2 Exploring data

This chapter and the next are a package deal—we’ll explore a real data set in this chapter and then get practical implications from the same in chapter 3. An exploratory data analysis (EDA) is a process—or, really, a series of processes—by which a data set is interrogated by computing basic statistics and creating graphical representations of the same. We won’t paint any broad strokes along the way; instead, we’ll focus our analysis on a single variable, a performance metric called win shares, and discover how win shares is associated with the other variables in our data. Our going-in hypothesis in the next chapter will directly tie back to the findings from this chapter. Along the way, we’ll demonstrate how to best use the power of R to thoroughly explore a data set—any data set.

2.1 Loading packages

2.2 Importing data

2.3 Wrangling data

2.3.1 Removing variables

2.3.2 Removing observations

2.3.3 Viewing data

2.3.4 Converting variable types

2.3.5 Creating derived variables

2.4 Variable breakdown

2.5 Exploratory data analysis

2.5.1 Computing basic statistics

2.5.2 Returning data