## Overview

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

### Sessions and topics

#### Part 0: Prepare

1. Preambulations

1. Basic R concepts and commands

#### Part 1: Explore

1. Visualizing data
2. Transforming data [2019-11-11]
3. Exploring data [2019-11-18]

#### Part 2: Wrangle

1. Tibbles [2019-11-25]

1. Importing data [2019-12-02]

1. Tidying data [2019-12-09]

1. Joining data [2019-12-16]

1. Text data [2020-01-13]

1. Time data [2020-01-20]

#### Part 3: Program

1. Functions [2020-01-27]

1. Iteration [2020-02-03]

1. Starting your data science project [2020-02-10]
• ds4psy: 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 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:

As later chapters depend on earlier ones, the first chapters are particularly important. Arranging the same information in a clock-wise fashion allows signaling our current position (by highlighting it in yellow):

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 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 on 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