We conclude this introductory chapter with some pointers to related resources on R (R Core Team, 2019), the tidyverse (Wickham, 2017), and this book and course.

Book and course

Resources related to this book and course at the University of Konstanz, Fall 2019:


The official textbook of this course is Data Science for Psychologists (by Hansjoerg Neth, 2019), freely available at

A more general and in-depth introduction is R for Data Science (Wickham & Grolemund, 2017):

  • Wickham, H., & Grolemund, G. (2017). R for data science: Import, tidy, transform, visualize, and model data. Sebastopol, Canada: O’Reilly Media, Inc. [Available at]

The ebook R for data science: Exercise solutions (by Jeffrey B. Arnold) provides exercise solutions to the exercises in r4ds.

Software and packages

Working through this book assumes an installation of the following software programs:

  • The R project for statistical computing is the origin of all things R. A current distribution of R for your machine can be downloaded from one if its mirrors.

  • R Studio provides an integrated development environment (IDE) for R.
    Especially foldable sections and many keyboard shortcuts (see Alt + Shift + K for an overview) can make your life in R a lot easier.

  • The R packages of the tidyverse (Wickham, 2017) and the ds4psy package (Neth, 2020).

R Markdown

R Markdown allows weaving text and code into reproducible research documents. For quick instructions on combining text and code, see Appendix E, or read the more detailed introduction of Chapter 27: R Markdown of the r4ds textbook. Alternatively, just start with one of the following templates:

  • minimal template: rmd_template_s [in .Rmd | .html format]

  • medium template: rmd_template_m [in .Rmd | .html format]

  • explicit explanations: Rmarkdown_basics [in .Rmd | .html format]


Some recommendations for additional books on R, the tidyverse, and various aspects of data science and statistics:

Web sites and blogs

Online information on R is abundant. Useful starting points include:

  • R-bloggers collects blog posts on R.

  • Quick-R (by Robert Kabacoff) is a popular website on R programming that also provides many pointers for using R in statistics.

  • The Simply statistics blog (by Rafa Irizarry, Roger Peng, and Jeff Leek) provides insightful and inspiring articles on many data science topics.

  • Towards data science provides space for sharing concepts, ideas, and codes.

Educational resources

Other R courses and exercises include:


Other helpful links that do not fit into the above categories include:


[index.Rmd updated on 2020-02-24 14:01:37 by hn.]


Neth, H. (2020). ds4psy: Data science for psychologists. Retrieved from

R Core Team. (2019). R: A language and environment for statistical computing. Retrieved from

Wickham, H. (2014a). Advanced R (1st ed.). Retrieved from

Wickham, H. (2015). R packages: Organize, test, document, and share your code. Retrieved from

Wickham, H. (2017). tidyverse: Easily install and load the ’tidyverse’. Retrieved from

Wickham, H., & Grolemund, G. (2017). R for data science: Import, tidy, transform, visualize, and model data. Retrieved from