R in action : data analysis and graphics with R

By:

Kabacoff, Robert I

Material type: Text

TextPublication details: New York: Manning Publications Co., 2015.Edition: 2nd edDescription: xxviii, 579pISBN:

9781617291388

Subject(s):

DDC classification:

519.502855 KAB KAB

Summary: 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.

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Holdings
Item type	Current library	Call number	Status	Barcode
Book	International Management Institute New Delhi General stacks	519.502855 KAB (Browse shelf(Opens below))	Available	22144

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Previous	338.927 BRI Introduction to sustainability	338.927 ECO The Economics of sustainable transformation	519.3 FER Course in game theory	519.502855 KAB R in action : data analysis and graphics with R	519.5028554 FIE Discovering statistics using IBM SPSS statistics	519.535 HAH Applied multivariate statistical concepts	612.821 WAL Why we sleep : the new science of sleep and dreams	Next

Includes bibliographical references and index.

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.

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