Generally, the problem with such techniques is that they do not scale properly that is, as the problem size grows, the methods slow down at a rate that might be unexpected. However techniques that might make sense in other languages are often very inefficient in R, but, due to R’s flexibility, it is often possible to implement these techniques in R. Coupled with the large variety of easily available packages, it allows access to both well-established and experimental statistical techniques. The R language provides a rich environment for working with data, especially data to be used for statistical modeling or graphics. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Giovanni Parmigiani The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University 550 North Broadway Baltimore, MD 21205-2011 USAĮ-ISBN 978-1-6 ISBN 978-0-9 DOI: 10.1663/978-1-6 Library of Congress Control Number: 2008921862 © 2008 Springer Science+Business Media, LLC All rights reserved. Kurt Hornik Department of Statistik and Mathematik Wirtschaftsuniversität Wien Augasse 2-6 A-1090 Wien Austria
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Series Editors: Robert Gentleman Program in Computational Biology Division of Public Health Sciences Fred Hutchinson Cancer Research Center 1100 Fairview Avenue, N, M2-B876 Seattle, Washington 98109-1024 USA Phil Spector Statistical Computing Facility Department of Statistics University of Califonia, Berkeley Berkeley, California 94720 Use R! Albert:Bayesian Computation with R Cook/Swayne:Interactive and Dynamic Graphics for Data Analysis: With R and GGobi Hahne/Huber/Gentleman/Falcon: Bioconductor Case Studies Paradis: Analysis of Phylogenetics and Evolution with R Pfaff: Analysis of Integrated and Cointegrated Time Series with R Sarkar: Lattice: Multivariate Data Visualization with R Spector: Data Manipulation with R Since many statistical modeling and graphics functions need their data presented in a data frame, techniques for converting the output of commonly used functions to data frames are provided throughout the book. For users with experience in other languages, guidelines for the effective use of programming constructs like loops are provided.
Character manipulation, while sometimes overlooked within R, is also covered in detail, allowing problems that are traditionally solved by scripting languages to be carried out entirely within R. Accordingly, the use of databases in R is covered in detail, along with methods for extracting data from spreadsheets and datasets created by other programs. Most experienced R users discover that, especially when working with large data sets, it may be helpful to use other programs, notably databases, in conjunction with R. All of the methods presented take advantage of the core features of R: vectorization, efficient use of subscripting, and the proper use of the varied functions in R that are provided for common data management tasks.
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In addition to the built-in functions, a number of readily available packages from CRAN (the Comprehensive R Archive Network) are also covered. This book presents a wide array of methods applicable for reading data into R, and efficiently manipulating that data.
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