Users of statistical computing need to produce graphs of their data and the results of their computations. In this chapter we start with a general overview of how this is done in R, and learn how to draw some basic plots. We then discuss some of the issues involved in choosing a style of plot to draw: it is not always an easy choice, and there are plenty of bad examples in the world to lead us astray. We will talk briefly about customizing graphs, and then move on to alternate packages for producing graphics.

Much of statistics relies on being able to evaluate expectations of random variables, and finding quantiles of distributions.

As of this writing, there are more than 3000 objects in the R base packages, and more than 15,000 other packages available on CRAN, most containing dozens of objects of their own. This represents a huge amount of functionality, and nobody could be expected to remember it all. The best we can hope for is that people should be able to discover it, using the help system and other resources, as well as judicious guesses about where to look. Guessing is made easier when consistent principles are followed as code is written. For example, names should reflect the purpose of packages and functions. Guessing is made harder by inconsistency, for example when names are poorly chosen, or conventions for forming names are inconsistent.

Programming involves writing relatively complex systems of instructions. There are two broad styles of programming: the imperative style (used in R, for example) involves stringing together instructions telling the computer what to do. The declarative style (used in HTML in web pages, for example, and to some extent in ggplot2, as described in Section 3.4) involves writing a description of the end result, without giving the details about how to get there. Within each of these broad styles, there are many subdivisions, and a given program may involve aspects of several of them. For example, R programs may be procedural (describing what steps to take to achieve a task), modular (broken up into self-contained packages), object-oriented (organized to describe operations on complex objects), and/or functional (organized as a collection of functions which do specific calculations without having external side-effects), among other possibilities. In this book we will concentrate on the procedural aspects of programming.

Having installed the R and RStudio systems, you are now ready to begin to learn the art of statistical programming. The first step is to learn the syntax of the language that you will be programming in; you need to know the rules of the language. This chapter will give you an introduction to the syntax of R. Most of what we discuss here relates to what you would type into the R console or into the RStudio script window.