- Series: Use R!
- Paperback: 154 pages
- Publisher: Springer; 2008 edition (March 19, 2008)
- Language: English
- ISBN-10: 0387747303
- ISBN-13: 978-0387747309
- Product Dimensions: 6.1 x 0.4 x 9.2 inches
- Shipping Weight: 11.2 ounces (View shipping rates and policies)
- Average Customer Review: 23 customer reviews
- Amazon Best Sellers Rank: #404,058 in Books (See Top 100 in Books)
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Data Manipulation with R (Use R!) 2008th Edition
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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)
From the Back Cover
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.
Top customer reviews
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This book will be helpful to any but the most absolutely new to R, and even the seasoned user will find interesting hints and examples. I cannot recommend it enough.
One minor qualm I have is the absence of references. Some topics (for instance, regular expressions) are fairly complex, and well documented elsewhere: a pointer or two would be helpful. Same with, for instance, SQL, which is mentioned and demonstrated briefly.
Another not-so-minor qualm is price. A book of this size from, for instance, Dover classics collection, with similar paper quality and covers, is about a third or fourth of the price. Although this is a new book I find the $54.95 tag (Amazon discounted price is about $44.50) fairly high. But this has nothing to do with the quality of the book, rather it has to do with the Springer pricing policies.
All in all, if you don't mind the price, this is a good buy.
You can see the table of contents and read the other reviews but areas that really shine include: dealing with categorical (named or ordered) factor variables, recoding numeric data into categorical variables, and also making and working with summary tables.
When it comes to data manipulation and clean-up Spector has the best coverage of any book or web FAQ. This book is very expensive for its size but it is worth every cent.
Not advanced by any means, so if you're an experienced R user, you won't need it.
I would recommend this to a beginner though, given that the R documentation generally sucks.