Which tasks do we tackle where? Here is an overview of book chapters and topics, with links to corresponding chapters in the r4ds textbook (Wickham & Grolemund, 2017):

Chapter topics


  1. Preambulations

  1. Basic R concepts and commands

Part 1: Explore

  1. Visualizing data

  1. Transforming data

  1. Exploring data

Part 2: Wrangle

  1. Tibbles

  1. Importing data

  1. Tidying data

  1. Joining data

  1. Strings of text

  1. Dates and times

Part 3: Program

  1. Functions

  1. Iteration

Assignment: Your data science project (see Appendix C).


The following appendices provide supporting materials for the ds4psy course:

  • Appendix A provides the solutions to exercises.

  • Appendix B provides details on the datasets used in this course.

  • Appendix C describes characteristics of successful data science projects.

Other appendices are of a more general nature and provide introductions to specific topics:

  • Appendix D provides a primer on using colors in R.

  • Appendix E provides a primer on using regular expressions.

  • Appendix F provides a primer on using R Markdown.


The following diagram provides a schematic overview of the topics and corresponding chapters in our course curriculum. Whereas the colors of the horizontal lines signals which sessions belong to different parts, the height of each bar reflects the relevance of each chapter within this course:

Relevance of chapters and topics, with colored lines indicating different parts.

Figure 0.1: Relevance of chapters and topics, with colored lines indicating different parts.

As you can see, the first four chapters are particularly important. And as the contents of later chapters build and depend on earlier ones, please make sure that you really make a solid start in your endeavours.

Arranging the same information in a clock-wise fashion is a bit gimmicky, but allows signaling our current position (by highlighting it in yellow):

Centrality of topics (on polar coordinates) and highlighting the current chapter (0).

Figure 0.2: Centrality of topics (on polar coordinates) and highlighting the current chapter (0).

Both plots also show that mastering Chapters 1 to 4 (i.e., Parts 0: Prepare and 1: Explore) will put you in a very good position for successfully completing this course.

Session structure

This course mostly uses the so-called flipped classroom paradigm, in which students are solving exercises — in pairs or small groups — with additional guidance by the instructor (see Wikipedia: flipped classroom for details). So what should you do when?


  • Before class: You need to prepare every session at home by

    1. reading the current chapter, and

    2. preparing an .R or .Rmd script that includes all chapter code (without the exercises).

  • During class: Every session consists of 2 parts:

    1. Asking questions (plenary): To start each weekly session, the instructor introduces the current topic and answers questions on main concepts or commands.

    2. Begin with weekly exercises (in random dyads/triples): Copy exercises into your script and solve them in a way that you can discuss/share with other members of the class.

  • After class: Finish exercises at home and submit your solutions on Ilias by Thursday of the same week (23:59).


Wickham, H., & Grolemund, G. (2017). R for data science: Import, tidy, transform, visualize, and model data. Retrieved from http://r4ds.had.co.nz