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Data Manipulation with R de Phil Spector

Data Manipulation with R: Use R!

Autor Phil Spector
en Limba Engleză Paperback – 19 mar 2008

Considerăm Data Manipulation with R o resursă de referință profesională și pentru nivelul de master, esențială pentru cercetătorii care utilizează R în modelare statistică și grafică. Publicată în prestigioasa serie Use R! de la Springer, lucrarea lui Phil Spector abordează o problemă critică: eficiența computațională. Subliniem faptul că, deși R este extrem de flexibil, implementarea unor tehnici împrumutate din alte limbaje poate duce la performanțe scăzute pe măsură ce volumul de date crește. Această carte învață utilizatorul să lucreze „în spiritul” R, profitând de structura sa internă pentru a optimiza procesele de transformare. Structura este riguros logică, începând cu fundamentele stocării datelor și continuând cu importul din surse variate. Remarcăm capitolele dedicate unor subiecte adesea problematice, precum factorii, manipularea șirurilor de caractere și lucrul cu baze de date. Progresia narativă se încheie cu tehnici avansate de agregare și remodelare a datelor, oferind un set complet de instrumente pentru pregătirea seturilor de date complexe. Ca alternativă la Data Analysis with R de Anthony Fischetti pentru cursurile de statistică aplicată, volumul de față are avantajul unei abordări mult mai tehnice asupra infrastructurii datelor, fiind mai degrabă un ghid de inginerie a datelor decât unul de analiză exploratorie. Spre deosebire de A Survivor's Guide to R, care oferă o introducere blândă pentru începători, lucrarea lui Spector este un manual compact și dens, ideal pentru cei care au depășit pragul de bază și caută rigoare în gestionarea obiectelor R complexe.

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Specificații

ISBN-13: 9780387747309
ISBN-10: 0387747303
Pagini: 154
Ilustrații: X, 154 p.
Dimensiuni: 156 x 237 x 13 mm
Greutate: 0.26 kg
Ediția:2008 edition
Editura: Springer
Seria Use R!

Locul publicării:New York, NY, United States

Public țintă

Research

De ce să citești această carte

Recomandăm această carte cercetătorilor și studenților la master care doresc să stăpânească pregătirea datelor la un nivel avansat. Cititorul câștigă o înțelegere profundă a modului în care R gestionează memoria și obiectele, transformând sarcini de curățare a datelor consumatoare de timp în procese eficiente și scalabile. Este un instrument indispensabil pentru oricine lucrează cu seturi de date mari care necesită prelucrări structurale riguroase.

Descriere scurtă

The R language provides a rich environment for working with data, especially data to be used for statistical modeling or graphics. Coupled with the large variety of easily available packages, it allows access to both well-established and experimental statistical techniques. However techniques that might make sense in other languages are often very ine?cient in R, but, due to R’s ?- ibility, it is often possible to implement these techniques in R. 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. The goal of this book is to present a wide variety of data - nipulation techniques implemented in R to take advantage of the way that R works,ratherthandirectlyresemblingmethodsusedinotherlanguages. Since this requires a basic notion of how R stores data, the ?rst chapter of the book is devoted to the fundamentals of data in R. The material in this chapter is a prerequisite for understanding the ideas introduced in later chapters. Since one of the ?rst tasks in any project involving data and R is getting the data into R in a way that it will be usable, Chapter 2 covers reading data from a variety of sources (text ?les, spreadsheets, ?les from other programs, etc. ), as well as saving R objects both in native form and in formats that other programs will be able to work with.

Cuprins

Data in R.- Reading and Writing Data.- R and Databases.- Dates.- Factors.- Subscripting.- Character Manipulation.- Data Aggregation.- Reshaping Data.

Recenzii

From the reviews:
"This comprehensive, compact and concise book provides all R users with a reference and guide to the mundane but terribly important topic of data manipulation in R. … This is a book that should be read and kept close at hand by everyone who uses R regularly."(Douglas M. Bates, International Statistical Reviews, Vol. 76 (2), 2008)
"Presents a wide array of methods applicable for reading statistical data into the R program and efficiently manipulating that data." (Journal of Economic Literature, Vol. 46, no. 3, September 2008)
"R is a programming language particularly suitable for statistical computing and data analysis. … Using a variety of examples based on data sets included with R, along with easily stimulated data sets, the book is recommended to anyone using R who wishes to advance from simple examples to practical real-life data manipulation solutions." (Christina Diakaki, Zentralblatt MATH, Vol. 1154, 2009)
"The book contains much good information regarding the unique way in which R manipulates data objects. lt provides a complement to the many books illustrating statistical applications of R. It is clear that the author is very familiar with R. and the explanations and illustrations are generally helpful. Personally, I found the chapters on reading and writing data and on data aggregation most helpful, because these topics are essential in exploring data." (Jim Albert, The American Statistician, May 2009, Vol. 63, no. 2)
“Readers of this book will receive a focused treatment of data manipulation … . This book has lots of examples which are helpful. … provides more depth to understand the data structure/objects within R and how to better take advantage of this structure. … I found this book very helpful to my understanding of the structure and will be using it as a reference tool in my work.” (Roger M. Sauter, Technometrics, Vol. 52 (3), August,2010)

Textul de pe ultima copertă

Since its inception, R has become one of the preeminent programs for statistical computing and data analysis. The ready availability of the program, along with a wide variety of packages and the supportive R community make R an excellent choice for almost any kind of computing task related to statistics. However, many users, especially those with experience in other languages, do not take advantage of the full power of R. Because of the nature of R, solutions that make sense in other languages may not be very efficient in R. This book presents a wide array of methods applicable for reading data into R, and efficiently manipulating that data.
In addition to the built-in functions, a number of readily available packages from CRAN (the Comprehensive R Archive Network) are also covered. 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.
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. 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. 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. For users with experience in other languages, guidelines for the effective use of programming constructs like loops are provided. 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.
Using a variety of examples based on data sets included with R, along with easily simulated data sets, the book is recommended to anyone using R who wishes to advance from simple examples to practical real-life data manipulation solutions.
Phil Spector is Applications Manager of the Statistical Computing Facility and Adjunct Professor in the Department of Statistics at University of California, Berkeley.

Caracteristici

Provides guidance on getting data into R from text files, web pages, spreadsheets, databases and other sources. Demonstrates techniques that use core features of the R language, and are scalable and efficient. Covers many built in functions along with selected packages from CRAN. Includes supplementary material: sn.pub/extras