Graphs and maps help you reason with data. They also help you communicate results. A good graph gives you the most information in the shortest time, with the least ink in the smallest space (Tufte, 1997). In this chapter, we show you how to make graphs and maps using R. A good strategy is to follow along with an open session, typing (or copying) the code as you read. Before you begin make sure you have the following data sets available in your workspace. Do this by typing . . . > SOI = read.table("SOI.txt", header=TRUE) > NAO = read.table("NAO.txt", header=TRUE) > SST = read.table("SST.txt", header=TRUE) > A = read.table("ATL.txt", header=TRUE) > US = read.table("H.txt", header=TRUE) . . . Not all the code is shown but all is available on our Web site. It is easy to make a graph. Here we provide guidance to help you make informative graphs. It is a tutorial on how to create publishable figures from your data. In R you have several choices. With the standard (base) graphics environment, you can produce a variety of plots with fine details. Most of the figures in this book use the standard graphics environment. The grid graphics environment is even more flexible. It allows you to design complex layouts with nested graphs where scaling is maintained upon resizing. The lattice and ggplot2 packages use grid graphics to create more specialized graphing functions and methods. The spplot function for example is plot method built with grid graphics that you will use to create maps. The ggplot2 package is an implementation of the grammar of graphics combining advantages from the standard and lattice graphic environments. It is worth the effort to learn. We begin with the standard graphics environment. A box plot is a graph of the five-number summary. The summary function applied to data produces the sample mean along with five other statistics including the minimum, the first quartile value, the median, the third quartile value, and the maximum. The box plot graphs these numbers. This is done using the boxplot function.