Thomas P. Hogan - Bare-Bones R: A Brief Introductory Guide

bare-bones R

This little book is dedicated to my stat students:
Past, present, and future.

bare-bones R

Copyright 2010 by SAGE Publications, Inc.

All rights reserved. No part of this book may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without permission in writing from the publisher.

Printed in the United States of America

Hogan, Thomas P.

Bare-bones R: a brief introductory guide / Thomas P. Hogan.

p. cm.

Includes bibliographical references and index.

ISBN 9781-41298041-8 (pbk.)

1. Social sciencesStatistical methodsData processing. 2. StatisticsData processing. 3. R
(Computer program language) I. Title.

HA32.H64 2011

005.262dc22 2009033530

This book is printed on acid-free paper.

09 10 11 12 13 10 9 8 7 6 5 4 3 2 1

I intend this bare-bones introduction to R as a supplement to a standard first-course textbook in statistics or for those who have already completed such a course and now need to do some statistical analysis on their own. This introduction, as suggested by its title, is bare-bones. It doesnt pretend to teach everything about Rjust to get people started with it.

R is a wonderful tool for statistical work. Its flexibility and richness offer a boon to experienced researchers. It also offers great advantages for statistical novicesthose taking their first course in statistics. However, for such novices, R can be intimidating, precisely because of its flexibility and richness. Its arcane customs, symbols, and terminology may make the novice shudder. Some of the purported introductions to R seem like trying to teach Mozart concertos in the first week of piano lessons. They are just overwhelming. In my experience, extant introductions share the following drawbacks, not for the experienced researcher but for the novice. They tend to present several ways of accomplishing any given task (thus nicely illustrating Rs richness), whereas the novice needs just one way to do it. They tend to use traditional R terminology (arguments of functions, vectors, etc.) rather than plain English. They are very short on examples. This feature helps their compactness, but novices need examples. They tend to include advanced statistical techniques (logistic regression, mixed-effects ANOVA, etc.), while the novice is still trying to learn the difference between a mean and a median. I have tried to avoid these drawbacks.

I make the following assumptions about the students using this bare-bones introduction:

Here are the guidelines I used in putting this material together:

Occasionally, I must admit, I violate one of these rulesbut only occasionally.

I realize that my assumptions and rules, as just outlined, may encourage my colleagues to pummel me with criticisms. Why introduce only this way to accomplish some task? What about this nifty add-on package? Wont this common English translation of a technical R term possibly mislead? And so on. Ill accept those criticisms. But Ill add a bet. If, within a few hours of instruction, I can get students feeling comfortable and confident using R, Ill bet those students will go on to learn a whole lot more about R on their own. We just need a good start.