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:

Chapter 4: Exploring data

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

Chapter 7: Exploratory Data Analysis

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