Chapter 7 Exploring data

Exploratory data analysis (EDA) is a fancy name for an ordinary, but important phase of any data analysis.

From a technical viewpoint, EDA is where our skills in transforming, reshaping, and visualizing data (e.g., using base R functions and additional tools from the dplyr, tidyr, and ggplot2 packages) meet so that we become familiar with a dataset.

Preparation

Recommended readings for this chapter include:

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

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

Preflections

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

i2ds: Preflexions

  • What are the first steps of any data analysis?

  • Which questions should we always ask before conducting any statistical test?

  • What is the difference between generating and testing hypotheses?

References

Neth, H. (2021a). Data science for psychologists. Social Psychology; Decision Sciences, University of Konstanz. https://bookdown.org/hneth/ds4psy/
Wickham, H., & Grolemund, G. (2017). R for data science: Import, tidy, transform, visualize, and model data. O’Reilly Media, Inc. http://r4ds.had.co.nz