The Essential R Reference

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Product Description

An essential library of basic commands you can copy and paste into R

The powerful and open-source statistical programming language R is rapidly growing in popularity, but it requires that you type in commands at the keyboard rather than use a mouse, so you have to learn the language of R. But there is a shortcut, and that's where this unique book comes in. A companion book to Visualize This: The FlowingData Guide to Design, Visualization, and Statistics, this practical reference is a library of basic R commands that you can copy and paste into R to perform many types of statistical analyses.

Whether you're in technology, science, medicine, business, or engineering, you can quickly turn to your topic in this handy book and find the commands you need.

• Comprehensive command reference for the R programming language and a companion book to Visualize This: The FlowingData Guide to Design, Visualization, and Statistics
• Combines elements of a dictionary, glossary, and thesaurus for the R language
• Provides easy accessibility to the commands you need, by topic, which you can cut and paste into R as needed
• Covers getting, saving, examining, and manipulating data; statistical test and math; and all the things you can do with graphs
• Also includes a collection of utilities that you'll find useful

Simplify the complex statistical R programming language with The Essential R Reference.

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The Essential R Reference Review

I am surprised to see N. "Utterly Useless!" Vadulam give the book five stars, and surprised to see myself copying his "the book does not explain command X - one star!" style, but in this case an example will help.

R's "sample" function wants a vector of values to sample from - or a single positive integer, to sample from 1,2,...,N. R's doc ("?sample") says so, if only once, but gives an example of the second scenario and provides a little wrapper called "resample" to use if one does not like the default behavior. How does the book explain this little gotcha? See pp. 112-114. First, there is a section on "resample", having the doc's wrapper and the doc's example: the need for "resample" is attributed to "a computational quirk in the 'sample' command [which produces - DS] an unexpected result when a conditional sample is used". A section on "sample" proper follows, which does not mention the scalar-argument case at all.

What do you make of this case? In my opinion, the author did a copy-paste from the doc, but managed to confuse matters twice, by failing to explain the possible need for "resample" ("conditional sample" - what's that?), actually misrepresenting "sample" syntax, and putting "resample", not an R function, alongside and before "sample".

I get the thematically-clustered-list-of-functions approach, and would have appreciated an easy to hold and leaf through, well-indexed, compressed brochure, but this low-effort book offers neither convenience nor insight, and does not add value to R doc on your PC and the R book that you already have, assuming it's by Adler, Kabacoff or Crawley.

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