Why interactive R custom visuals?

Let's start with a graph you've already implemented in R. Consider, for example, the raincloud plot of Fare versus Pclass variables introduced in Chapter 14, Exploratory Data Analysis:

Figure 16.1 – Raincloud plot for Fare (transformed) and Pclass variables

Focus for a moment only on the boxplots you see in Figure 16.1. Although the Fare variable is already transformed according to Yeo-Johnson to try to reduce skewness, there remain some extreme outliers for each of the passenger classes described by the categorical variable, Pclass. If, for example, you want to know the values of the transformed variable Fare corresponding to the whiskers (fences) of the boxplot on the left so that you can then determine the outliers located beyond those whiskers, it would be convenient that these values appear when you pass the mouse near that boxplot, as in Figure 16.2:

Figure 16.2 – Main labels...