Part 4. Advanced methods

In this final section, we consider advanced methods of statistical and graphical analysis to round out your data analysis toolkit. Chapter 13 expands on the regression methods in chapter 8 to cover parametric approaches to data that aren’t normally distributed. The chapter starts with a discussion of the generalized linear model, and then focuses on cases where we’re trying to predict an outcome variable that’s either categorical (logistic regression) or a count (poisson regression).

Dealing with a large number of variables can be challenging, due to the complexity inherent in multivariate data. Chapter 14 describes two popular methods for exploring and simplifying multivariate data. Principal components analysis can be used to transform a large number of correlated variables into a smaller set of composite variables. Factor analysis consists of a set of techniques for uncovering the latent structure underlying a given set of variables. Chapter 14 provides step-by-step instructions for carrying out each.

More often than not, researchers must deal with incomplete datasets. Chapter 15 considers modern approaches to the ubiquitous problem of missing data values. R supports a number of elegant approaches for analyzing datasets that are incomplete for various reasons. Several of the best approaches are described here, along with guidance around which ones to use, and which ones to avoid.

Chapter 16 completes our discussion of graphics with presentations of some of R’s most advanced and useful approaches to visualizing data. This includes visual representations of complex data using the lattice package, and an introduction to the new, and increasingly popular, ggplot2 package. The chapter ends with a review of packages that provide functions for interacting with graphs in real-time.

After completing part 4, you will have the tools to manage a wide range of complex data analysis problems. This includes modeling non-normal outcome variables, dealing with large numbers of correlated variables, and handling messy and incomplete data. Additionally, you will have the tools to visualize complex data in useful, innovative and creative ways.