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R in Action: Data Analysis and Graphics with R (Anglais) Broché – 1 septembre 2011

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Descriptions du produit

Présentation de l'éditeur


A practical guide to the use of R for statistical analysis and graphing.


The ability to interpret and act on the massive amounts of information

locked in web and enterprise systems is critical to success in the

modern business economy. R, a free software environment for

statistical computing and graphics, is a comprehensive package that

empowers developers and analysts to capture, process, and respond

intelligently to statistical information.

R in Action is the first book to present both the R system and the use

cases that make it such a compelling package for business developers.

The book begins by introducing the R language, and then moves on to

various examples illustrating R's features. Coverage includes data

mining methodologies, approaches to messy data, R’s extensive

graphical environment, useful add-on modules, and how to interface R

with other software platforms and data management systems.



• In-depth coverage of the R environment


• Dozens of practical examples


• Author Rob Kabacoff has years of hands-on experience

Biographie de l'auteur

Dr. Robert Kabacoff

has more than 20 years of experience providing

research and statistical consultation to organizations in health care, financial

services, manufacturing, behavioral sciences, government, and academia.

He is a former professor of psychology at Nova Southeastern University in

Florida, where he taught graduate courses in quantitative methods and

statistical programming. For the past two years, he has managed Quick-R, a

popuar R tutorial website.

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Détails sur le produit

  • Broché: 375 pages
  • Editeur : Manning Publications; Édition : 1 (1 septembre 2011)
  • Langue : Anglais
  • ISBN-10: 1935182390
  • ISBN-13: 978-1935182399
  • Dimensions du produit: 18,7 x 2,7 x 23,5 cm
  • Moyenne des commentaires client : 5.0 étoiles sur 5  Voir tous les commentaires (1 commentaire client)
  • Classement des meilleures ventes d'Amazon: 67.689 en Livres anglais et étrangers (Voir les 100 premiers en Livres anglais et étrangers)
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Dans ce livre

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Couverture | Copyright | Table des matières | Extrait | Index | Quatrième de couverture
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Par Hong le 9 juillet 2013
Format: Broché Achat vérifié
I am using R for almost my entire graduate study! And through this book, you can also learn programming by using R. And you can try matlab too. SUper nice part is the graphic!! Which is crucial for many biologist! ^_^ And of course for me!
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Commentaires client les plus utiles sur Amazon.com (beta)

Amazon.com: 37 commentaires
74 internautes sur 76 ont trouvé ce commentaire utile 
Go for the second edition 10 septembre 2011
Par Dimitri Shvorob - Publié sur Amazon.com
Format: Broché
... It's June 2015, and the second edition is finally out, after a year-plus of pushed-back release dates. The changes are incremental - and are identified in my review of "version 2" - but material (coverage of "ggplot" is expanded, for example), so I would suggest going with the new book.
36 internautes sur 36 ont trouvé ce commentaire utile 
Best R book I ever read 31 mai 2012
Par Junyu Lee - Publié sur Amazon.com
Format: Broché
The reason I picked this book because I read a article in a Chinese R BBS which recomanded this book as the best book for beginner. I checked Amazon reviews, it only had 3.4. Someone even gave it one star. I was glad I didn't take these reviews serously. Just as the saying: one man's food is another man's poison. I found this book was right for me.

First, there is no book which can cover everything of R, even the very common used part. If there is one, it might be the most boring book in the world. Second, the part you use day in day out, might not be the part others use frequently. Third, a lot of stuff, you have to learn when you have a problem to solve. The simple example is, when I started to learn read.table, I have no idea what all these arguments were and how to use them. Later, I had problem, I gradually learned how to use skip, nrows, row.names. There are still a lot, such as check.names, I have never used so far and don't know and don't care. I don't think a book should list all these stuff. That would be super boring.

I had taught myself R using The R Book before I read this one. I found the best of book, it taught you something you could put to use right away. I especially like its statistic part, simple, clear, and staight forward. It covers a lot of stuff usually not covered by most of books. For example, it explains the output of the summary (lm(x~y)) which I wanted to know but felt too embarrassed to ask because I thought everybody knew except me.

This book is right for beginner like me. As I said, if you want to learn how to build a car, how the engine works before you learn how to drive a car, this book is not for you. If you simple want to learn just how to drive plus a little bit maintenance, totally don't care how engine works, this is the right book for you. By the way, The R Book is a really good book for the R part. The statistic part is too complicate for me. Two much engine manufacture stuff.
11 internautes sur 11 ont trouvé ce commentaire utile 
If only I had this book when I was trying to do ANOVA back then... 18 février 2012
Par Emre Sevinc - Publié sur Amazon.com
Format: Broché
This book fills an important gap by introducing the basics of R and statistical data analysis from a very practical and pragmatic point of view. It has a broad coverage and after introducing basic data set manipulation techniques and commands, it goes on to describe many important statistical data analysis techniques from simple linear regression to more advanced methods such as ANOVA, power analysis, resampling, bootstrapping, generalized linear models, PCA, factor analysis, and handling missing values.

One of the nice features of the book is the description and discussion of many different visualization methods. The author, using many interesting and real world examples, shows how basic and more advanced visualization methods in R can be very helpful in exploring and understanding many different types of data sets.

The reader should be careful, though. This book does not dive into the gory details of all the topics it covers. Luckily the author is also aware of that, and he always mentions the good and detailed references for the readers who want to master the mathematical details. But make no mistake, some of the discussions about the pitfalls of some modeling techniques such as regression are quite adequate.

You should also bear in mind that this book is not a guide to programming in R in general, even though you'll be able to do many different types of data analysis after having finished this book, you'd definitely need a book like The Art of R Programming: A Tour of Statistical Software Design in order to develop your own sophisticated functions, modules and packages. Nevertheless I still consider R in Action is the perfect book for people who are curious about R and want to discover how they can utilize R to analyze real world data and come up with predictions.

I would easily give the book 5 stars if it also included the list of references. This is a huge omission and I want to believe that this was just an accident which will be corrected in the next edition. For example on page 111 it reads: "... recommend two excellent books that you'll find in the References section at the end of this book: Venables & Ripley (2000) and Chambers (2008).". But there is no References section at the end of the book! Thus you cannot learn more about Venables, Ripley and Chambers (you are left to your own Google skills).
14 internautes sur 16 ont trouvé ce commentaire utile 
Includes Topics That Others Skip 22 octobre 2011
Par Robert A. Muenchen - Publié sur Amazon.com
Format: Broché
I thoroughly enjoyed reading this book. Kabacoff's writing is clear and concise. He covers a wide range of statistics and graphics, including topics that many books skip, such as missing values analysis and interactive graphics. One of the best things about R is the huge number of packages available for it. But which ones are worth investigating? This book covers quite a few packages I have never used before, so even experienced R users will make many useful discoveries reading it. I have no doubt this book will become as popular as his web site, Quick-R (statmethods.net).
10 internautes sur 11 ont trouvé ce commentaire utile 
Good introduction to using the R language and environment 1 janvier 2013
Par Erik Gfesser - Publié sur Amazon.com
Format: Broché Achat vérifié
Quite a few texts on R have entered the marketplace over the last few years, so my decision to go with this text about a year ago was based on my survey of a number of reviews others have written for this and other books in this space, along with my personal experience with other Manning publications such as "Spring in Action", "Java Persistence with Hibernate", and "Hadoop in Action". In general, my experience working through this book has been pleasant, so I personally do not understand some of the complaints that other reviewers have made about the content that Kabacoff provides here.

That said, however, be forewarned that as with other texts, you should not expect to find all of your answers about R in this book. In my opinion, Manning publications are typically written in a format that fits well with the agile learning method with which I have grown accustomed during my consulting career. The author introduces topics along the way, sometimes more piecemeal that I would like, but his style forced me to explore other resources for more detail, bringing familiarity to other available resources. The number of plugin statistical packages for R has grown exponentially over the years (there are now over 2500), for example, so no book, not even "R in a Nutshell: A Desktop Quick Reference (Second Edition)", which I purchased recently, should be expected to be a one-stop shop.

This text quickly brought me up to speed with R language basics working with data sets, and introduced me to specifics with regard to R statistical methods and visualizations. Using R 2.15.0 for Windows, starting with the small data sets the author provides with which to run his examples, as well as sample data sets that the R language itself provides, I soon found myself working with larger data sets that the City of Chicago makes publicly available via its website, followed by using R at work. Your comfort level will be greater or lesser depending on your experience working with data.

As someone new to R, but not new to working with data, I especially appreciated the first five chapters that encompass the first of four parts in the book ("Introduction to R", "Creating a Dataset", "Getting Started with Graphs", "Basic Data Management", and "Advanced Data Management"). Like it or not, but as with any language, most data work revolves around first getting it into the correct format. After these first five chapters, the author walks the reader through basic graphs and statistics, followed by intermediate methods such as regression and analysis of variance (ANOVA), and advanced methods such as generalized linear models and more advanced graphics than was covered earlier in the book.
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