- R in a Nutshell
- Preface
- I. R Basics
- II. The R Language
- III. Working with Data
- IV. Data Visualization
- V. Statistics with R
- VI. Additional Topics
- A. R Reference
- Bibliography
- Index
- About the Author
- Colophon
- Copyright

R includes several packages for visualizing data: `graphics`

, `grid`

, and `lattice`

. Usually, you’ll find that functions
within the `graphics`

and `lattice`

packages are the most useful.^{[10]} If you’re familiar with Microsoft Excel, you’ll find that R can generate all of the charts that
you’re familiar with: column charts, bar charts, line plots, pie charts,
and scatter plots. Even if that’s all you need, R makes it much easier
than Excel to automate the creation of charts and to customize them.
However, there are many, many more types of charts available in R, many of
them quite intuitive and elegant.

To make this a little more interesting, let’s work with some real
data. We’re going to look at all field goal attempts in the National
Football League (NFL) in 2005.^{[11]} For those of you who aren’t familiar with American football,
here’s a quick explanation. A team can attempt to kick a football between
a set of goalposts to receive 3 points. If it misses the field goal,
possession of the ball reverts to the other team (at the spot on the field
where the kick was attempted). We’re going to take a look at kick attempts
in the NFL in 2005.

First, let’s take a quick look at the distribution of distances. R
provides a function, `hist`

, that can do
this quickly for us. Let’s start by loading the appropriate data set. (The
data set is included in the `nutshell`

package; see the Preface for information on how to
obtain this package.)

>library(nutshell)>data(field.goals)

Let’s take a ...