Chapter 6 Transforming data

Anyone regularly working with data is aware that transforming data (aka. “data munging” or “data wrangling”) is an essential pre-requisite for any successful data analysis.

Key topics (and corresponding R packages) of this chapter are:

All these sections and packages are designed for manipulating data structures (mostly vectors or tables) into other data structures.

If transforming data is viewed as a challenge and a task, our main goal is to gain insights into the contents of our data. From this perspective, tidy data becomes an intermediate goal — a way of representing our data so that it can be processed more easily and rapidly.


Recommended readings for this chapter include:

of the ds4psy book (Neth, 2021a), and the corresponding chapters

of the r4ds book (Wickham & Grolemund, 2017).


Before reading, please take some time to reflect upon the following questions:

i2ds: Preflexions

  • Assuming we had all the data required for answering our question, which additional obstacles would we face?

  • The same data can be stored in different data structures. Which ones? (Think in terms of different data types and their data structures or shapes.)

  • Does it matter how data is stored? Why or why not?


Bache, S. M., & Wickham, H. (2014). magrittr: A forward-pipe operator for R.
Neth, H. (2021a). Data science for psychologists. Social Psychology; Decision Sciences, University of Konstanz.
Wickham, H., François, R., Henry, L., & Müller, K. (2021). dplyr: A grammar of data manipulation.
Wickham, H., & Grolemund, G. (2017). R for data science: Import, tidy, transform, visualize, and model data. O’Reilly Media, Inc.