Data Visualization with ggplot2 (Part 2)

In this chapter, we’ll delve into how to use R ggplot2 as a tool for graphical data analysis, progressing from just plotting data to applying a variety of statistical methods. This includes a variety of linear models, descriptive and inferential statistics (mean, standard deviation and confidence intervals) and custom functions.

The Coordinates and Facets layers offer specific and very useful tools for efficiently and accurately communicating data. In this chapter we’ll look at the various ways of effectively using these two layers.

Now that you’ve built high-quality plots, it’s time to make them pretty. This is the last step in the data viz process. The Themes layer will enable you to make publication quality plots directly in R.

Once you have the technical skill to make great visualizations, it’s important that you make them as meaningful as possible. In this chapter we’ll go over three plot types that are mostly discouraged in the data viz community - heat maps, pie charts and dynamite plots. We’ll understand what the problems are with these plots and what the alternatives are.

In this case study, we’ll explore the large, publicly available California Health Interview Survey dataset from 2009. We’ll go step-by-step through the development of a new plotting method - a mosaic plot - combining statistics and flexible visuals. At the end, we’ll generalize our new plotting method to use on a variety of datasets we’ve seen throughout the first two courses.