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Beginning R: The Statistical Programming Language Paperback – Jun 5 2012


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Product Description

From the Back Cover

Gain better insight into your data using the power of R

While R is very flexible and powerful, it is unlike most of the computer programs you have used. In order to unlock its full potential, this book delves into the language, making it accessible so you can tackle even the most complex of data analysis tasks. Simple data examples are integrated throughout so you can explore the capabilities and versatility of R. Along the way, you'll also learn how to carry out a range of commonly used statistical methods, including Analysis of Variance and Linear Regression. By the end, you'll be able to effectively and efficiently analyze your data and present the results.

Beginning R:

  • Discusses how to implement some basic statistical methods such as the t-test, correlation, and tests of association

  • Explains how to turn your graphs from merely adequate to simply stunning

  • Provides you with the ability to define complex analytical situations

  • Demonstrates ways to make and rearrange your data for easier analysis

  • Covers how to carry out basic regression as well as complex model building and curvilinear regression

  • Shows how to produce customized functions and simple scripts that can automate your workflow

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Wrox Beginning guides are crafted to make learning programming languages and technologies easier than you think, providing a structured, tutorial format that guides you through all the techniques involved.

Visit the Beginning R website at www.wrox.com/go/beginningr

About the Author

Dr. Mark Gardener is an ecologist, lecturer, and writer working in the UK. He is currently self-employed and runs courses in ecology, data analysis, and R for a variety of organizations.


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Amazon.com: 5 reviews
6 of 8 people found the following review helpful
Practice Problems May 8 2013
By BoroTurtle - Published on Amazon.com
Format: Paperback Verified Purchase
I have experience with statistics, SAS, and C programming, but I had no experience with R. The book has been quite useful for learning the basics of R programming. The features I particularly like are: (1) end of chapter summaries, (2) practice problems, with solutions at the end of the book, and (3) clearly written explanations of 'How It Works'.

The author covers how to use R to conduct statistical analyses that would be covered in a basic or advanced undergraduate statistics class. As noted in another review, the focus is on how to conduct an analysis (e.g., t-test), not why a t-test would be appropriate.

The book would be a great companion to a statistics book such as Howell's Statistical Methods for Psychology. Students who are enrolled in a statistics class or have had a statistics class and want to learn R may benefit the most from the book. The practice problems with solutions make it quite useful for self-teaching.
5 of 7 people found the following review helpful
Good for beginning the basics of data analysis Aug. 21 2013
By uberstomping - Published on Amazon.com
Format: Kindle Edition Verified Purchase
I read the book from the perspective of an experienced software developer and found that the book described R's functions and capabilties in a manner that was too simple and from my perspective and didn't require the level of details provided in the book. But with that being said, the book would be perfect for perhaps researchers or scientists with a less-strong programming background because it guides the users through the basics of beginning data analyses with R with thorough examples and exercises. I found the book requires a grasp of statistics to really appreciate the content (even though some concepts are explained). Only one chapter seemed relevant for the actual "programming" of R. On the other hand I got a decent enough overview of the nature of functions built into R.
7 of 11 people found the following review helpful
Not an Intro book for non programmers. Nov. 20 2012
By Not_A_Bot_12 - Published on Amazon.com
Format: Kindle Edition Verified Purchase
Updating my review and raised it a star...
This is a good third level book. It makes very little use of any 3rd party packages. Just native R functions.

After you have played with R for a semester, this may be a good follow on book. It will improve your R knowledge and decide how deep you want to go into programming with R.

There is 5 different ways to do stuff, so ever book shows a different way. Not relaying on packages gives some "behind the scenes" on what is happening.

********Original Review********
The book really shouldn't start with Beginning R. On the book cover it says programmer to programmer in small text in the upper right hand corner. Programmers is who the book is directed at.

I bought the book because it had Beginning in the title and the kindle version price was reasonable.

1. The book assumes you have the required math/statistics knowledge already.

2. The book assumes you have a good understanding of programming already.

Based on my quick look at the book last night this isn't a book for someone familiar with the research methods based approach. The use of the word "independent" is only 3 times in the context of "independent variable" in the entire book. There is 3 sample PDFs on the books web site. just google for it.

I will update my review in a couple months after I have progressed in my knowledge more, but wanted to help out those who were considering this book.

Also the "R for dummies" and "Art of R programming" are not intro books either. The R for dummies book is more disappointing as dummies books are usually good intro books.
Five Stars Nov. 28 2014
By Pallassana K Kannan - Published on Amazon.com
Format: Paperback Verified Purchase
A very good starter for students learning statistical programming in R. Highly recommend it!
0 of 1 people found the following review helpful
Good value Aug. 17 2014
By Dimitri Shvorob - Published on Amazon.com
Format: Paperback
I have given Mark Gardener grief over his "Essential R" and, sorry, this book, too, does not scream "painstaking writing", "originality" or "teaching expertise". And yet the overall impression is quite positive: the author's R experience and straightforward writing, and simple but attractive typesetting, produce a nice collection of R vignettes, spanning R basics and simple statistics. Coverage is by no means comprehensive, and "base" R alone is discussed - a problem, as so many "add-on" packages have become standard - but the book does cover a lot of ground, and does not cost much. I would still recommend Robert Kabacoff's "R in Action" as the best introduction to R, but for the more timid beginners, "Beginning R", alongside Paul Teetor's "R Cookbook", could be a sensible starting point.


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