page i
Preface
These notes are an introduction to using the statistical software package R for an introductory statistics course.
They are meant to accompany an introductory statistics book such as Kitchens “Exploring Statistics”. The goals
are not to show all the features of R, or to replace a standard textbook, but rather to be used with a textbook to
illustrate the features of R that can be learned in a one-semester, introductory statistics course.
These notes were written to take advantage of R version 1.5.0 or later. For pedagogical reasons the equals sign,
=, is used as an assignment operator and not the traditional arrow combination <-. This was added to R in version
1.4.0. If only an older version is available the reader will have to make the minor adjustment.
There are several references to data and functions in this text that need to be installed prior to their use. To
install the data is easy, but the instructions vary depending on your system. For Windows users, you need to
download the “zip” file , and then install from the “packages” menu. In UNIX, one uses the command R CMD
INSTALL packagename.tar.gz. Some of the datasets are borrowed from other authors notably Kitchens. Credit is
given in the help files for the datasets. This material is available as an R package from:
http://www.math.csi.cuny.edu/Statistics/R/simpleR/Simple 0.4.zip for Windows users.
http://www.math.csi.cuny.edu/Statistics/R/simpleR/Simple 0.4.tar.gz for UNIX users.
If necessary, the file can sent in an email. As well, the individual data sets can be found online in the directory
http://www.math.csi.cuny.edu/Statistics/R/simpleR/Simple.
This is version 0.4 of these notes and were last generated on August 22, 2002. Before printing these notes, you
should check for the most recent version available from
the CSI Math department (http://www.math.csi.cuny.edu/Statistics/R/simpleR).
Copyright
c
John Verzani (verzani@math.csi.cuny.edu), 2001-2. All rights reserved.
Contents
Introduction 1
What is R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
A note on notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Data 1
Starting R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Entering data with c . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Data is a vector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Univariate Data 8
Categorical data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Numerical data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Bivariate Data 19
Handling bivariate categorical data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Handling bivariate data: categorical vs. numerical . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Bivariate data: numerical vs. numerical . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Linear regression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Multivariate Data 32
Storing multivariate data in data frames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Accessing data in data frames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Manipulating data frames: stack and unstack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Using R’s model formula notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Ways to view multivariate data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
The
lattice package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
simpleR – Using
R for Introductory Statistics
