THE R BOOK

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R is a high-level language and an environment for data analysis and graphics. . on the R help pages in writing this book, and I am extremely grateful to all the. Hugely successful and popular text presenting an extensive and comprehensive guide for all R users. The R language is recognized as one of the most powerful. The high-level language of R is recognized as one of the most powerful and flexible statistical software environments, and is rapidly becoming the standard.


The R Book

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The R book / Michael J. Crawley. – 2e. pages cm. Includes bibliographical references and index. ISBN (hardback). 1. The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that. Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful.

If you can find one that matches what you want to do and it is a big book , then it's great. If not, it's frustrating.

Still, the idea way of learning R is sharing a lab It's big. Still, the idea way of learning R is sharing a lab with someone who already knows it. Good for learning the R language. Disastrous if used to learn stats.

The R Book, 2nd Edition

Be forewarned: His knowledge of R is supreme. Of statistics, well, not so much. Crawley is a biologist first, statistician second, and it shows.

He displays a lack of fundamental training in statistics, as one might expect of a self-taught statistician. As a consequence, Good for learning the R language.

As a consequence, his book does a disservice to the unexperienced readers who naively assume he knows what he is talking about. Mar 14, Peter Flom rated it really liked it Shelves: The best thing about this large book on R is its breadth: No one book can cover all of R, but this covers a lot.

The biggest problem is the index and contents, which could have been better done and which could have made the book more usable. Ppl only use R for the packages, and at this point R only remains a useful and viable language bc of the way its development ecosystem has used packages to patch nearly the entire fucking language e.

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May 21, Lauren Adams rated it it was amazing. Jul 29, Sam rated it it was ok Shelves: It's hard to find a good R-Book.

This one did the job for me while I was learning, but you will be lost if you are not on r-seek constantly filling the gaps. This book also requires a strong stomach for poor writing and typos. I was an reviewer for the forthcoming "R In Action" and felt that it filled many of the holes this book contained.

R-seek and google will also be the ultimate R companion though. A fine and useful reference on the R programming language for my bookshelf. If you are learning or using R this is a great reference source.

Joan Carles Oliva.

Good recipe book for R if you already know statistics. If you don't, it gives a false sensation that you will, at the beginning, but soon gets too complex. R book is an essential book when working with R, especially for beginners! In addition, the 2nd edition now uses colors to differentiate the user-typed commands and the R-generated output. The images above do illustrate one continuing gripe I have about The R Book.

Many other R books wrap their data sets up into a R package that can be downloaded from CRAN and easily loaded into a R session with the library function.

The R Book asks you to first download all of the files, and then manually type out their location when you want to load them, i. In what should be taken as damning with faint praise, I will give Crawley credit for at least including a link to the location of the example data files in the 2nd edition of the book.

The files were always available for the 1st edition, the book just failed to mention that fact.

The 2nd edition also adds color to many of the figures. The vast majority of the text in the 2nd edition has remained unchanged from the 1st edition. Some of the material has been reorganized and expanded somewhat, particularly in the early chapter on the essentials of the R language. Write a customer review. Customer images. See all customer images. Read reviews that mention worked examples looks like excellent reference get started linear models essentials of the r language without explaining table of contents reference manual statistical theory time series second edition modern applied covers a lot much better reference book highly recommend statistical methods data analysis data frames.

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Book Review: The R Book, Second Edition (2013)

Given the length of this book, and the list of contents covered, I had the highest expectations about it. After spending 2 intensive months reading it, I have mixed feelings. Positive points are the large number of statistical models and methods described. The R examples are useful to follow the explanations, and the writing style is comprehensive. I agree with some reviewers in that the linear models section Chaps. The last Chapter also presents useful tricks for dealing with graphs in R.

Unfortunately, I have 2 important complaints. The first one is about the presentation of contents: The author systematically abuses of cross-references. You will find sentences like "here we present an example of [method XX] that will be introduced on page XXX" throughout the entire book.

This is disappointing, since it forces the reader to constantly move back and forth, looking for the relevant info. There is no point in presenting an example based on a method that you haven't introduced yet.

Examples should be autonomous, and not frequently taken from previous data sets "already used in page YYY". The second complaint derives from the previous one. The book is hard to use as both a reference manual and a companion for undergraduate or graduate students. Disregarding the comments from the author, if you don't have a solid theoretical background in statistical inference, regression analysis and linear models, you won't get very much benefit of this book.

The author completely lacks of a rigorous, structured method for presenting new concepts. Even worse, important definitions and concepts are usually hidden in between of examples that has nothing to do with them. In summary, if you already have a good theoretical background in statistics, this could be a useful add-on to your bookshelf though be ready to spend a lot of side tags to map important concepts for later.

If you're looking for a introductory book with R, Springer has just published a second, expanded edition of the classic book by Dalgaard. If you're looking for a definitive reference manual of statistical methods illustrated with R, you will have to wait for something else, or look for specific titles Like Faraway's "Linear Models with R". For Ph. I have been using R for about 5 years and for the last 2 years have I been using it regularly.

This book might have helped at the early stages of learning R, but at the moment I have learned not to trust this text. I am now seeing questions on the r-help mailing list related to the disorganization that others have commented upon.

The problems start early. For Windows users he uses doubled backslashes without explaining why these are needed or the alternative. He gets the distinction between the two indexing operators wrong when he states that one does not use "[" with lists and fails to note that dataframes are in point of fact lists. After starting with the R concept of vectors, the author introduces functions which have list arguments before even describing either lists or the list function.

I could go on and on. Going much further on to the section on count data he compares a dataset to the theoretic Poisson distribution noting that the counts at the low and the high ranges are larger than expected, calling this "highly aggregated", whereas most statisticians would call this over-dispersed.

In the next paragraph he makes the opposite statement but then says that the overly dispersed data "shows randomness". I was further bother by his frequent use of the dangerous practice of using attach and the confusing practice of naming objects using names that were also function names such as "exp" and "data".

I get the sense that the R News reviewer gave up noting errors and decided instead to move quickly on to recommending "Modern Applied Statistics" by Venables and Ripley. I would also suggest Harrell's "Regression Modeling Strategies". People sometimes complain that the R documentation in the help pages is difficult, but in my experience the R help pages are far more precise than Crawley's versions of R.

I would avoid this book due to a its disorganization, b its misleading advice regarding R syntax, and c its statistical errors. It probably has some value for its large number of worked examples but there are much better books available.

I've delayed writing a review for this book because my first impression was strongly negative. Unfortunately, a month or so with the book hasn't changed my impression. My first impression was somewhat superficial: I find the layout and typography is bad enough to be very distracting.It also allows you to accept potential citations to this item that we are uncertain about.

Michael J Crawley Author. Human and Ecological Risk Assessment. Mixed-Effects Models -- If you are an experienced programmer who wants to make robust applications in R, you need this book.

Features full colour text and extensive graphics throughout. Presents a fully revised and updated bibliography and reference section.

Introduces a clear structure with numbered section headings to help readers locate information more efficiently.

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