R for Dummies - 2nd Edition by Andrie De Vries & Joris Meys (Paperback)
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<p/><br></br><p><b> About the Book </b></p></br></br>This accessible guide is the ideal introduction to R for complete beginners. Learn to master the programming language of choice among statisticians and data analysts worldwide.<p/><br></br><p><b> Book Synopsis </b></p></br></br><b>Mastering R has never been easier</b> <p>Picking up R can be tough, even for seasoned statisticians and data analysts. <i>R For Dummies</i>, <i>2<sup>nd</sup> Edition</i> provides a quick and painless way to master all the R you'll ever need. Requiring no prior programming experience and packed with tons of practical examples, step-by-step exercises, and sample code, this friendly and accessible guide shows you how to know your way around lists, data frames, and other R data structures, while learning to interact with other programs, such as Microsoft Excel. You'll learn how to reshape and manipulate data, merge data sets, split and combine data, perform calculations on vectors and arrays, and so much more.</p> <p>R is an open source statistical environment and programming language that has become very popular in varied fields for the management and analysis of data. R provides a wide array of statistical and graphical techniques, and has become the standard among statisticians for software development and data analysis. <i>R For Dummies, 2<sup>nd</sup> Edition</i> takes the intimidation out of working with R and arms you with the knowledge and know-how to master the programming language of choice among statisticians and data analysts worldwide.</p> <ul> <li>Covers downloading, installing, and configuring R</li> <li>Includes tips for getting data in and out of R</li> <li>Offers advice on fitting regression models and ANOVA</li> <li>Provides helpful hints for working with graphics</li> </ul> <p><i>R For Dummies, 2<sup>nd</sup> Edition</i> is an ideal introduction to R for complete beginners, as well as an excellent technical reference for experienced R programmers.</p><p/><br></br><p><b> From the Back Cover </b></p></br></br><p><b><i>Learn to: </i></b> <ul> <li>Use R for data analysis and processing</li> <li>Write functions and scripts for repeatable analysis</li> <li>Create high-quality charts and graphics</li> <li>Perform statistical analysis and build models</li> </ul> <p><b>All the R you need for statistical and data analysis, explained in plain English!</b> <p>Do you have to use R on the job, but you don't know much about programming? Relax! Here's a quick and painless guide to using R for all sorts of statistical and data analysis. Through real-world examples and step-by-step exercises you'll learn how to reshape and manipulate data, perform calculations, create graphical representations, and more. <ul> <li><b>Get started with R --</b> acquire and install R, learn what makes R unique, compare code editors, and write your first R script</li> <li><b>Play well with others --</b> see how R interacts with Excel and other programs and why statisticians and data analysts favor it</li> <li><b>R ABCs --</b> make your code clear and explore functions and arguments</li> <li><b>A calculating side --</b> take advantage of R's calculating power, working with numbers, infinity, missing values, mathematical functions, and vectors</li> <li><b>Get textual --</b> use character vectors for text data, manipulate text, and work with strings and factors</li> <li><b>Data tells a tale --</b> manipulate and process data using subsets, merge data from different sources, sort it, and summarize using statistical techniques</li> <li><b>Go graphic --</b> present your data visually using plots and lattice graphics</li> </ul> <p><b>Open the book and find: </b> <ul> <li>User packages that extend R</li> <li>Tips for getting data into and out of R</li> <li>How R handles basic arithmetic</li> <li>What to do with regression models</li> <li>Advice on managing the logical flow</li> <li>Effective debugging strategies</li> <li>How to incorporate dates and times</li> <li>Additional graphics packages you can use</li> </ul><p/><br></br><p><b> About the Author </b></p></br></br><p><b>Andrie de Vries</b> is a leading R expert and Business Services Director for Revolution Analytics. With over 20 years of experience, he provides consulting and training services in the use of R. <b>Joris Meys</b> is a statistician, R programmer and R lecturer with the faculty of Bio-Engineering at the University of Ghent.
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