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25 Recipes for Getting Started with R
     

25 Recipes for Getting Started with R

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by Paul Teetor
 

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R is a powerful tool for statistics and graphics, but getting started with this language can be frustrating. This short, concise book provides beginners with a selection of how-to recipes to solve simple problems with R. Each solution gives you just what you need to know to use R for basic statistics, graphics, and regression.

You'll find recipes on reading data

Overview

R is a powerful tool for statistics and graphics, but getting started with this language can be frustrating. This short, concise book provides beginners with a selection of how-to recipes to solve simple problems with R. Each solution gives you just what you need to know to use R for basic statistics, graphics, and regression.

You'll find recipes on reading data files, creating data frames, computing basic statistics, testing means and correlations, creating a scatter plot, performing simple linear regression, and many more. These solutions were selected from O'Reilly's R Cookbook, which contains more than 200 recipes for R that you'll find useful once you move beyond the basics.

Product Details

ISBN-13:
9781449303235
Publisher:
O'Reilly Media, Incorporated
Publication date:
03/07/2011
Pages:
62
Product dimensions:
6.80(w) x 9.00(h) x 0.30(d)

Meet the Author

Paul Teetor is a quantitative developer with Masters degrees in statistics and computer science. He specializes in analytics and software engineering for investment management, securities trading, and risk management. He works with hedge funds, market makers, and portfolio managers in the greater Chicago area.

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25 Recipes for Getting Started with R 4 out of 5 based on 0 ratings. 1 reviews.
Guest More than 1 year ago
25 Recipes for Getting Started with R is a collection of short introductory tutorials ("recipes") derived from the R Cookbook by the same author. The goal is to show the reader how to perform basic tasks in R, a language for statistical analysis.Starting with the obligatory "installing R", the book moves through reading data files of various types, then it's off to recipes on various statistical functions. The recipe format is generally useful, making use of straightforward examples, then referring the reader to R's documentation for more detailed information.Be aware that this is not a statistics tutorial and no attempt is made to explain, for example, *why* you might want to compare the mean of two samples; the book simply tells you how to do something like that in R.I appreciated the author's ability to provide examples which did not involve unnecessary complications - each recipe solves the stated problem without requiring the reader to delve into tangential topics. This can be exactly what you need when starting out. The potential downside, of course, is that a book like this may not be a "keeper" once past the novice stage. Readers who intend to use R extensively would likely prefer to purchase the R Cookbook, which can provide a little more depth.Disclaimer, I received access to an electronic copy of this book for purposes of review.