Jared P. Lander - R for Everyone: Advanced Analytics and Graphics

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Advanced Analytics and Graphics

Jared P. Lander

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Library of Congress Cataloging-in-Publication Data

Lander, Jared P.
R for everyone / Jared P. Lander.
pages cm
Includes bibliographical references.
ISBN-13: 978-0-321-88803-7 (alk. paper)
ISBN-10: 0-321-88803-0 (alk. paper)
1. R (Computer program language) 2. Scripting languages (Computer science) 3. StatisticsData
processing. 4. StatisticsGraphic methodsData processing. 5. Computer simulation. I. Title.
QA76.73.R3L36 2014
005.13dc23
2013027407

Copyright 2014 Pearson Education, Inc.

All rights reserved. Printed in the United States of America. This publication is protected by copyright, and permission must be obtained from the publisher prior to any prohibited reproduction, storage in a retrieval system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or likewise. To obtain permission to use material from this work, please submit a written request to Pearson Education, Inc., Permissions Department, One Lake Street, Upper Saddle River, New Jersey 07458, or you may fax your request to (201) 236-3290.

ISBN-13: 978-0-321-88803-7
ISBN-10: 0-321-88803-0
Text printed in the United States on recycled paper at RR Donnelley in Crawfordsville, Indiana.
First printing, December 2013

To my mother and father

Rhas had tremendous growth in popularity over the last three years. Based on that, youd think that it was a new, up-and-coming language. But surprisingly, R has been around since 1993. Why the sudden uptick in popularity? The somewhat obvious answer seems to be the emergence of data science as a career and a field of study. But the underpinnings of data science have been around for many decades. Statistics, linear algebra, operations research, artificial intelligence, and machine learning all contribute parts to the tools that a modern data scientist uses. R, more than most languages, has been built to make most of these tools only a single function call away.

Thats why Im very excited to have this book as one of the first in the Addison-Wesley Data and Analytics Series. R is indispensable for many data science tasks. Many algorithms useful for prediction and analysis can be accessed through only a few lines of code, which makes it a great fit for solving modern data challenges. Data science as a field isnt just about math and statistics, and it isnt just about programming and infrastructure. This book provides a well-balanced introduction to the power and expressiveness of R and is aimed at a general audience.

I cant think of a better author to provide an introduction to R than Jared Lander. Jared and I first met through the New York City machine learning community in late 2009. Back then, the New York City data community was small enough to fit in a single conference room, and many of the other data meetups had yet to be formed. Over the last four years, Jared has been at the forefront of the emerging data science profession.

Through running the Open Statistical Programming Meetup, speaking at events, and teaching a course at Columbia on R, Jared has helped grow the community by educating programmers, data scientists, journalists, and statisticians alike. But Jareds expertise isnt limited to teaching. As an everyday practitioner, he puts these tools to use while consulting for clients big and small.

This book provides an introduction both to programming in R and to the various statistical methods and tools an everyday R programmer uses. Examples use publicly available datasets that Jared has helpfully cleaned and made accessible through his Web site. By using real data and setting up interesting problems, this book stays engaging to the end.

Paul Dix, Series Editor

With the increasing prevalence of data in our daily lives, new and better tools are needed to analyze the deluge. Traditionally there have been two ends of the spectrum: lightweight, individual analysis using tools like Excel or SPSS and heavy duty, high-performance analysis built with C++ and the like. With the increasing strength of personal computers grew a middle ground that was both interactive and robust. Analysis done by an individual on his or her own computer in an exploratory fashion could quickly be transformed into something destined for a server, underpinning advanced business processes. This area is the domain of R, Python, and other scripted languages.

R, invented by Robert Gentleman and Ross Ihaka of the University of Auckland in 1993, grew out of S, which was invented by John Chambers at Bell Labs. It is a high-level language that was originally intended to be run interactively where the user runs a command, gets a result, and then runs another command. It has since evolved into a language that can also be embedded in systems and tackle complex problems.