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R in a Nutshell (In a Nutshell)by Joseph Adler
Synopses & Reviews
Why learn R? Because it's rapidly becoming the standard for developing statistical software. R in a Nutshell provides a quick and practical way to learn this increasingly popular open source language and environment. You'll not only learn how to program in R, but also how to find the right user-contributed R packages for statistical modeling, visualization, and bioinformatics.
The author introduces you to the R environment, including the R graphical user interface and console, and takes you through the fundamentals of the object-oriented R language. Then, through a variety of practical examples from medicine, business, and sports, you'll learn how you can use this remarkable tool to solve your own data analysis problems.
"I am excited about this book. R in a Nutshell is a great introduction to R, as well as a comprehensive reference for using R in data analytics and visualization. Adler provides 'real world' examples, practical advice, and scripts, making it accessible to anyone working with data, not just professional statisticians."
--Martin Schultz, Arthur K. Watson Professor of Computer Science, Yale University
If youre considering R for statistical computing and data visualization, this book provides a quick and practical guide to just about everything you can do with the open source R language and software environment. Youll learn how to write R functions and use R packages to help you prepare, visualize, and analyze data. Author Joseph Adler illustrates each process with a wealth of examples from medicine, business, and sports.
Updated for R 2.14 and 2.15, this second edition includes new and expanded chapters on R performance, the ggplot2 data visualization package, and parallel R computing with Hadoop.
About the Author
Joseph Adler has years of experience working with lots of popular data mining packages, including databases (including Oracle, PostgreSQL, and MS Access), statistical analysis tools (SAS, SPSS, S-Plus, and R), and data mining tools (SAS Enterprise Miner, Insightful Miner, Oracle Data Mining, Weka, and SPSS Clementine). He is currently leading a project at Verisign to pick a data mining package for enterprise deployment.
Table of Contents
PrefaceR BasicsChapter 1: Getting and Installing RChapter 2: The R User InterfaceChapter 3: A Short R TutorialChapter 4: R PackagesThe R LanguageChapter 5: An Overview of the R LanguageChapter 6: R SyntaxChapter 7: R ObjectsChapter 8: Symbols and EnvironmentsChapter 9: FunctionsChapter 10: Object-Oriented ProgrammingWorking with DataChapter 11: Saving, Loading, and Editing DataChapter 12: Preparing DataData VisualizationChapter 13: GraphicsChapter 14: Lattice GraphicsChapter 15: ggplot2Statistics with RChapter 16: Analyzing DataChapter 17: Probability DistributionsChapter 18: Statistical TestsChapter 19: Power TestsChapter 20: Regression ModelsChapter 21: Classification ModelsChapter 22: Machine LearningChapter 23: Time Series AnalysisAdditional TopicsChapter 24: Optimizing R ProgramsChapter 25: BioconductorChapter 26: R and HadoopR ReferenceBibliographyColophon
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