Chapter 0
About This Document
This document attempts to reproduce the examples and some of the exercises in An Introduction to Categori-
cal Data Analysis [1] using the R statistical programming environment. Numbering and titles of chapters will
follow that of Agresti’s text, so if a particular example/analysis is of interest, it should not be hard to find,
assuming that it is here.
Since R is particularly versatile, there are often a number of different ways to accomplish a task, and
naturally this document can only demonstrate a limited number of possibilities. The reader is urged to explore
other approaches on their own. In this regard it can be very helpful to read the online documentation for the
various functions of R, as well as other tutorials. The help files for many of the R functions used here are
also included in the appendix for easy reference, but the online help system is definitely the preferred way to
access this information.
It is also worth noting that as of this writing (early 2000), R is still very much under development.
Thus new functionality is likely to become available that might be more convenient to use than some of the
approaches taken here. Of course any user can also write their own R functions to automate any task, so
the possibilities are endless. Do not be intimidated though, for this is really the fun of using R and its best
feature: you can teach it to do whatever is neede, instead of being constrained only to what is “built in.”
A Note on the Datasets
Often in this document I will show how to enter the data into R as a part the example. However, most of the
datasets are avaiable already in R format in the R package for the course, sta4504, available from the course
web site. After installing the library on your computer and starting R, you can list the functions and data files
available in the package by typing
> library(help = sta4504)
> data(package = sta4504)
You can make the files in the package to your R session by typing
> library(sta4504)
and you can read one of the package’s datasets into your R session simply by typing, e.g.,
> data(deathpen)
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