## Schedule

What do we do when? Here is an overview of topics, with dates, and links to corresponding chapters in the r4ds textbook (Wickham & Grolemund, 2017) and this book ds4psy.

### Sessions, dates, and topics

#### Part 0: Prepare

1. Preambulations (15.04.2019)
2. Basic R concepts and commands (29.04.2019)

#### Part 1: Explore

1. Visualizing data (06.05.2019)
2. Transforming data (13.05.2019)
3. Exploring data (20.05.2019)

#### Part 2: Wrangle

1. Tibbles (27.05.2019)

1. Importing data (03.06.2019)

1. Tidying data (17.06.2019)

1. Joining data (24.06.2019)

#### Part 3: Program

1. Functions (01.07.2019)

1. Iteration (08.07.2019)

1. Starting your data science project (15.07.2019, due on 31.08.2019; see Appendix C).

#### Appendices

• 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.

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

• Appendix E provides an introduction into using R Markdown.

### Orientation

The following diagram provides a schematic overview of the topics and sessions 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 the corresponding session within this course:

As later sessions depend on earlier ones, the first sessions are particularly important. Arranging the same information in a clock-wise fashion allows signaling our current position (by highlighting it in yellow). The plot also shows that mastering Sessions 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 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 class 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 to Ilias by Thursday of the same week (23:59).

### References

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