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R in Action, Second Edition presents both the R language and the examples that make it so useful for business developers. Focusing on practical solutions, the book offers a crash course in statistics and covers elegant methods for dealing with messy and incomplete data that are difficult to analyze using traditional methods. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on time series analysis, cluster analysis, and classification methodologies, including decision trees, random forests, and support vector machines.
Business pros and researchers thrive on data, and R speaks the language of data analysis. R is a powerful programming language for statistical computing. Unlike general-purpose tools, R provides thousands of modules for solving just about any data-crunching or presentation challenge you're likely to face. R runs on all important platforms and is used by thousands of major corporations and institutions worldwide.
R in Action, Second Edition teaches you how to use the R language by presenting examples relevant to scientific, technical, and business developers. Focusing on practical solutions, the book offers a crash course in statistics, including elegant methods for dealing with messy and incomplete data. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on forecasting, data mining, and dynamic report writing.
This book is designed for readers who need to solve practical data analysis problems using the R language and tools. Some background in mathematics and statistics is helpful, but no prior experience with R or computer programming is required.
Dr. Rob Kabacoff is a seasoned researcher who specializes in data analysis. He has taught graduate courses in statistical programming and manages the Quick-R website at statmethods.net.
Essential to anyone doing data analysis with R, whether in industry or academia.
A go-to reference for general R and many statistics questions.
Accessible language, realistic examples, and clear code.
Offers a gentle learning curve to those starting out with R for the first time.
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