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Data Manipulation with R (Use R!) [Paperback]

Phil Spector
4.1 out of 5 stars  See all reviews (17 customer reviews)

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Book Description

April 7, 2008 0387747303 978-3540756781 2008

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.


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Editorial Reviews

Review

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.


Product Details

  • Series: Use R!
  • Paperback: 154 pages
  • Publisher: Springer; 2008 edition (April 7, 2008)
  • Language: English
  • ISBN-10: 0387747303
  • ISBN-13: 978-3540756781
  • Product Dimensions: 9.1 x 6.3 x 0.4 inches
  • Shipping Weight: 6.4 ounces (View shipping rates and policies)
  • Average Customer Review: 4.1 out of 5 stars  See all reviews (17 customer reviews)
  • Amazon Best Sellers Rank: #287,535 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews
79 of 83 people found the following review helpful
5.0 out of 5 stars a must for statisticians wanting to learn R May 11, 2008
Format:Paperback
This book along with Jim Albert's should be read by every statistician that does a lot of statistical computing. Both books help you learn R quickly and apply it to many important problems in research both applied and theoretical. Albert emphasizes applications in Bayesian statistics whereas Spector is teaching how to do data manipulation, things like merging and transposing data sets. These techniques can be easy to do in a language like SAS after a little training but in other programming languages it can be very difficult.
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27 of 27 people found the following review helpful
5.0 out of 5 stars Great little book June 13, 2008
Format:Paperback|Verified Purchase
This concise 150 page book contains a wealth of information, writen clearly and with many well-chosen examples. I liked it a lot. It covers reading and writing data in/out of the R workspace, including access to databases. The names of other chapters suggest the topics covered: "Dates", "Factors", "Subscripting", "Character manipulation", "Data aggregation", "Reshaping data".

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.
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21 of 21 people found the following review helpful
5.0 out of 5 stars Start here December 19, 2008
Format:Paperback|Verified Purchase
All too often novices wanting to use R for an analysis never get to the analysis because they can't successfully import, clean-up and restructure their data for the analysis functions. This book prevents those problems by telling you the critical data and file manipulation materials that are usually briefly (and inadequately) covered in stat books. It is a short easy read that will give you the tools to get your data ready to go.

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.
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9 of 9 people found the following review helpful
2.0 out of 5 stars Not as good a book as "R in a Nutshell" November 8, 2010
Format:Paperback|Verified Purchase
I was pretty disappointed with the thoroughness of this book on R. I ordered the book "R in a Nutshell" as well and Nutshell does a much better job explaining the same topics and does it in a much more through manner and you don't feel like you got ripped off spending $50 on this very thin book. I would not recommend it and instead would choose R in a Nutshell for learning how to do R data cleaning, manipulation, processing and analysis.
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10 of 11 people found the following review helpful
3.0 out of 5 stars Useful code but disorganized September 6, 2009
Format:Paperback
I am neither a statistician nor a computer programmer yet I used statistics and programming on a daily basis and that is my perspective on this book. I am an ecologist with messy data with relational databases, missing data, dates etc. Most statisticians will already have more than a dollop of programming under their belts and will have a completely different experience with this book. I found the book disorganized with sections that should occur together in different sections or in the wrong order of how a user would approach a problem. For example, Chapter 3 goes over SQL coding but I wasn't sure what package we were supposed to be using or if this coding was used for all of them and, if so, what are the caveats to each. It would have been incredibly useful to discuss these three packages with strengths, weaknesses, and examples of each. It's great that there's code to use MySQL but it would have been useful to provide some information on the MySQL side so one could get going on queries without referring to yet another manual. Much of this information was redundant with other manuals that one should already have such as Introductory Statistics with R (Statistics and Computing), Modern Applied Statistics with S, and The R Book.
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4 of 4 people found the following review helpful
5.0 out of 5 stars An outstanding book on data handling in R January 4, 2010
Format:Paperback
After using R for many statistical analyses for several years, I finally began using R for manipulating data when my SAS license expired. This book has been essential, my copy is now completely dog-eared. I found that 150 pages was a good length to provide plenty of challenging, yet masterable, material. All is clear and well organized. I recommend this book without hesitation to those who want to move beyond spreadsheets for data manipulation, or who seek an alternative to commercial products.
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4 of 4 people found the following review helpful
5.0 out of 5 stars Great use of your money. May 21, 2009
Format:Paperback
This is a great book for new users to R, especially those who are used to Stata and other programs. Recoding, and working with data in different formats, in particular, can be challenges for new users, this book discusses these options in depth. I had been searching for a good data manipulation book on R for a while, since I was tired of having to go to websites constantly; this book serves that purpose.
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Most Recent Customer Reviews
4.0 out of 5 stars Good book
If you are unfamiliar with R or need a book that explains how to manipulate data with R then this is a good book. Read more
Published 6 months ago by JoeT
4.0 out of 5 stars Covers the basics and a bit more
This is a good book that really focus on data manipulation with R. It goes straight to the point and it covers all basic methods: reading from file, pipes, web. Read more
Published 16 months ago by Oscar Cassetti
4.0 out of 5 stars Good addition to my R library
So far, this looks like a good overview of information on handling data, most of which is available scattered throughout other R reference books. Read more
Published on December 7, 2010 by Watson
4.0 out of 5 stars Overpriced but very useful
When I first saw that this book was only 150 pages at the full retail price of $60, I balked at purchasing it. Instead, I borrowed it from the library and read it. Read more
Published on June 22, 2010 by J. R. Stevens
5.0 out of 5 stars Data manipulation with R
This book was just what I needed- now I just need to spend the time with it!
Published on August 18, 2009 by David R. Spurgin
1.0 out of 5 stars Not so useful book.
I am sorry to say that this book is not so useful.

You cannot find the answer to a simple question such as "To read into "R" a spreadsheet created in the Openoffice... Read more
Published on June 4, 2009 by N. Vadulam
5.0 out of 5 stars A Wonderful Reference
Whenever R is doing something I don't expect when managing data, this is the first book I turn to. Every page is filled with deep insights into how R works. Read more
Published on April 20, 2009 by Robert A. Muenchen
5.0 out of 5 stars An outstanding short book on an important topic
Data manipulation, in my opinion, is one of R's weak points, and I have found the help available directly in R to be inadequate to understanding how things work. Read more
Published on February 14, 2009 by Peter Flom
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