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Loading... ## The R Book (edition 2007)## by Michael J. Crawley
## Work detailsThe R Book by Michael J. Crawley
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Sign up for LibraryThing to find out whether you'll like this book. No current Talk conversations about this book. as a reference guide for mostly-vanilla R, its perfect. but mostly-vanilla R is an atrocious mess, an international emergency, etc. 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.g. see hadley wickham's work). so i honestly cant rec this, as ur prolly better off reading a mix of blog posts, data-sci books, and hadley wickham's stuff ( ) Poorly presented and badly organized -- a real disappointment. Desperate for information on R when first starting out, I searched the internet for any resource I could find. Fortunately most of the resources were free, unfortunately, very few were helpful to a novice user. Instead I turned to finding a book that provided comprehensive coverage of R and it’s hundreds of add-on modules. I immediately stumbled across The R Book by Michael J Crawley. Undismayed by the high price ($110 new), I placed my order. Now that my copy of The R Book is sufficiently creased and worn, I feel comfortable enough to write my review. Where The R Book makes up in coverage it lacks in depth. Do not expect to find detailed explanations on advanced statistics. For example, the chapter on Mulivariate Statistics is 15 pages long and contains no more than a paragraph on neural networks. Many of the explanations of functions seem to skip major strokes and are not always complete. Now that my criticisms are out of the way, I’ll move on to the pro’s. The R Book is, as I mentioned above, the most thoroughly comprehensive source for R on the market. A serious R user should maintain a copy, at least as a reference book. For novices, the book does contain explanations and guidance for statistical models using R. Crawley’s coverage of statistical models and classical tests are adequate. For example, he takes the time to explain appropriate model types by type of response variable (i.e.-proportional, count, etc). He even includes a fairly brief but adequate explanation of Logistic Regression. Expect a more thorough explanation of classical statistical methods in the first three-quarters of the book and a more brief explanation of more advanced methods at the end. To the novice user, Crawley’s explanations may seem overwhelmingly complex and rife with statistical jargon; afterall, R is used primarily by statisticians. However, any resourceful student or professional with a basic understanding of statistics will find enough resources on the web in order to aid their reading. Additionally, Crawley provides adequate coverage of R’s graphics capabilities. However, if you are or plan to be a sophisticated R graphics user, I would recommend purchasing a book that covers graphics specifically. The R Book is recommended reading for novice to expert users. The former as a both a reference and a learning tool and the latter as a reference book. The $110 is not a stretch for those of us used to paying high prices for textbooks, afterall The R Book contains nearly 900 pages. - ryan@rstatx.com www.rstatx.com I have about ten R or S books now. This one is the best general R text I've used. Previously, I had to make my own R guide based on things found in various other texts in order to look something up, but Crawley's text seems to be sufficiently organized and comprehensive to permit me to look up answers to most of my R questions. no reviews | add a review
References to this work on external resources. ## Wikipedia in EnglishNone
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 setting for quantitative analysis, statistics and graphics. R provides free access to unrivalled coverage and cutting-edge applications, enabling the user to apply numerous statistical methods ranging from simple regression to time series or multivariate analysis. |
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