The book provides an introduction to data science that is tailored to the needs of psychologists, but is also suitable for students of the humanities and other biological or social sciences. This audience typically has some elementary knowledge of statistics, but rarely an idea how the data got into a shape that allows for statistical testing. By using various data types and working with many different examples, we teach tools for transforming, summarizing, and visualizing data. By keeping our eyes open for the perils and pitfalls of misleading representations, the book fosters fundamental skills of data literacy and cultivates reproducible research practices that enable and precede any practical use of statistics.

The materials in this book are based on a Data science for psychologists course at the University of Konstanz in Summer 2019. The course provides an introduction to data science in R (R Core Team, 2019) from a tidyverse (Wickham, 2017) perspective, and the book summarizes essential concepts and commands, and collects corresponding examples and exercises. Individual sessions summarize and elaborate upon key chapters of the R for Data Science (Wickham & Grolemund, 2017) textbook. With the exception of Chapter 1 — which covers basic R concepts and commands that are not explicated at the beginning of the textbook — reading this book is not required for passing the course. However, scores of practical examples and exercises should appeal to the interests of social scientists who aim to apply the newly aquired tools to challenging problems. Hopefully, students will find solving these exercises both instructive and enjoyable and the summaries of essential commands helpful.


Creative Commons License

Data science for psychologists (ds4psy) by Hansjörg Neth is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.


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