The Art of R Programming

404 Pages·2011·4.51 MB·English

by  Norman Matloff

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R is the world’s most popular language for developing • Interface R with C/C++ and Python for increased PPRROOGG RR AA MMMMIINNGG
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statistical software: Archaeologists use it to track the speed or functionality
spread of ancient civilizations, drug companies use it TT
• Find new packages for text analysis, image manipula-
to discover which medications are safe and effective,
tion, and thousands more
and actuaries use it to assess financial risks and keep OO A T O U R O F S T A T I S T I C A L S O F T W A R E D E S I G N
markets running smoothly. • Squash annoying bugs with advanced debugging
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techniques
The Art of R Programming takes you on a guided tour
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of software development with R, from basic types Whether you’re designing aircraft, forecasting the
N O R M A N M A T L O F F
and data structures to advanced topics like closures, weather, or you just need to tame your data, The Art of
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recursion, and anonymous functions. No statistical R Programming is your guide to harnessing the power
knowledge is required, and your programming skills of statistical computing. RR
can range from hobbyist to pro.
ABOUT THE AUTHOR OO
Along the way, you’ll learn about functional and object-
Norman Matloff is a professor of computer science GG
oriented programming, running mathematical simulations,
(and a former professor of statistics) at the University
and rearranging complex data into simpler, more useful
of California, Davis. His research interests include RR
formats. You’ll also learn to:
parallel processing and statistical regression, and
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• Create artful graphs to visualize complex data sets he is the author of several widely used web tutorials
and functions on software development. He has written articles for MM
the New York Times, the Washington Post, Forbes
• Write more efficient code using parallel R and MM
Magazine, and the Los Angeles Times, and he is the
vectorization
co-author of The Art of Debugging (No Starch Press).
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This book uses RepKover—a durable binding that won’t snap shut. STATISTICAL SOCOMPUTERS/MSHELVE IN: FF
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THE ART OF R
PROGRAMMING

THE ART OF R
PROGRAMMING
A Tour of Statistical
Software Design
by Norman Matloff
SanFrancisco
THEARTOFRPROGRAMMING.Copyright©2011byNormanMatloff.
Allrightsreserved.Nopartofthisworkmaybereproducedortransmittedinanyformorbyanymeans,electronic
ormechanical,includingphotocopying,recording,orbyanyinformationstorageorretrievalsystem,withoutthe
priorwrittenpermissionofthecopyrightownerandthepublisher.
1514131211 123456789
ISBN-10:1-59327-384-3
ISBN-13:978-1-59327-384-2
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LibraryofCongressCataloging-in-PublicationData
Matloff, Norman S.
The art of R programming : tour of statistical software design / by Norman Matloff.
p. cm.
ISBN-13: 978-1-59327-384-2
ISBN-10: 1-59327-384-3
1. Statistics-Data processing. 2. R (Computer program language) I. Title.
QA276.4.M2925 2011
519.50285'5133-dc23
2011025598
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mationcontainedinit.
BRIEF CONTENTS
Acknowledgments ...................................................................xvii
Introduction......................................................................... xix
Chapter1:GettingStarted ........................................................... 1
Chapter2:Vectors .................................................................. 25
Chapter3:MatricesandArrays....................................................... 59
Chapter4:Lists...................................................................... 85
Chapter5:DataFrames..............................................................101
Chapter6:FactorsandTables ........................................................121
Chapter7:RProgrammingStructures..................................................139
Chapter8:DoingMathandSimulationsinR ...........................................189
Chapter9:Object-OrientedProgramming..............................................207
Chapter10:Input/Output............................................................231
Chapter11:StringManipulation......................................................251
Chapter12:Graphics ...............................................................261
Chapter13:Debugging..............................................................285
Chapter14:PerformanceEnhancement: SpeedandMemory ............................305
Chapter15:InterfacingRtoOtherLanguages..........................................323
Chapter16:ParallelR ...............................................................333
AppendixA:InstallingR..............................................................353
AppendixB:InstallingandUsingPackages ............................................355

CONTENTS IN DETAIL
ACKNOWLEDGMENTS xvii
INTRODUCTION xix
WhyUseRforYourStatisticalWork? .............................................. xix
Object-OrientedProgramming ............................................ xvii
FunctionalProgramming.................................................. xvii
WhomIsThisBookFor? ..........................................................xviii
MyOwnBackground............................................................. xix
1
GETTING STARTED 1
1.1 HowtoRunR .............................................................. 1
1.1.1 InteractiveMode................................................ 2
1.1.2 BatchMode.................................................... 3
1.2 AFirstRSession............................................................ 4
1.3 IntroductiontoFunctions..................................................... 7
1.3.1 VariableScope................................................. 9
1.3.2 DefaultArguments .............................................. 9
1.4 PreviewofSomeImportantRDataStructures .................................. 10
1.4.1 Vectors,theRWorkhorse........................................ 10
1.4.2 CharacterStrings ............................................... 11
1.4.3 Matrices ....................................................... 11
1.4.4 Lists ........................................................... 12
1.4.5 DataFrames ................................................... 14
1.4.6 Classes ........................................................ 15
1.5 ExtendedExample: RegressionAnalysisofExamGrades....................... 16
1.6 StartupandShutdown ...................................................... 19
1.7 GettingHelp............................................................... 20
1.7.1 Thehelp()Function.............................................. 20
1.7.2 Theexample()Function.......................................... 21
1.7.3 IfYouDon’tKnowQuiteWhatYou’reLookingFor ................. 22
1.7.4 HelpforOtherTopics ........................................... 23
1.7.5 HelpforBatchMode............................................ 24
1.7.6 HelpontheInternet ............................................. 24
2
VECTORS 25
2.1 Scalars,Vectors,Arrays,andMatrices ....................................... 26
2.1.1 AddingandDeletingVectorElements............................. 26
2.1.2 ObtainingtheLengthofaVector ................................. 27
2.1.3 MatricesandArraysasVectors .................................. 28
2.2 Declarations ............................................................... 28
2.3 Recycling.................................................................. 29
2.4 CommonVectorOperations ................................................. 30
2.4.1 VectorArithmeticandLogicalOperations ......................... 30
2.4.2 VectorIndexing................................................. 31
2.4.3 GeneratingUsefulVectorswiththe: Operator..................... 32
2.4.4 GeneratingVectorSequenceswithseq() .......................... 33
2.4.5 RepeatingVectorConstantswithrep()............................. 34
2.5 Usingall()andany()........................................................ 35
2.5.1 ExtendedExample: FindingRunsofConsecutiveOnes ............. 35
2.5.2 ExtendedExample: PredictingDiscrete-ValuedTimeSeries.......... 37
2.6 VectorizedOperations...................................................... 39
2.6.1 VectorIn,VectorOut............................................ 40
2.6.2 VectorIn,MatrixOut............................................ 42
2.7 NAandNULLValues ....................................................... 43
2.7.1 UsingNA...................................................... 43
2.7.2 UsingNULL .................................................... 44
2.8 Filtering ................................................................... 45
2.8.1 GeneratingFilteringIndices...................................... 45
2.8.2 Filteringwiththesubset()Function ................................ 47
2.8.3 TheSelectionFunctionwhich() ................................... 47
2.9 AVectorizedif-then-else: Theifelse()Function ................................. 48
2.9.1 ExtendedExample: AMeasureofAssociation ..................... 49
2.9.2 ExtendedExample: RecodinganAbaloneDataSet ................ 51
2.10 TestingVectorEquality...................................................... 54
2.11 VectorElementNames...................................................... 56
2.12 Moreonc()................................................................ 56
3
MATRICES AND ARRAYS 59
3.1 CreatingMatrices .......................................................... 59
3.2 GeneralMatrixOperations.................................................. 61
3.2.1 PerformingLinearAlgebraOperationsonMatrices................. 61
3.2.2 MatrixIndexing ................................................ 62
3.2.3 ExtendedExample: ImageManipulation .......................... 63
3.2.4 FilteringonMatrices ............................................ 66
3.2.5 ExtendedExample: GeneratingaCovarianceMatrix............... 69
viii ContentsinDetail