.

Whatever you learn from, the most effective way to learn R is to use it on your own data, a little every day, one question at a time, as Elaf did.

References

Çetinkaya-Rundel, Mine, and Johanna Hardin. 2024. Introduction to Modern Statistics. 2nd ed. OpenIntro. https://openintro-ims.netlify.app.
Chollet, François, Tomasz Kalinowski, and J. J. Allaire. 2022. Deep Learning with r. 2nd ed. Manning.
Cohen, Jacob. 1988. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Lawrence Erlbaum Associates.
Gelman, Andrew, and Jennifer Hill. 2007. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. https://doi.org/10.1017/CBO9780511790942.
Gelman, Andrew, Jennifer Hill, and Aki Vehtari. 2020. Regression and Other Stories. Cambridge University Press. https://doi.org/10.1017/9781139161879.
Hyndman, Rob J., and George Athanasopoulos. 2021. Forecasting: Principles and Practice. 3rd ed. OTexts. https://otexts.com/fpp3/.
James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2021. An Introduction to Statistical Learning: With Applications in r. 2nd ed. Springer. https://doi.org/10.1007/978-1-0716-1418-1.
Kuhn, Max, and Julia Silge. 2022. Tidy Modeling with r: A Framework for Modeling in the Tidyverse. O’Reilly Media. https://www.tmwr.org.
Wickham, Hadley. 2016. Ggplot2: Elegant Graphics for Data Analysis. 2nd ed. Springer. https://doi.org/10.1007/978-3-319-24277-4.
Wickham, Hadley. 2021. Mastering Shiny. O’Reilly Media. https://mastering-shiny.org.
Wickham, Hadley, Mine Çetinkaya-Rundel, and Garrett Grolemund. 2023. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. 2nd ed. O’Reilly Media. https://r4ds.hadley.nz.