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RObject-orientedProgramming

TableofContents

Credits

AbouttheAuthor

AbouttheReviewers

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Preface

Whatthisbookcovers

Whatyouneedforthisbook

Whothisbookisfor

Conventions

Readerfeedback

Customersupport

Downloadingtheexamplecode

Errata

Copyrightviolations

Questions

1.DataTypes

Assignment

Theworkspace

Discretedatatypes

Integer

Logical

Character

Factors

Continuousdatatypes

Double

Complex

Specialdatatypes

Notesontheasandisfunctions

Summary

2.OrganizingData

Basicdatastructures

Vectors

Dataframes

Tables

Matricesandarrays

Censoringdata

Appendingrowsandcolumns

Operationsondatastructures

Theapplycommands

lapplyandsapply

tapply

mapply

Summary

3.SavingDataandPrintingResults

Fileanddirectoryinformation

Enteringdata

Enteringdatafromthecommandline

Readingtablesfromfiles

CSVfiles

Fixed-widthfiles

Printingresultsandsavingdata

Savingaworkspace

Thecatcommand

Theprint,format,andpastecommands

Primitiveinput/output

Networkoptions

Openingasocket

Basicsocketoperations

Summary

4.CalculatingProbabilitiesandRandomNumbers

Overview

Distributionfunctions

Cumulativedistributionfunctions

Inversecumulativedistributionfunctions

Generatingpseudorandomnumbers

Sampling

Summary

5.CharacterandStringOperations

Basicstringoperations

Sixfocusedtasks

Determiningthelengthofastring

Locationofasubstring

Extractingorchangingasubstring

Transformingthecase

Splittingstrings

Creatingformattedstrings

Regularexpressions

Summary

6.ConvertingandDefiningTimeVariables

Introductionandassumptions

Convertingstringstotimedatatypes

Convertingtimedatatypestostrings

Operationsontimedatatypes

Summary

7.BasicProgramming

Conditionalexecution

Loopconstructs

Theforloop

Thewhileloop

Therepeatloop

Breakandnextstatements

Functions

Definingafunction

Argumentstofunctions

Executingscripts

Summary

8.S3Classes

Definingclassesandmethods

Definingobjectsandinheritance

Encapsulation

Afinalnote

Summary

9.S4Classes

IntroducingtheAntclass

DefininganS4class

DefiningmethodsforanS4class

Definingnewmethods

Polymorphism

Extendingtheexistingmethods

Inheritance

Miscellaneousnotes

Summary

10.CaseStudy–CourseGrades

Overview

TheCourseclass

ThedefinitionoftheCourseclass

Readinggradesfromafile

Theassignmentclasses

TheNumericGradeclass

TheLetterGradeclass

Example–readinggradesfromafile

Definingindexingoperations

Redefiningexistingfunctions

Redefiningarithmeticoperations

Summary

11.CaseStudy–Simulation

Thesimulationclasses

TheMonte-Carloclass

Examples

Summary

A.PackageManagement

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CreditsAuthor

KellyBlack

Reviewers

MzabalazoZ.Ngwenya

PrabhanjanTattar

TengfeiYin

CommissioningEditor

AkramHussain

AcquisitionEditor

RichardBrookes-Bland

ContentDevelopmentEditor

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TechnicalEditor

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CopyEditors

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Graphics

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AbouttheAuthorKellyBlackisafacultymemberintheDepartmentofMathematicsatClarksonUniversity.Hisbackgroundisinnumericalanalysiswithafocusontheuseofspectralmethodsandstochasticdifferentialequations.HemakesextensiveuseofRintheanalysisoftheresultsofMonte-Carlosimulations.

InadditiontousingRforhisresearchinterests,KellyalsousestheRenvironmentforhisstatisticsclasses.HehasextensiveexperiencesharinghisexperienceswithRintheclassroom.TheuseofRtoexploredatasetsisanimportantpartofthecurriculum.

Iwouldliketothankmywifeanddaughterfortheirsupportandinspiration.Youaremylife,andwedothistogether.

Ialsowishtothanktothankthepeoplewhotookadirectroleinbringingthisbooktocompletion.Inparticular,thanksgototheContentDevelopmentEditor,ParitaKhedekar,forherworkinassemblingeverything,keepingmeontrack,andensuringtheoverallintegrityofthisbook.AdditionalthanksgototheTechnicalEditor,TanviBhatt,formaintainingtheintegrityofthebookasawholeand,technicalguidance.Thankyou!

Iwouldalsoliketothankthereviewers.Unfortunately,someofyourinsightswerenotabletobeintegratedintothiswork.Ididreadyourreviewsandvaluedthem.Itriedtobalanceyourconcernstothebestofmyability.Thankyouaswell.

AbouttheReviewersMzabalazoZ.Ngwenyahasworkedextensivelyinthefieldofstatisticalconsultingandcurrentlyworksasabiometrician.HehasanMScinMathematicalStatisticsfromtheUniversityofCapeTownandispresentlystudyingforaPhD.Hisresearchinterestsincludestatisticalcomputing,machinelearning,andspatialstatistics.Previously,hewasinvolvedinreviewingPacktPublishing’sLearningRStudioforRStatisticalComputing,RStatisticalApplicationDevelopmentbyExampleBeginner’sGuide,andMachineLearningwithR.

PrabhanjanTattariscurrentlyworkingasaBusinessAnalysisSeniorAdvisoratDellGlobalAnalytics,Dell.Hehas7yearsofexperienceasastatisticalanalyst.Survivalanalysisandstatisticalinferencearehismainareasofresearch/interest,andhehaspublishedseveralresearchpapersinpeer-reviewedjournalsandalsoauthoredtwobooksonR:RStatisticalApplicationDevelopmentbyExample,PacktPublishing,andACourseinStatisticswithR,NarosaPublishing.TheRpackages,gpkandRSADBEarealsomaintainedbyhim.

TengfeiYinearnedhisPhDinMolecular,Cellular,andDevelopmentalBiology(MCDB)withafocusoncomputationalbiologyandbioinformaticsfromIowaStateUniversity,withaminorinStatistics.Hisresearchinterestsincludeinformationvisualization,high-throughputbiologicaldataanalysis,datamining,andappliedstatisticalgenetics.HehasdevelopedandmaintainedseveralsoftwarepackagesinRandBioconductor.

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PrefaceTheRenvironmentisapowerfulsoftwaresuitethatstartedasamodelfortheSlanguageoriginallydevelopedatBellLaboratories.TheoriginalcodebasewascreatedbyRossIhakaandRobertGentlemanin1993.Itrapidlygrewwiththehelpofothers,andithassincebecomeastandardinstatisticalcomputing.Thesoftwaresuiteitselfhasgrownwellbeyondanimplementationofalanguageandhasbecomean“environment”.Itisextensible,andthewidevarietyofpackagesthatareavailablehelpmakeitapowerfulresourcethatcontinuestogrowinpopularityandpower.

OuraiminthisbookistoprovidearesourceforprogrammingusingtheRlanguage,andweassumethatyouwillbemakinguseoftheRenvironmenttoimplementandtestyourcode.Thebookcanberoughlydividedintofourparts.Inthefirstpart,weprovideadiscussionofthebasicideasandtopicsnecessarytounderstandhowRclassifiesdataandtheoptionsthatcanbeusedtomakecalculationsfromdata.Inthesecondpart,weprovideadiscussionofhowRorganizesdataandtheoptionsavailabletokeeptrackofdata,displaydata,andreadandsavedata.Inthethirdpart,weprovideadiscussiononprogrammingtopicsspecifictotheRlanguageandtheoptionsavailableforobject-orientedprogramming.Inthefourthpart,weprovideseveralextendedexamplesasawaytodemonstratehowallofthetopicscanfittogethertosolveproblems.

WhatthisbookcoversAlistofthechaptersisgivenhere.Thefirstthreechaptersfocusonthebasicrequirementsassociatedwithgettingdataintothesystemandthemostbasictasksassociatedwithcalculationsassociatedwithdata.Thenextthreechaptersfocusonthemiscellaneousissuesthatariseinpracticewhenworkingwithandexaminingdataincludingthemechanicsofdealingwithdifferentdatatypes.Thenextthreechaptersfocusonbasicandadvancedprogrammingtopics.Thefinalthreechaptersprovidemoredetailedexamplestodemonstratehowalloftheideascanbebroughttogethertosolveproblems.

Chapter1,DataTypes,offersabroadoverviewofthedifferentdatatypes.Thisincludesbasicrepresentationssuchasfloat,double,complex,factors,andintegerrepresentations,anditalsoincludesexamplesofhowtoentervectorsthroughtheinteractiveshell.AbriefdiscussionofthemostbasicoperationsandhowtointeractwiththeRshellisalsogiven.

Chapter2,OrganizingData,offersamoredetailedlookatthewaydataisorganizedwithintheRenvironment.Additionaltopicsincludehowtoaccessthedataaswellashowtoperformbasicoperationsonthevariousdatastructures.Theprimarydatastructuresexaminedarelists,arrays,tables,anddataframes.

Chapter3,SavingDataandPrintingResults,offersadetailedlookatthewaystobringdataintotheRenvironmentandbuildsonthetopicsdiscussedinthepreviouschapter.Additionaltopicsrevolvearoundthewaystodisplayresultsaswellasvariouswaystosavedata.

Chapter4,CalculatingProbabilitiesandRandomNumbers,offersadetailedexaminationoftheprobabilityandsamplingfeaturesoftheRlanguage.TheRenvironmentincludesanumberoffeaturestoaidinthewaydatacanbeanalyzed.Anystatisticalanalysisincludesanunderlyingrelianceonprobability,anditisatopicthatcannotbeignored.TheavailabilityofawidevarietyofprobabilityandsamplingoptionsisoneofthestrengthsoftheRlanguage,andweexploresomeoftheoptionsinthischapter.

Chapter5,CharacterandStringOperations,offersadetailedexaminationofthevariousoptionsavailableforexamining,testing,andperformingoperationsonstrings.Thisisanimportanttopicbecauseitisnotuncommonfordatasetstohaveinconsistencies,andaroutinethatreadsdatafromafileshouldincludesomebasicchecks.

Chapter6,ConvertingandDefiningTimeVariables,offersadetailedexaminationofthetimedatastructure.Abasicintroductionisgiveninthefirstchapter,andmoredetailsareprovidedinthischapter.Theprevalenceoftime-relateddatamakesthetopicofthesedatastructurestooimportanttoignore.

Chapter7,BasicProgramming,offersadetailedexaminationofthemostbasicflowcontrolsandprogrammingfeaturesoftheRlanguage.Thechapterprovidesdetailsaboutconditionalexecutionaswellasthevariousloopingconstructs.Additionally,mundanetopicsassociatedwithwritingprograms,execution,andformattingarealsodiscussed.

Chapter8,S3Classes,offersadetailedexaminationofS3classes.Thisisthefirstand

mostcommonapproachtoobject-orientedprogramming.TheuseofS3classescanbeconfusingtopeoplealreadyfamiliarwithobject-orientedprogramming,buttheirflexibilityhasmadethemapopularwaytoapproachobject-orientedprogramminginR.

Chapter9,S4Classes,offersadetailedexaminationofS4classes.Thisisamorerecentapproachtoobject-orientedprogrammingcomparedtoS3classes.Itisamorestructuredapproachandismorefamiliartopeoplewhohaveexperiencewithobject-orientedprogramming.

Chapter10,CaseStudy–CourseGrades,offersanin-depthexampleofagrade-trackingapplication.Thisisthefirstofthreeexamples,anditisthesimplestexample.Itwaschosenasitissomethingthatislikelytobemorefamiliartoawiderrangeofpeople.

Chapter11,CaseStudy–Simulation,offersanin-depthexampleofanapplicationthatisusedtogeneratedatabasedonMonte-Carlosimulations.Theapplicationdemonstrateshowanobject-orientedapproachcanbeusedtocreateanenvironmentusedtoexecutesimulations,organizetheresults,andperformabasicanalysisontheresults.

Chapter12,CaseStudy–Regression,offersanin-depthexampleofanapplicationthatoffersawiderangeofoptionsyoucanusetoperformregression.Regressionisacommontaskandoccursinawidevarietyofcontexts.Theapplicationthatisdevelopeddemonstratesaflexiblewaytohandlebothcontinuousandordinaldataasawaytodemonstratetheuseofaflexibleobject-orientedapproach.Youcandownloadthischapterformhttps://www.packtpub.com/sites/default/files/downloads/6682OS_Case_Study_Regression.pdf

Appendix,PackageManagement,givesabriefoverviewofinstalling,updating,andremovingpackagesisgiven.PackagesarelibrariesthatcanbeaddedtoRthatextenditscapabilities.BeingabletoextendRandmakeuseofotherlibrariesrepresentsoneR’smorepowerfulfeatures.

WhatyouneedforthisbookItisassumedthatyouwillbeworkingintheRenvironment,andtheexamplecodehasbeendevelopedandtestedforRversion3.0.1andlater.TheRenvironmentisatypeoffreesoftwareandismadeavailablethroughtheeffortsandgenerosityoftheRFoundation.Itcanbedownloadedfromhttp://www.r-project.org/.ThematerialinthefirsthalfofthebookassumesthatyouhaveaccesstoRandcanworkfromtheinteractivecommandlinewithintheRenvironment.Thematerialinthesecondhalfofthebookassumesthatyouarefamiliarwithprogrammingandcanwriteandsavecomputercode.Ataminimum,youshouldhaveaccesstoaprogrammingeditorandshouldbefamiliarwithdirectorystructuresandsearchpaths.

WhothisbookisforIfyouarefamiliarwithprogrammingandwishtogainabasicunderstandingoftheRenvironmentandlearnhowtocreateprogrammingapplicationsusingtheRlanguage,thisisthebookforyou.ItisassumedthatyouhavesomeexposuretotheRenvironmentandhaveabasicunderstandingofR.Thisbookdoesnotprovideextensivemotivationsforcertainapproachesandpracticesassumingthatthereaderiscomfortableinthedevelopmentofsoftwareapplications.

ConventionsInthisbook,youwillfindanumberofstylesoftextthatdistinguishbetweendifferentkindsofinformation.Herearesomeexamplesofthesestyles,andanexplanationoftheirmeaning.

Codewordsintext,databasetablenames,foldernames,filenames,fileextensions,pathnames,dummyURLs,userinput,andTwitterhandlesareshownasfollows:“Alistiscreatedusingthelistcommand,andavariablecanbetestedorcoercedusingtheis.listandas.listcommands.”

Ablockofcodeissetasfollows:

>x=rnorm(5,mean=10,sd=3)

[1]11.1727198.78428410.0740355.73517110.800138

>pnorm(abs(x-10),mean=0,sd=3)-pnorm(-abs(x-10),mean=0,sd=3)

[1]0.304133630.314698030.019688490.844860370.21030971

Whenwewishtodrawyourattentiontoaparticularpartofacodeblock,therelevantlinesoritemsaresetinbold:

>v<-c(1,3,5,7,-10)

[1]1357-10

>v[2]<-v[1]-v[5]

[1]11157-10

Newtermsandimportantwordsareshowninbold.

NoteWarningsorimportantnotesappearinaboxlikethis.

TipTipsandtricksappearlikethis.

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Chapter1.DataTypesInthischapter,weprovideabroadoverviewofthedifferentdatatypesavailableintheRenvironment.Thismaterialisintroductoryinnature,andthischapterensuresthatimportantinformationonimplementingalgorithmsisavailabletoyou.Thereareroughlyfivepartsinthischapter:

WorkingwithvariablesintheRenvironment:ThissectiongivesyouabroadoverviewofinteractingwiththeRshell,creatingvariables,deletingvariables,savingvariables,andloadingvariablesDiscretedatatypes:ThissectiongivesyouanoverviewoftheprincipledatatypesusedtorepresentdiscretedataContinuousdatatypes:ThissectiongivesyouanoverviewoftheprincipledatatypesusedtorepresentcontinuousdataIntroductiontovectors:ThissectiongivesyouanintroductiontovectorsandmanipulatingvectorsinRSpecialdatatypes:Thissectiongivesyoualistofotherdatatypesthatdonotfitintheothercategoriesorhaveothermeanings

AssignmentTheRenvironmentisaninteractiveshell.Commandsareenteredusingthekeyboard,andtheenvironmentshouldfeelfamiliartoanyoneusedtoMATLABorthePythoninteractiveinterpreter.Toassignavaluetoavariable,youcanusuallyusethe=symbolinthesamewayastheseotherinterpreters.ThedifferencewithR,however,isthatthereareotherwaystoassignavariable,andtheirbehaviordependsonthecontext.

Anotherwaytoassignavaluetoavariableistousethe<-symbols(sometimescalledoperators).Atfirstglance,itseemsoddtohavedifferentwaystoassignavalue,butwewillseethatvariablescanbesavedindifferentenvironments.Thesamenamemaybeusedindifferentenvironments,andthenamecanbeambiguous.Wewilladopttheuseofthe<-operatorinthistextbecauseitisthemostcommonoperator,anditisalsotheleastlikelytocauseconfusionindifferentcontexts.

TheRenvironmentmanagesmemoryandvariablenamesdynamically.Tocreateanewvariable,simplyassignavaluetoit,asfollows:

Avariablehasascope,andthemeaningofavariablenamecanvarydependingonthecontext.Forexample,ifyourefertoavariablewithinafunction(thinksubroutine)orafterattachingadataset,thentheremaybemultiplevariablesintheworkspacewiththesamename.TheRenvironmentmaintainsasearchpathtodeterminewhichvariabletouse,andwewilldiscussthesedetailsastheyarise.

The<-operatorfortheassignmentwillworkinanycontextwhilethe=operatoronlyworksforcompleteexpressions.Anotheroptionistousethe<<-operator.Theadvantageofthe<<-operatoristhatitinstructstheRenvironmenttosearchparentenvironmentstoseewhetherthevariablealreadyexists.Insomecontexts,withinafunctionforexample,the<-operatorwillcreateanewvariable;however,the<<-operatorwillmakeuseofanexistingvariableoutsideofthefunctionifitisfound.

Anotherwaytoassignvariablesistousethe->and->>operators.Theseoperatorsaresimilartothosegivenpreviously.Theonlydifferenceisthattheyreversethedirectionofassignment,asfollows:

>14.5->a

>1/12.0->>b

[1]14.5

[1]0.08333333

TheworkspaceTheRenvironmentkeepstrackofvariablesaswellasallocatesandmanagesmemoryasitisrequested.Onecommandtolistthecurrentlydefinedvariablesisthelscommand.Avariablecanbedeletedusingthermcommand.Inthefollowingexample,theaandbvariableshavebeenchanged,andtheavariableisdeleted:

>a<-17.5

>b<-99/4

[1]"a""b"

>objects()

[1]"a""b"

>rm(a)

[1]"b"

Ifyouwishtodeleteallofthevariablesintheworkspace,thelistoptioninthermcommandcanbecombinedwiththelscommand,asfollows:

[1]"b"

>rm(list=ls())

character(0)

Awidevarietyofotheroptionsareavailable.Forexample,therearedirectoryoptionstoshowandsetthecurrentdirectory,asfollows:

>getwd()

[1]"/home/black"

>setwd("/tmp")

>getwd()

[1]"/tmp"

>dir()

[1]"antActivity.R""betterS3.R"

[3]"chiSquaredArea.R""firstS3.R"

[5]"math100.csv""opsTesting.R"

[7]"probabilityExampleOne.png""s3.R"

[9]"s4Example.R"

Anotherimportanttaskistosaveandloadaworkspace.Thesaveandsave.imagecommandscanbeusedtosavethecurrentworkspace.Thesavecommandallowsyoutosaveaparticularvariable,andthesave.imagecommandallowsyoutosavetheentireworkspace.Theusageofthesecommandsisasfollows:

>save(a,file="a.RData")

>save.image("wholeworkspace.Rdata")

Thesecommandshaveavarietyofoptions.Forexample,theasciioptionisacommonlyusedoptiontoensurethatthedatafileisina(nearly)human-readableform.Thehelpcommandcanbeusedtogetmoredetailsandseemoreoftheoptionsthatareavailable.Inthefollowingexample,thevariableaissavedinafile,a.RData,andthefileissavedina

human-readableformat:

>save(a,file="a.RData",ascii=TRUE)

>save.image("wholeworkspace.RData",ascii=TRUE)

>help(save)

Asanalternativetothehelpcommand,the?operatorcanalsobeusedtogetthehelppageforagivencommand.Anadditionalcommandisthehelp.searchcommandthatisusedtosearchthehelpfilesforagivenstring.The??operatorisalsoavailabletoperformasearchforagivenstring.

Theinformationinafilecanbereadbackintotheworkspaceusingtheloadcommand:

>load("a.RData")

[1]"a"

Anotherquestionthatariseswithrespecttoavariableishowitisstored.Thetwocommandstodeterminethisaremodeandstorage.mode.Youshouldtrytousethesecommandsforeachofthedatatypesdescribedinthefollowingsubsections.Basically,thesecommandscanmakeiteasiertodeterminewhetheravariableisanumericvalueoranotherbasicdatatype.

Thepreviouscommandsprovideoptionsforsavingthevaluesofthevariableswithinaworkspace.Theydonotsavethecommandsthatyouhaveentered.ThesecommandsarereferredtoasthehistorywithintheRworkspace,andyoucansaveyourhistoryusingthesavehistorycommand.Thehistorycanbedisplayedusingthehistorycommand,andtheloadhistorycommandcanbeusedtoreplaythecommandsinafile.

Thelastcommandgivenhereisthecommandtoquit,q().SomepeopleconsiderthistobethemostimportantcommandbecausewithoutityouwouldneverbeabletoleaveR.Therestofusarenotsurewhyitisnecessary.

DiscretedatatypesOneofthefeaturesoftheRenvironmentistherichcollectionofdatatypesthatareavailable.Here,webrieflylistsomeofthebuilt-indatatypesthatdescribediscretedata.Thefourdatatypesdiscussedaretheinteger,logical,character,andfactordatatypes.Wealsointroducetheideaofavector,whichisthedefaultdatastructureforanyvariable.AlistofthecommandsdiscussedhereisgiveninTable2andTable3.

ItshouldbenotedthatthedefaultdatatypeinR,foranumber,isadoubleprecisionnumber.Stringscanbeinterpretedinavarietyofways,usuallyaseitherastringorafactor.YoushouldbecarefultomakesurethatRisstoringinformationintheformatthatyouwant,anditisimportanttodouble-checkthisimportantaspectofhowdataistracked.

IntegerThefirstdiscretedatatypeexaminedistheintegertype.Valuesare32-bitintegers.Inmostcircumstances,anumbermustbeexplicitlycastasbeinganinteger,asthedefaulttypeinRisadoubleprecisionnumber.Thereareavarietyofcommandsusedtocastintegersaswellasallocatespaceforintegers.Theintegercommandtakesanumberforanargumentandwillreturnavectorofintegerswhoselengthisgivenbytheargument:

>bubba<-integer(12)

>bubba

[1]000000000000

>bubba[1]

>bubba[2]

>bubba[[4]]

>b[4]<-15

[1]0001500000000

Intheprecedingexample,avectoroftwelveintegerswasdefined.Thedefaultvaluesarezero,andtheindividualentriesinthevectorareaccessedusingbraces.Thefirstentryinthevectorhasindex1,sointhisexample,bubba[1]referstotheinitialentryinthevector.Notethattherearetwowaystoaccessanelementinthevector:singleversusdoublebraces.Foravector,thetwomethodsarenearlythesame,butwhenweexploretheuseoflistsasopposedtovectors,themeaningwillchange.Inshort,thedoublebracesreturnobjectsofthesametypeastheelementswithinthevector,andthesinglebracesreturnvaluesofthesametypeasthevariableitself.Forexample,usingsinglebracesonalistwillreturnalist,whiledoublebracesmayreturnavector.

Anumbercanbecastasanintegerusingtheas.integercommand.Avariable’stypecanbecheckedusingthetypeofcommand.ThetypeofcommandindicateshowRstorestheobjectandisdifferentfromtheclasscommand,whichisanattributethatyoucanchangeorquery:

>as.integer(13.2)

>thisNumber<-as.integer(8/3)

>typeof(thisNumber)

[1]"integer"

Notethatasequenceofnumberscanbeautomaticallycreatedusingeitherthe:operatorortheseqcommand:

[1]12345

>myNum<-as.integer(1:5)

>myNum[1]

>myNum[3]

>seq(4,11,by=2)

[1]46810

>otherNums<-seq(4,11,by=2)

>otherNums[3]

Acommontaskistodeterminewhetherornotavariableisofacertaintype.Forintegers,theis.integercommandisusedtodeterminewhetherornotavariablehasanintegertype:

>a<-1.2

>typeof(a)

[1]"double"

>is.integer(a)

[1]FALSE

>a<-as.integer(1.2)

>typeof(a)

[1]"integer"

>is.integer(a)

[1]TRUE

LogicalLogicaldataconsistsofvariablesthatareeithertrueorfalse.ThewordsTRUEandFALSEareusedtodesignatethetwopossiblevaluesofalogicalvariable.(TheTRUEvaluecanalsobeabbreviatedtoT,andtheFALSEvaluecanbeabbreviatedtoF.)Thebasiccommandsassociatedwithlogicalvariablesaresimilartothecommandsforintegersdiscussedintheprevioussubsection.ThelogicalcommandisusedtoallocateavectorofBooleanvalues.Inthefollowingexample,alogicalvectoroflength10iscreated.ThedefaultvalueisFALSE,andtheBooleannotoperatorisusedtoflipthevaluestoevaluatetoTRUE:

>b<-logical(10)

[1]FALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE

[1]FALSE

[1]TRUETRUETRUETRUETRUETRUETRUETRUETRUETRUE

>!b[5]

[1]TRUE

>typeof(b)

[1]"logical"

>mode(b)

[1]"logical"

>storage.mode(b)

[1]"logical"

>b[3]<-TRUE

[1]FALSEFALSETRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSE

Tocastavaluetoalogicaltype,youcanusetheas.logicalcommand.NotethatzeroismappedtoavalueofFALSEandothernumbersaremappedtoavalueofTRUE:

>a<--1:1

[1]-101

>as.logical(a)

[1]TRUEFALSETRUE

Todeterminewhetherornotavaluehasalogicaltype,youusetheis.logicalcommand:

>b<-logical(4)

[1]FALSEFALSEFALSEFALSE

>is.logical(b)

[1]TRUE

Thestandardoperatorsforlogicaloperationsareavailable,andalistofsomeofthemorecommonoperationsisgiveninTable1.Notethatthereisadifferencebetweenoperationssuchas&and&&.Asingle&isusedtoperformanandoperationoneachpairwiseelementoftwovectors,whilethedouble&&returnsasinglelogicalresultusingonlythefirstelementsofthevectors:

>l1<-c(TRUE,FALSE)

>l2<-c(TRUE,TRUE)

>l1&l1

[1]TRUEFALSE

>l1&&l1

[1]TRUE

>l1|l2

[1]TRUETRUE

>l1||l2

[1]TRUE

TipYoucandownloadtheexamplecodefilesforallPacktbooksyouhavepurchasedfromyouraccountathttp://www.packtpub.com.Anadditionalsourcefortheexamplesinthisbookcanbefoundathttps://github.com/KellyBlack/R-Object-Oriented-Programming.Ifyoupurchasedthisbookelsewhere,youcanvisithttp://www.packtpub.com/supportandregistertohavethefilese-maileddirectlytoyou.

Thefollowingtableshowsvariouslogicaloperatorsandtheirdescription:

LogicalOperator Description

< Lessthan

> Greaterthat

<= Lessthanorequalto

>= Greaterthanorequalto

== Equalto

!= Notequalto

| Entrywiseor

& Entrywiseand

&& And

xor(a,b) Exclusiveor

Table1–listofoperatorsforlogicalvariables

CharacterOnecommonwaytostoreinformationistosavedataascharactersorstrings.Characterdataisdefinedusingeithersingleordoublequotes:

>a<-"hello"

[1]"hello"

>b<-'there'

[1]"there"

>typeof(a)

[1]"character"

Thecharactercommandcanbeusedtoallocateavectorofcharacter-valuedstrings,asfollows:

>many<-character(3)

[1]""""""

>many[2]<-"thisisthesecond"

>many[3]<-'yo,third!'

>many[1]<-"andthefirst"

[1]"andthefirst""thisisthesecond""yo,third!"

Avaluecanbecastasacharacterusingtheas.charactercommand,asfollows:

>a<-3.0

>b<-as.character(a)

[1]"3"

Finally,theis.charactercommandtakesasingleargument,anditreturnsavalueofTRUEiftheargumentisastring:

>a<-as.character(4.5)

[1]"4.5"

>is.character(a)

[1]TRUE

FactorsAnothercommonwaytorecorddataistoprovideadiscretesetoflevels.Forexample,theresultsofanindividualtrialinanexperimentmaybedenotedbyavalueofa,b,orc.OrdinaldataofthiskindisreferredtoasafactorinR.Thecommandsandideasareroughlyparalleltothedatatypesdescribedpreviously.Therearesomesubtledifferenceswithfactors,though.Factorsareusedtodesignatedifferentlevelsandcanbeconsideredorderedorunordered.Therearealargenumberofoptions,anditiswisetoconsultthehelppagesforfactorsusingthe(help(factor))command.Onethingtonote,though,isthatthetypeofcommandforafactorwillreturnaninteger.

Factorscanbedefinedusingthefactorcommand,asfollows:

>lev<-factor(x=c("one","two","three","one"))

[1]onetwothreeone

Levels:onethreetwo

>levels(lev)

[1]"one""three""two"

>sort(lev)

[1]oneonetwothree

Levels:onetwothree

>lev<-

factor(x=c("one","two","three","one"),levels=c("one","two","three"))

[1]onetwothreeone

Levels:onetwothree

>levels(lev)

[1]"one""two""three"

>sort(lev)

[1]oneonetwothree

Levels:onetwothree

Thetechniquesusedtocastavariabletoafactorortestwhetheravariableisafactoraresimilartothepreviousexamples.Avariablecanbecastasafactorusingtheas.factorcommand.Also,theis.factorcommandcanbeusedtodeterminewhetherornotavariablehasatypeoffactor.

ContinuousdatatypesThedatatypesforcontinuousdatatypesaregivenhere.Thedoubleandcomplexdatatypesaregiven.AlistofthecommandsdiscussedhereisgiveninTable2andTable3.

DoubleThedefaultnumericdatatypeinRisadoubleprecisionnumber.Thecommandsaresimilartothoseoftheintegerdatatypediscussedpreviously.Thedoublecommandcanbeusedtoallocateavectorofdoubleprecisionnumbers,andthenumberswithinthevectorareaccessedusingbraces:

>d<-double(8)

[1]00000000

>typeof(d)

[1]"double"

>d[3]<-17

[1]001700000

Thetechniquesusedtocastavariabletoadoubleprecisionnumberandtestwhetheravariableisadoubleprecisionnumberaresimilartotheexamplesseenpreviously.Avariablecanbecastasadoubleprecisionnumberusingtheas.doublecommand.Also,todeterminewhetheravariableisadoubleprecisionnumber,theas.doublecommandcanbeused.

ComplexArithmeticforcomplexnumbersissupportedinR,andmostmathfunctionswillreactproperlywhengivenacomplexnumber.Youcanappenditotheendofanumbertoforceittobetheimaginarypartofacomplexnumber,asfollows:

[1]0+1i

>1i*1i

[1]-1+0i

>z<-3+2i

[1]3+2i

[1]5+12i

>Mod(z)

[1]3.605551

>Re(z)

>Im(z)

>Arg(z)

[1]0.5880026

>Conj(z)

[1]3-2i

Thecomplexcommandcanalsobeusedtodefineavectorofcomplexnumbers.Thereare

anumberofoptionsforthecomplexcommand,soaquickcheckofthehelppage,(help(complex)),isrecommended:

>z<-complex(3)

[1]0+0i0+0i0+0i

>typeof(z)

[1]"complex"

>z<-complex(real=c(1,2),imag=c(3,4))

[1]1+3i2+4i

>Re(z)

Thetechniquestocastavariabletoacomplexnumberandtotestwhetherornotavariableisacomplexnumberaresimilartothemethodsseenpreviously.Avariablecanbecastascomplexusingtheas.complexcommand.Also,totestwhetherornotavariableisacomplexnumber,theas.complexcommandcanbeused.

SpecialdatatypesTherearetwoothercommondatatypesthatoccurthatareimportant.Wewilldiscussthesetwodatatypesandprovideanoteaboutobjects.ThetwodatatypesareNAandNULL.Thesearebriefcomments,asthesearerecurringtopicsthatwewillrevisitmanytimes.

Thefirstdatatypeisaconstant,NA.Thisisatypeusedtoindicateamissingvalue.ItisaconstantinR,andavariablecanbetestedusingtheis.nacommand,asfollows:

>n<-c(NA,2,3,NA,5)

[1]NA23NA5

>is.na(n)

[1]TRUEFALSEFALSETRUEFALSE

>n[!is.na(n)]

[1]235

AnotherspecialtypeistheNULLtype.IthasthesamemeaningasthenullkeywordintheClanguage.Itisnotanactualtypebutisusedtodeterminewhetherornotanobjectexists:

>a<-NULL

>typeof(a)

[1]"NULL"

Finally,we’llquicklyexplorethetermobjects.ThevariablesthatwedefinedinalloftheprecedingexamplesaretreatedasobjectswithintheRenvironment.Whenwestartwritingfunctionsandcreatingclasses,itwillbeimportanttorealizethattheyaretreatedlikevariables.ThenamesusedtoassignvariablesarejustashortcutforRtodeterminewhereanobjectislocated.

Forexample,thecomplexcommandisusedtoallocateavectorofcomplexvalues.Thecommandisdefinedtobeasetofinstructions,andthereisanobjectcalledcomplexthatpointstothoseinstructions:

>complex

function(length.out=0L,real=numeric(),imaginary=numeric(),

modulus=1,argument=0)

if(missing(modulus)&&missing(argument)){

.Internal(complex(length.out,real,imaginary))

n<-max(length.out,length(argument),length(modulus))

rep_len(modulus,n)*exp((0+1i)*rep_len(argument,

<bytecode:0x2489c80>

<environment:namespace:base>

Thereisadifferencebetweencallingthecomplex()functionandreferringtothesetofinstructionslocatedatcomplex.

NotesontheasandisfunctionsTwocommontasksaretodeterminewhetheravariableisofagiventypeandtocastavariabletodifferenttypes.Thecommandstodeterminewhetheravariableisofagiventypegenerallystartwiththeisprefix,andthecommandstocastavariabletoadifferenttypegenerallystartwiththeasprefix.Thelistofcommandstodeterminewhetheravariableisofagiventypearegiveninthefollowingtable:

Typetocheck Command

Integer is.integer

Logical is.logical

Character is.character

Factor is.factor

Double is.double

Complex is.complex

NA is.NA

List is.list

Table2–commandstodeterminewhetheravariableisofaparticulartype

ThecommandsusedtocastavariabletoadifferenttypearegiveninTable3.Thesecommandstakeasingleargumentandreturnavariableofthegiventype.Forexample,theas.charactercommandcanbeusedtoconvertanumbertoastring.

Thecommandsintheprevioustableareusedtotestwhattypeavariablehas.Thefollowingtableprovidesthecommandsthatareusedtochangeavariableofonetypetoanothertype:

Typetoconvertto Command

Integer as.integer

Logical as.logical

Character as.character

Factor as.factor

Double as.double

Complex as.complex

NA as.NA

List as.list

Table3–commandstocastavariableintoaparticulartype

SummaryInthischapter,weexaminedsomeofthedatatypesavailableintheRenvironment.Theseincludediscretedatatypessuchasintegersandfactors.Italsoincludescontinuousdatatypessuchasrealandcomplexdatatypes.Wealsoexaminedwaystotestavariabletodeterminewhattypeitis.

Inthenextchapter,welookatthedatastructuresthatcanbeusedtokeeptrackofdata.Thisincludesvectorsanddatatypessuchaslistsanddataframesthatcanbeconstructedfromvectors.

Chapter2.OrganizingDataInthischapter,wewillexploretheprimarydatastructuresthatareusedtoorganizedata.Someofthedetailsaboutaccessinginformationwithindatastructureswillbediscussed,andsomeofthewaystoapplydifferentoperationstopartsofthedatawithinadatastructurewillbediscussedtoo.Thereareroughlythreepartstothischapter:

Basicdatastructures:Thissectiongivesyouinformationonusingvectors,lists,dataframes,tables,matrices,andtimeseriesAccessingandmanagingmemory:ThissectiongivesyouanoverviewofthebasicwaystogainaccessandcensorspecificelementsOperationsondatastructures:Thissectiongivesyouanoverviewoftheoperationsandmethodsusedtoapplyoperationswithinthedifferentkindsofdatastructures

BasicdatastructuresThebasicdatastructuresusedtoorganizedatawithintheRenvironmentincludevectors,lists,dataframes,tables,andmatrices.Here,weprovidedetailsforeachofthesedatastructuresanddemonstratehowtocreatethem.Thischapterdoesnotincludeinformationabouthowtoreaddatafromafile,andthefocusisonthedatastructuresthemselves.MoreinformationaboutreadingfromafilecanbefoundinChapter3,SavingDataandPrintingResults.

VectorsThedefaultdatastructureinRisthevector.Forexample,ifyoudefineavariableasasinglenumber,Rwilltreatitasavectoroflengthone:

Vectorsrepresentaconvenientandstraightforwardwaytostorealonglistofnumbers.PleaseseeChapter1,DataTypes,toseemoreexamplesofcreatingvectors.Oneusefulandcommonwaytodefineavectoristousetheccommand.Theccommandconcatenatesasetofargumentstoformasinglevector:

>v<-c(1,3,5,7,-10)

[1]1357-10

>v[2]<-v[1]-v[5]

[1]11157-10

Twoothermethodstogeneratevectorsmakeuseofthe:notationandtheseqcommand.The:notationisusedtocreatealistofsequentiallynumberedvaluesforgivenstartandendvalues.Theseqcommanddoesthesamething,butitprovidesmoreoptionstodeterminetheincrementbetweenvaluesinthevector:

[1]12345

>10:14

[1]1011121314

>a<-3:7

[1]34567

>b<-seq(3,5)

[1]345

>b<-seq(3,10,by=3)

[1]369

ListsAnotherimportanttypeisthelist.Listsareflexibleandareanunstructuredwayoforganizinginformation.Alistcanbethoughtofasacollectionofnamedobjects.Alistiscreatedusingthelistcommand,andavariablecanbetestedorcoercedusingtheis.listandas.listcommands.Acomponentwithinalistisaccessedusingthe$charactertodenotewhichobjectwithinthelisttouse.Asanexample,supposethatwewanttocreatealisttokeeptrackoftheparametersforanexperiment.Thefirstcomponent,calledmeans,willbeavectoroftheassumedmeans.Thesecondcomponentwillbetheconfidencelevel,andthethirdcomponentwillbethevalueofaparameterfortheexperimentcalledmaxRealEigen:

>assumedMeans<-c(1.0,1.5,2.1)

>alpha<-0.05

>eigenValue<-3+2i

>l<-list(means=assumedMeans,alpha=alpha,maxRealEigen=eigenValue)

$means

[1]1.01.52.1

$alpha

[1]0.05

$maxRealEigen

[1]3+2i

>l$means

[1]1.01.52.1

>l$means[2]

[1]1.5

Thenamesandattributescommandscanbeusedtodeterminethecomponentswithinalist.Theattributescommandisamoregenericcommandthatcanbeusedtolistthecomponentsofclassesandawiderrangeofobjects.Notethatthenamescommandcanalsobeusedtorenamethecomponentsofalist.Inthefollowingexample,weusethepreviousexamplebutchangethenamesoftheelements:

>l<-list(means=c(1.0,1.5,2.1),alpha=0.05,maxRealEigen=3+2i)

>names(l)

[1]"means""alpha""maxRealEigen"

>names(l)<-c("assumedMeans","confidenceLevels","maximumRealValue")

$assumedMeans

[1]1.01.52.1

$confidenceLevels

[1]0.05

$maximumRealValue

[1]3+2i

DataframesAdataframeissimilartoalist,andmanyoftheoperationsaresimilar.Theprimarydifferenceisthatallofthecomponentsofadataframemusthavethesamenumberofelements.Thisisoneofthemostcommonwaystostoreinformation,andmanyofthefunctionsavailabletoreaddatafromafilereturnadataframebydefault.Forexample,supposeweaskfivepeopletwoquestions.Thefirstquestionis,“Doyouhaveapetcat?”Thesecondquestionis,“Howmanyroomsinyourhouseneednewcarpet?”:

>d<-data.frame(Q1=as.factor(c("y","n","y","y","n")),

+Q2=c(2,0,1,2,0))

[1]ynyyn

Levels:ny

>summary(d)

n:2Min.:0

y:31stQu.:0

Median:1

Mean:1

3rdQu.:2

Max.:2

Thenamesandattributescommandshavethesamebehaviorswithdataframesaslists.Intheprecedingexample,wetakethedataframedefinedinthepreviousexampleandrenamethefieldstosomethingmoredescriptive:

+Q2=c(2,0,1,2,0))

>names(d)<-c("HaveCat","NumberRooms")

HaveCatNumberRooms

TablesTablescanbeeasilyconstructedandRwillautomaticallygeneratefrequencytablesfromcategoricaldata.Thetablecommandhasanumberofoptions,butwefocusonbasicexampleshere.Moredetailscanbefoundusingthehelp(table)command.Inthenextexample,wetakethedatafromtheprecedingcatquestionsandcreateatablefromtheanswersfromthefirstquestion:

+Q2=c(2,0,1,2,0))

>q1Results<-table(d$Q1)

>q1Results

>summary(q1Results)

Numberofcasesintable:5

Numberoffactors:1

Ifyouwishtocreateatwowaytable,thensimplyprovidetwovectorstothetablecommandtogetthecrosstabulation.Again,welookatthedatafromthecatquestions.Notethatwehavetoconvertthesecondquestiontoafactor:

+Q2=c(2,0,1,2,0))

>results<-table(d$Q1,as.factor(d$Q2))

>results

>summary(results)

Numberofcasesintable:5

Numberoffactors:2

Testforindependenceofallfactors:

Chisq=5,df=2,p-value=0.08208

Chi-squaredapproximationmaybeincorrect

Therowsandcolumnsofthetablehavenamesassociatedwiththem,andtherownamesandcolnamescommandscanbeusedtoassignthenames.Thesecommandsaresimilartothenamescommand.Intheprecedingexample,thenamesinthetablearenotdescriptive.Inthefollowingexample,webuildthetableandrenametherowsandcolumns:

+Q2=c(2,0,1,2,0))

>results<-table(d$Q1,as.factor(d$Q2))

>rownames(results)<-c("NoCat","HasCat")

>colnames(results)<-c("Noroom","Oneroom","Tworooms")

>results

NoroomOneroomTworooms

NoCat200

HasCat012

Onelastnote;theargumenttothetablecommandrequiresordinaldata.Ifyouhavenumericdata,itcanbequicklytransformedtoencodewhichintervalcontainseach

number.Thecutcommandtakesthenumericdataandavectorofbreakpointsthatindicatethecutoffpointsbetweeneachinterval,asfollows:

>a<-c(-0.8,-0.7,0.9,-1.4,-0.3,1.2)

>b<-cut(a,breaks=c(-1.5,-1,-0.5,0,0.5,1.0,1.5))

[1](-1,-0.5](-1,-0.5](0.5,1](-1.5,-1](-0.5,0](1,1.5]

Levels:(-1.5,-1](-1,-0.5](-0.5,0](0,0.5](0.5,1](1,1.5]

>summary(b)

(-1.5,-1](-1,-0.5](-0.5,0](0,0.5](0.5,1](1,1.5]

121011

>table(b)

(-1.5,-1](-1,-0.5](-0.5,0](0,0.5](0.5,1](1,1.5]

121011

MatricesandarraysTablesareaspecialcaseofanarray.Anarrayoramatrixcanbeconstructeddirectlyusingeitherthearrayormatrixcommands.Thearraycommandtakesavectoranddimensions,anditconstructsanarrayusingcolumnmajororder.Ifyouwishtoprovidethedatainrowmajororder,thenthecommandtotransposetheresultist:

>a<-c(1,2,3,4,5,6)

>A<-array(a,c(2,3))

[,1][,2][,3]

[1,]135

[2,]246

[,1][,2]

[1,]12

[2,]34

[3,]56

Youarenotlimitedtotwo-dimensionalarrays.Thedimoptioncanincludeanynumberofdimensions.Inthefollowingexample,athree-dimensionalarrayiscreatedbyusingthreenumbersforthenumberofdimensions:

>A<-array(1:24,c(2,3,4),dimnames=c("row","col","dep"))

[,1][,2][,3]

[1,]135

[2,]246

[,1][,2][,3]

[1,]7911

[2,]81012

[,1][,2][,3]

[1,]131517

[2,]141618

[,1][,2][,3]

[1,]192123

[2,]202224

>A[2,3,4]

Amatrixisatwo-dimensionalarrayandisaspecialcasethatcanbecreatedusingthematrixcommand.Ratherthanusingthedimensions,thematrixcommandrequiresthatyouspecifythenumberofrowsorcolumns.Thecommandhasanadditionaloptionto

specifywhetherornotthedataisinrowmajororcolumnmajororder:

>B<-matrix(1:12,nrow=3)

[,1][,2][,3][,4]

[1,]14710

[2,]25811

[3,]36912

>B<-matrix(1:12,nrow=3,byrow=TRUE)

[,1][,2][,3][,4]

[1,]1234

[2,]5678

[3,]9101112

Bothmatricesandarrayscanbemanipulatedtodetermineorchangetheirdimensions.Thedimcommandcanbeusedtogetorsetthisinformation:

>C<-matrix(1:12,ncol=3)

[,1][,2][,3]

[1,]159

[2,]2610

[3,]3711

[4,]4812

>dim(C)

>dim(C)<-c(3,4)

[,1][,2][,3][,4]

[1,]14710

[2,]25811

[3,]36912

CensoringdataUsingalogicalvectorasanindexisusefulforlimitingdatathatisexamined.Forexample,tolimitavectortoexamineonlythepositivevaluesinthedataset,alogicalcomparisoncanbeusedfortheindexintothevector:

>u<-1:6

>v<-c(-1,1,-2,2,-3,3)

[1]123456

[1]-11-22-33

>u[v>0]

[1]246

>u[v<0]=-2*u[v<0]

[1]-22-64-106

AnotherusefulaspectofalogicalindexintoavectoristheuseoftheNAdatatype.Theis.nafunctionandalogicalNOToperator(!)canbeausefultoolwhenavectorcontainsdatathatisnotdefined:

>v<-c(1,2,3,NA,4,NA)

[1]123NA4NA

>v[is.na(v)]

[1]NANA

>v[!is.na(v)]

[1]1234

NotethatmanyfunctionshaveoptionalargumentstospecifyhowRshouldreacttodatathatcontainsavaluewiththeNAtype.Unfortunately,thewaythisisdoneisnotconsistent,andyoushouldusethehelpcommandwithrespecttoanyparticularfunction:

>v<-c(1,2,3,NA,4,NA)

[1]123NA4NA

>mean(v)

>mean(v,na.rm=TRUE)

[1]2.5

Inthislastexample,thena.rmoptioninthemeanfunctionissettoTRUEtospecifythatRshouldignoretheentriesinthevectorthatareNA.

AppendingrowsandcolumnsThecbindandrbindcommandscanbeusedtoappenddatatoexistingobjects.Thesecommandscanbeusedonvectors,matrices,arrays,andtheyareextendedtoalsoactondataframes.Thefollowingexamplesusedataframes,asthatisacommonoperation.Youshouldbecarefulandtrythecommandsonarraystomakesurethattheoperationbehavesinthewayyouexpect.

Thecbindcommandisusedtocombinethecolumnsofthedatagivenasarguments:

>d<-data.frame(one=c(1,2,3),two=as.factor(c("one","two","three")))

>e<-c("ein","zwei","drei")

>newDataFrame<-cbind(d,third=e)

>newDataFrame

onetwothird

11oneein

22twozwei

33threedrei

>newDataFrame$third

[1]einzweidrei

Levels:dreieinzwei

Iftheargumentstothecbindcommandaretwodataframes(ortwoarrays),thenthecommandcombinesallofthecolumnsfromallofthedataframes(arrays):

>e<-data.frame(three=c(4,5,6),four=as.factor(c("vier","funf","sechs")))

>newDataFrame<-cbind(d,e)

>newDataFrame

onetwothreefour

11one4vier

22two5funf

33three6sechs

Therbindcommandconcatenatestherowsoftheobjectspassedtoit.Thecommandusesthenamesofthecolumnstodeterminehowtoappendthedata.Thenumberandnamesofthecolumnsmustbeidentical:

>e<-data.frame(one=c(4,5,6),two=as.factor(c("vier","funf","sechs")))

>newDataFrame<-rbind(d,e)

>newDataFrame

onetwo

33three

44vier

55funf

66sechs

OperationsondatastructuresTheRenvironmenthasarichsetofoptionsavailableforperformingoperationsondatawithinthevariousdatastructures.Theseoperationscanbeperformedinavarietyofwaysandcanberestrictedaccordingtovariouscriteria.Thefocusofthissectionisthepurposeandformatsofthevariousapplycommands.

TheapplycommandsareusedtoinstructRtouseagivencommandonspecificpartsofalist,vector,orarray.Eachdatatypehasdifferentversionsoftheapplycommandsthatareavailable.Beforediscussingthedifferentcommands,itisimportanttodefinethenotionofthemarginsofatableorarray.Themarginsaredefinedalonganydimension,andthedimensionusedmustbespecified.Themargincommandcanbeusedtodeterminethesumoftherow,columns,ortheentirecolumnofanarrayortable:

>A<-matrix(1:12,nrow=3,byrow=TRUE)

[,1][,2][,3][,4]

[1,]1234

[2,]5678

[3,]9101112

>margin.table(A)

>margin.table(A,1)

[1]102642

>margin.table(A,2)

[1]15182124

Thelasttwocommandsspecifytheoptionalmarginargument.Themargin.table(A,1)commandspecifiesthatthesumsareinthefirstdimension,thatis,therows.Themargin.table(A,2)commandspecifiesthatthesumsareintheseconddimension,thatis,thecolumns.Theideaofspecifyingwhichdimensiontouseinacommandcanbeimportantwhenusingtheapplycommands.

TheapplycommandsThevariousapplycommandsareusedtooperateonthedifferentdatastructures.Eachone—apply,lapply,sapply,tapply,andmapply—willbebrieflydiscussedinorderinthefollowingsections.

applyTheapplycommandisusedtoapplyagivenfunctionacrossagivenmarginofanarrayortable.Forexample,totakethesumofaroworcolumnfromatwowaytable,usetheapplycommandwithargumentsforthetable,thesumcommand,andwhichdimensiontouse:

>A<-matrix(1:12,nrow=3,byrow=TRUE)

[,1][,2][,3][,4]

[1,]1234

[2,]5678

[3,]9101112

>apply(A,1,sum)

[1]102642

>apply(A,2,sum)

[1]15182124

YoushouldbeabletoverifytheseresultsusingtherowSumsandcolSumscommandsaswellasthemargin.tablecommanddiscussedpreviously.

lapplyandsapplyThelapplycommandisusedtoapplyafunctiontoeachelementinalist.Theresultisalist,whereeachcomponentofthereturnedobjectisthefunctionappliedtotheobjectintheoriginallistwiththesamename:

>theList<-list(one=c(1,2,3),two=c(TRUE,FALSE,TRUE,TRUE))

>sumResult<-lapply(theList,sum)

>sumResult

>typeof(sumResult)

[1]"list"

>sumResult$one

Thesapplycommandissimilartothelapplycommand,anditperformsthesameoperation.Thedifferenceisthattheresultiscoercedtobeavectorifpossible:

>theList<-list(one=c(1,2,3),two=c(TRUE,FALSE,TRUE,TRUE))

>meanResult<-sapply(theList,mean)

>meanResult

onetwo

2.000.75

>typeof(meanResult)

[1]"double"

tapplyThetapplycommandisusedtoapplyafunctiontodifferentpartsofdatawithinanarray.Thefunctiontakesatleastthreearguments.Thefirstisthedatatoapplyanoperation,thesecondisthesetoffactorsthatdefineshowthedataisorganizedwithrespecttothedifferentlevels,andthethirdistheoperationtoperform.Inthefollowingexample,avectorisdefinedthathasthediameteroftrees.Asecondvectorisdefined,whichspecifieswhatkindoftreewasmeasuredforeachobservation.Thegoalistofindthestandarddeviationforeachtypeoftree:

>diameters<-c(28.8,27.3,45.8,34.8,25.3)

>tree<-as.factor(c("pine","pine","oak","pine","oak"))

>tapply(diameters,tree,sd)

oakpine

14.4956893.968627

mapplyThelastcommandtoexamineisthemapplycommand.Themapplycommandtakesafunctiontoapplyandalistofarrays.Thefunctiontakesthefirstelementsofeacharrayandappliesthefunctiontothatlist.Itthentakesthesecondelementsofeacharrayandappliesthefunction.Thisisrepeateduntilitgoesthrougheveryelement.Notethatifoneofthearrayshasfewerelementsthantheothers,themapplycommandwillresetandstartatthebeginningofthatarraytofillinthemissingvalues:

>a<-c(1,2,3)

>b<-c(1,2,3)

>mapply(sum,a,b)

[1]246

SummaryInthischapter,weexaminedthebasicdatastructuresavailabletohelporganizedata.Thesedatastructuresincludevectors,lists,dataframes,tables,andarrays.Weexaminedsomeofthewaystomanagethedatastructuresusingtherbindandcbindcommands.Finally,weexaminedsomeofthemethodsavailabletoperformcalculationsonthedatawithinthedatastructureandexaminedthevariousfunctionsavailabletoapplycommandstopartsofthedatawithinthedatastructure.

Inthenextchapter,wewillbuildontheseideasandexaminehowtogetinformationfromadatafileandintothevariousdatastructures.Wewillalsoexaminethemethodsavailabletoproduceformattedoutputtodisplaytheresultsofcalculationsondata.

Chapter3.SavingDataandPrintingResultsThischapterprovidesyouwithabroadoverviewofthewaystogetinformationintoaswellasoutoftheRenvironment.Therearevariouspackagesthatareavailableandrelatedtothisimportantfunction,butwewillfocusonasubsetofthebasic,built-infunctions.Thechapterisdividedintothefollowingfivesections:

Fileanddirectoryinformation:ThissectiongivesyouabriefoverviewofhowfilesanddirectoriesareorganizedintheRenvironmentInput:ThissectiongivesyouanoverviewofthemethodsthatcanbeusedtobringdataintotheRenvironmentOutput:ThissectiongivesyouanoverviewofthemethodsavailabletogetdataoutoftheRenvironmentPrimitiveinput/output:ThissectiongivesyouanoverviewofthemethodsyoucanusetowritedatainbinaryorcharacterformsinpredefinedformatsNetworkoptions:Thissectiongivesyouabriefoverviewofthemethodsassociatedwithcreatingandmanipulatingsockets

FileanddirectoryinformationBeforediscussinghowtosaveorreaddata,wefirstneedtoexamineR’sfacilitiesforgettinginformationaboutfilesanddirectories.Wewillfirstdiscussthecommandsusedtoworkwithdirectoriesandfiles,andthendiscussthecommandsusedtomanipulatethecurrentworkingdirectory.Thebasiccommandstolistdirectoryandfileinformationarethedir,list.dirs,andlist.filescommands.Thebasiccommandstolistandchangethecurrentworkingdirectoryaregetwdandsetwd.

Thedir,list.dirs,andlist.filescommandsareusedtogetinformationaboutdirectoriesandfileswithindirectories.Bydefault,thecommandswillgetinformationaboutthedirectoriesinthecurrentworkingdirectory:

>dir()

[1]"R""bin""csv"

>d<-dir()

[1]"R"

Theprecedingcommandsalsoacceptawidevarietyofoptions.Useofthehelpcommandisrecommendedtoseemoredetails:

>list.files('./csv')

[1]"network.csv""trees.csv"

>f<-list.files('./csv')

[1]"trees.csv"

Thesecommandshaveanoptionalparameterforspecifyingapattern,andthepatternisaregularexpression.Thetopicofregularexpressionsisbeyondthescopeofthisdiscussion,butitoffersaverypowerfuloptionforspecifyingafiltertodeterminethenamesoffiles.Forexample,allofthefilesthatbeginwiththeletterncanbeeasilydetermined:

>f<-list.files('./csv',pattern='^n')

[1]"network.csv"

Anotherimportanttopicistheideaofthecurrentworkingdirectory.WhentheRenvironmentseeksafileordirectorywhosenameisgiveninarelativeform,itstartsfromthecurrentworkingdirectory.Thereareseveralotherwaystospecifythecurrentdirectory,anditispartofthemajorityofgraphicalinterfaces.Unfortunately,itvariesacrossthedifferentinterfaces.

Thecommandstomanipulatethecurrentworkingdirectoryviathecommandlinearethegetwdandsetwdcommands.Thenamesofdirectories(folders)areseparatedusingforwardslashes:

>getwd()

[1]"/tmp/examples"

>d<-getwd()

[1]"/tmp/examples"

EnteringdataHavingdiscussedtheideasassociatedwithdirectoriesandfiles,wecannowdiscusshowtoreaddata.Here,wewillprovideanoverviewofthedifferentwaystogetinformationfromafile.Wewillbeginwithashortoverviewaboutenteringdatafromthecommandlinefollowedbyexamplesforreadingatextfileintheformofatable,fromacsvfile,andfixedwidthfiles.Finally,wewilldiscussmoreprimitivemethodstoreadfromabinaryfile.

ItisimportanttonotethatwerelyonthetopicsdiscussedinChapter1,DataTypesandChapter2,OrganizingData.IassumethatyouarefamiliarwiththevariousdatatypesgiveninChapter1,DataTypes,aswellasthedatastructuresdiscussedinChapter2,OrganizingData.Inthischapter,wewillexploreasmallnumberofwaystoreaddataintoR.TherearealargenumberoflibrariesavailabletoreaddatainawidevarietyofformatssuchasJSON,XML,SAS,Excel,andotherfileformats.TherearealsomoreoptionsavailableinthebaseRdistribution.Toseemoreoptions,typereadandpresstheTABkey(nospaceafterthelettersread)toseeapartiallistofotheroptions.

EnteringdatafromthecommandlineWewillexaminetwowaystoreaddataincludingreadingkeyboardinputfromthecommandlineandreadingdatafromafile.Wefirstexaminesometechniquesusedtoobtaininformationthroughthecommandline.MoredetailscanbefoundinChapter2,OrganizingData,andwewillexploreadditionalwaystoenterdataincludingtheuseofthescananddata.entrycommands.

Inadditiontoconcatenatinginformationwiththeccommand,thereareadditionalcommandstomakeiteasiertodefinedata.Thefirstcommandwewillexamineisthescancommand.Ifyousimplyassignavariableusingthescancommandwithnoarguments,thenyouarepromptedandrequiredtoenterthenumbersfromthecommandline.Ifyouenterablankline(justhittheEnterkey),thenthepreviousvaluesarereturnedasavector:

>x<-scan()

1:28.8

2:27.3

3:45.8

4:34.8

5:23.5

Read5items

[1]28.827.345.834.823.5

Inthisexample,thescancommandisusedtopromptustoenterasetofnumbers.Afterablankentryisgiven,thecommandreturnsavectorwiththepreviousvalues.

Ifyouprovideafilename,thenthescancommandwillreadthevaluesfromthefileasifyouhadtypedthemonthecommandline.Supposethatwehaveafilecalleddiameters.csvwiththefollowingcontents:

Youcanreadthecontentsusingthescancommandasfollows:

>x<-scan("diameters.csv")

Read5items

[1]28.827.345.834.825.3

Youcanreadmorecomplexdatafromafileusingthescancommand,butyoumustspecifythestructureofthefile.Thismeansthatyouhavetospecifythedatatypes.Here,assumethatwehaveadatafilecalledtrees.csv:

pine,28.8

pine,27.3

oak,45.8

pine,34.8

oak,25.3

Thefirstcolumnisthecharacterdata,andthesecondcolumnisthenumericdata.Theinformationoneachlineisseparatedbyacomma.Thescancommandassumesthattheinformationisseparatedusingwhitespace(spacesandtabs),sointhiscase,wehavetospecifythatacommaisusedastheseparatorwithinthefile.Inthefollowingexample,thefileisread,andtheformatisgivenusingthewhatargument:

>x<-scan("trees.csv",what=list("character","double"),sep=",")

Read5records

[1]"pine""pine""oak""pine""oak"

[1]"28.8""27.3""45.8""34.8""25.3"

Anothermethodforenteringdataistousethedata.entrycommand.Thecommandwillopenupagraphicalinterfaceifitisavailableonyoursystem.Thedetailscanvarydependingonyouroperatingsystemandthegraphicalinterfacethatyouareusing.

ReadingtablesfromfilesOnecommonmethodusedtoreaddatafromafileistoreaditasatable.Thisassumesthatthefileisnicelyformattedandarrangedinconvenientrowsandcolumns.Acommandtoreaddatainthisformistheread.tablecommand.Therearealargenumberofoptionsforthiscommand,anditishighlyrecommendedthatyouusethehelpcommand,help(read.table),toseemorecompletedetailsaboutthiscommand.

Thefirstexampledemonstrateshowtoreadasimplefile.ItisassumedthatyouhaveafilecalledtrialTable.datinthefollowingformat:

Thefilehasnoheader,andthevaluesareseparatedbyspaces(whitespace).Inthissimpleformat,thefilecanbereadwiththedefaultoptions:

>trial<-read.table("trialTable.dat")

>trial

V1V2V3

>typeof(trial)

[1]"list"

>names(trial)

[1]"V1""V2""V3"

Theresultisalistofvalues.Nonameswerespecifiedforthenamesofthecolumns,andthedefaultvaluesforthecolumnnameshavebeenused.

CSVfilesTheread.tablecommandoffersageneralwaytoreadthedatafromafilewithaknownstructure.Onecommonfilestructureisafilewherethevaluesareseparatedbycommas,oracsvfile.Thecommandtoreadacsvfileistheread.csvcommand.Thereisanalternateversionofthecommand,read.csv2,whichhasadifferentsetofdefaults.Thedifferenceisthatthedefaultsforread.csv2aredefinedtoallowasimplewayofreadingafileinwhichthedelimiterbetweenthedecimalvaluesisacommaandthevaluesareseparatedbysemicolons,whicharemorecommonlyusedinsomeEuropeancountries.

Theread.csvcommandissimilartotheread.tablecommand.Theprimarydifferenceisthattheresultisreturnedasadataframe,andagreaterrangeofdatatypesforthecolumnsarerecognized.

Intheprecedingexamples,thetrialTable.csvfileisreadintotheworkspace.Thesamefilecanbereadusingtheread.csvcommand.ThetrialTable.csvfiledoesnothaveaheader,andthenumbersareseparatedusingspaces:

>trial<-read.csv("trialTable.csv",header=FALSE,sep='')

>trial

V1V2V3

>typeof(trial)

[1]"list"

Inthenextexample,wehaveadatafileinwhicheachlinehasthesamenumberofcolumns,andthedatafieldsareseparatedbycommas.Thefilewasdownloadedfromhttp://www.bea.gov/.Thefirstsixlinesofthefileareusedtoidentifyinformationaboutthedatainahuman-readableform,butthatinformationshouldbeignoredbytheread.csvfunction.Theotherthingtonoteisthattheseventhlineisaheader;ithasinformationthatdefinesthelabelusedtorefertothecolumns.Thelastthingtonoteisthatthenumbersareseparatedbycommas.Allofthesedetailsmustbespecifiedifwewanttoreadthefileusingtheread.csvcommand.Thissecondfile,inventories.csv,canbereadusingtheread.csvcommand,asfollows:

inventories<-read.csv("inventories.csv",+skip=6,header=TRUE,sep=",")

>typeof(inventories)

[1]"list"

>names(inventories)

[1]"Line""X""X1994.1""X1994.2""X1994.3""X1994.4""X1995.1"

[8]"X1995.2""X1995.3""X1995.4""X1996.1""X1996.2""X1996.3""X1996.4"

Fixed-widthfilesAnothercommonfileformatisafixedwidthfile.Inafixedwidthfile,everylinehasthesameformatandtheinformationwithinagivenlineisstrictlyorganizedbycolumns.Afileinthisformatcanbereadusingtheread.fwfcommand.Tousetheread.fwfcommand,youmustspecifythenameofthefileandthewidthofeachcolumn.YoucaninstructRtoignoreacolumnbyprovidinganegativevalueforthewidthofthecolumn.

Inthisexample,weassumethatafilewiththenametrialFWF.datisinthecurrentworkingdirectory.Thecontentsofthefileareasfollows:

12312345121234

B100ZZ18

C200YY20

D300XX22

Thefirstthreecolumnsareassumedtocontainletters,thenextfivecolumnscontainnumbers,thenexttwocolumnshaveletters,andthelastfourcolumnsarenumbers.Intheexamplefile,thetoprowshouldbeignoredasitisusedtodemonstratehowthefileisorganized.Theskipoptionisusedtoindicatehowmanylinestoignoreatthetopofthefile:

>trial<-read.fwf('trialFWF.dat',c(3,5,2,4),skip=1)

>trial

V1V2V3V4

1B100ZZ18

2C200YY20

3D300XX22

>trial$V1

[1]BCD

Levels:BCD

Notethatwhenawidthisgivenasanegativenumber,thatcolumnisignored:

>trial<-read.fwf('trialFWF.dat',c(3,-5,2,4),skip=1)

>trial

V1V2V3

1BZZ18

2CYY20

3DXX22

PrintingresultsandsavingdataWewillexploretheoptionsavailabletotakeinformationstoredwithintheRenvironmentandexpressthatinformationineitherhuman-ormachine-readableforms.WewillstartwithabriefdiscussiononsavingtheworkspaceinanRenvironment.Next,wewilldiscussvariouscommandsthatcanbeusedtoprintinformationtoeitherthescreenorafile.Finally,wewilldiscusstheprimitivecommandsthatcanbeusedforbasicfileoperations.

SavingaworkspaceTherearetwocommandsusedtosavetheinformationinthecurrentworkspace.Thefirstisthesavecommand,whichallowsyoutosaveparticularvariables.Thesecondisthesave.imagecommand,whichallowsyoutosaveallthevariableswithintheworkspace.

Thesavecommandrequiresalistofvariablestosave,andthenameofafiletosavethevariables.Thereareawidevarietyofoptions,butinthemostbasicformyousimplysavespecificvariablesfromthecurrentworkspace.Here,weusethelscommandtofirstlistthevariablesinthecurrentworkspaceandthenusethesavecommandtosavetwovariables,inventoriesandtrees:

>dir()

[1]"diameters.csv""inventories.csv""network.csv""trees.csv"

[5]"trialFWF.dat""trialTable.csv"

[1]"a""d""f""inventories""trees"

[6]"trial""x""y"

>save(inventories,trees,file="theInventories.RData")

>dir()

[1]"diameters.csv""inventories.csv""network.csv"

[4]"theInventories.RData""trees.csv""trialFWF.dat"

[7]"trialTable.csv"

Thesave.imagecommandrequiresonlyoneargument;thenameofthefileusedtosavetheinformation:

>dir()

[7]"trialTable.csv"

>save.image("wholeShebang.RData")

>dir()

[7]"trialTable.csv""wholeShebang.RData"

IfyoustartanewRsession,theinformationthathasbeensavedusingasaveorsave.imagecommandcanbereadusingtheloadcommand:

character(0)

>dir()

[7]"trialTable.csv""wholeShebang.RData"

>load("theInventories.RData")

[1]"inventories""trees"

ThecatcommandThecatcommandcanbeusedtotakealistofvariables,convertthemtoatextform,andconcatenatetheresults.Ifnofileorconnectorisspecified,theresultisprintedtothescreen;otherwise,theconnectorisusedtodeterminehowtheresultishandled.Notethatthereisanadditionalsetofcommands,thevariouswritecommands,butthosecommandsareconvenientroutinesthatallowashorthandnotationtoaccessthecatcommands.Thesecommandsareprimarilyusedinscripts:

>one<-"A"

>two<-"B"

>cat(one,two,"\n")

Thecatcommandallowsyoutospecifyanumberofoptions.Forexample,youcanspecifytheseparatorbetweenvariables,labelstobeused,orwhetherornottoappendtoagivenfile:

>cat(one,two,"\n",sep="-")

Theprint,format,andpastecommandsWeexaminethreewaystodisplayinformationusingtheprint,format,andpastecommands.Thesecanbeusedbyprogramstodisplaytheformattedoutput.Thethreecommandsprovidenumerousoptionstoensurethattheinformationappearsinhuman-readableforms.

Theprintcommandisusedtodisplaythecontentsofasinglevariable,asfollows:

>one<-"A"

>print(one)

[1]"A"

Thepastecommandtakesalistofvariables,convertsthemtocharacters,andconcatenatestheresult.Thisisausefulcommandtodynamicallycreateannotationsforplotsandfigures,asfollows:

>one<-"A"

>two<-"B"

>numbers<-paste(one,two)

>numbers

[1]"AB"

>numbers<-paste(one,two,sep="/")

>numbers

[1]"A/B"

Inthisexample,thenumbersvariableisastring,anditistheresultofconvertingtheargumentstoastringandconcatenatingtheresults.Inthesecondpartoftheexample,theseparatorwaschangedfromthedefault,aspace,toaforwardslash.

TheformatcommandconvertsanRobjecttoastring,anditallowsalargenumberofoptionstospecifytheappearanceoftheobject:

>three<-exp(1)

>nice<-format(three,digits=2)

[1]"2.7"

>nice<-format(three,digits=12)

[1]"2.71828182846"

>nice<-format(three,digits=3,width=5,justify="right")

[1]"2.72"

>nice<-format(three,digits=3,width=8,justify="right",decimal.mark="#")

[1]"2#72"

Inthisexample,theformatcommandisusedinvariouswaystoconvertanumericvariabletoastring.Thevariousoptionstochangethenumberofdigitsandthetotalnumberofcharactershasbeenchangedtorefinetheresults.

Primitiveinput/outputThereareanumberofprimitivecommandsthatofferfinegraincontrolforreadingandwritinginformationtoandfromafile.Wedonotprovideextensiveexamplesherebecausethesecommandsaremoreusefulwhencombinedwiththeprogrammingcommandsthatareexploredinlaterchapters.

Beforediscussingthesecommands,itisimportanttodiscusstheideaofaconnector.Aconnectorisagenericwaytotreatadatasource.Thiscanbeafile,anHTTPconnection,adatabaseconnection,oranothernetworkconnection.Inthissection,weonlyexploreonetypeofconnector,thatis,thebasictextfileconnector.Moreinformationcanbefoundusingthehelpcommand,help(file).Thefilecommandisusedtocreateaconnectortoafile.TheargumentstothefilecommandaresimilartothefopencommandfoundintheClanguage.

Themostbasicuseofthefilecommandrequiresthatyouprovideanameofafileandthemodethatwillbeusedinmanipulatingthefile.ThemodecantellRwhetherthefilewillbeusedtoreadorwriteaswellaswhetherornotitisabinaryfile.Inthisfirstexample,wewillopenafileandwriteadoubleprecisionnumberandthenacharacterstring.Inthenextexamplethatfollows,wewillopenthefileandreadtheinformationbackintotheworkspace.Towritetheinformation,wewillfirstusethefilecommandtoopenafile,calltwoBinaryValues.dat,andusethebinarymode.WewillthenusethewriteBincommandtowritethetwovalues.Weassumeherethatadoubleprecisionnumberrequiresfourbytes:

>fileConnector=file("twoBinaryValues.dat",open="wb")

>theNumber=as.double(2.72)

>writeBin(theNumber,fileConnector,size=4)

>note<-"hellothere!"

>nchar(note)

>writeBin(note,fileConnector,size=nchar(note))

>close(fileConnector)

Inthisexample,afileconnectoriscreatedtowriteinformationinabinaryformat.Twovariablesarethenwrittentothefile,andthefileisclosed.Thesameinformationisreadinthenextexample.ThereadBincommandisusedtoreadtheinformationfromthefile:

>fileConnector=file("twoBinaryValues.dat",open="rb")

>value<-readBin(fileConnector,double(),1,size=4)

>value

[1]2.72

>note<-readBin(fileConnector,character(),12,size=1)

[1]"hellothere!"

>close(fileConnector)

Thereareanumberofcommandsthatcanbeusedtoreadandwritecharacterdata.ThewriteCharandreadCharcommandsareusedtowriteandreadcharacterdatainasimilarwayasthewriteBinandreadBincommands.ThewriteLinesandreadLines

commandscanbeusedtowritewholelinesascharactersatonetime.

NetworkoptionsAnotherwaytoreadinformationisthroughanetworkconnectionusingsockets.Themethodsavailabletomanipulatesocketswillbebrieflyexploredinthissection.WefirstexplorethehighlevelsocketcommandsthatmakeuseofthesocketConnectioncommandtocreateaconnector.Next,someofthemorebasicoptionsarebrieflystated.Thisisanadvancedtopicbeyondthescopeofthisbook,butitisprovidedhereasamatterofcompleteness.

OpeningasocketThesocketConnectioncommandwillcreateanetworkconnectiontoagivenhostusingaportnumber.Thecommandreturnsaconnectorthatcanbetreatedthesameasafileconnector.Inthefollowingexample,aconnectionisopenedtothewaterdata.usgs.govwebsiteusingthestandardHTTPport,80.ItthensendstheHTTPheadernecessarytorequestthedataforthedailyflowratesfortheSouthColtonstationontheRaquetteRiverinnorthernNewYork:

>usgs<-socketConnection(host="waterdata.usgs.gov",80)

>writeLines("GET/ny/nwis/dv?

cb_00060=on&format=rdb&site_no=04267500&referred_module=sw&period=&begin_da

te=2013-05-08&end_date=2014-05-08HTTP/1.1",con=usgs)

>writeLines("Host:waterdata.usgs.gov",con=usgs)

>writeLines("\n\n",con=usgs)

>lines=readLines(usgs)

>lines

[1]"HTTP/1.1200OK"

[2]"Date:Fri,09May201416:28:26GMT"

[3]"Server:Apache"

[4]"AMF-Ver:4.02"

[5]"Connection:close"

[6]"Transfer-Encoding:chunked"

[7]"Content-Type:text/plain"

[9]"3bc"

[10]"#----------------------------------WARNING-----------------------

-----------------"

[11]"#ThedatayouhaveobtainedfromthisautomatedU.S.Geological

Surveydatabase"

[12]"#havenotreceivedDirector'sapprovalandassuchareprovisional

andsubjectto"

[13]"#revision.Thedataarereleasedontheconditionthatneitherthe

USGSnorthe"

[14]"#UnitedStatesGovernmentmaybeheldliableforanydamages

resultingfromitsuse."

[15]"#Additionalinfo:http://waterdata.usgs.gov/ny/nwis/?provisional"

[16]"#"

[17]"#File-formatdescription:http://waterdata.usgs.gov/nwis/?

tab_delimited_format_info"

[18]"#Automated-retrievalinfo:

http://help.waterdata.usgs.gov/faq/automated-retrievals"

[19]"#"

[20]"#Contact:gs-w_support_nwisweb@usgs.gov"

[21]"#retrieved:2014-05-0912:28:35EDT(vaww01)"

[22]"#"

[23]"#Dataforthefollowing1site(s)arecontainedinthisfile"

[24]"#USGS04267500RAQUETTERIVERATSOUTHCOLTONNY"

...[DeletedLines]...

[425]"USGS\t04267500\t2014-05-07\t5810\tP"

[426]"USGS\t04267500\t2014-05-08\t5640\tP"

[427]""

[428]"0"

[429]""

>close(usgs)

Inthisexample,asocketconnectioniscreated.ItisusedtomakeaconnectiontoaURLwithagivenport.ThesocketconnectoristhenusedtosendanHTTPheadertorequestaparticularpagefromthegivenhost.TheresultingpageisreadusingthereadLinescommand.ThereadLinescommandisusedtoreadeverylineasastring,andtheinformationinthevectorwillhavetobeparsedtotransformitintoauseableform.

BasicsocketoperationsAsidefromthehigherleveloptiongivenintheprevioussection,therearealsomoreprimitivecommandsforcreatingandusingasocket.Thecommandsexaminedarethemake.socket,read.socket,write.socket,andtheclose.socketcommands.Socketsarescarceresources,andchecksneedtobeputinplacetoensurethattheyarereleasedwhensomethinggoeswrong.Forthatreason,asocketisusuallycreatedwithinafunctionwithextrachecks.Theexamplehereisbasic,anditisprovidedsimplytodemonstratethecommands.Youshouldseethehelppagesforthesocketcommandsforamorecomprehensiveexample.

Toreplicatetheprecedingexample,thesocketisopened,andtheHTTPrequestissubmitted:

>socketRead<-make.socket("waterdata.usgs.gov",80)

>write.socket(socketRead,"GET/ny/nwis/dv?

cb_00060=on&format=rdb&site_no=04267500&referred_module=sw&period=&begin_da

te=2013-05-08&end_date=2014-05-08HTTP/1.1\n");

>write.socket(socketRead,"Host:waterdata.usgs.gov\n\n\n");

>incoming<-read.socket(socketRead);

>close.socket(socketRead)

[1]FALSE

Theincomingvariablewillnowcontaintheresultoftheoperation.

SummaryInthischapter,wehaveexploredsomeofthefacilitiesavailabletoreadandwriteinformationtoafileaswellaswaystoenterdatafromthecommandline.Weexaminedthewaystochangebetweendirectoriesandgetthedirectoryinformation.

Afterexaminingwaystoreaddata,weexploredsomeofthewaysthattheinformationcanbedisplayed.Wefirstexaminedhowtoprintinformationonthecommandline,andwethenexploredhowtosavedatatoalocalfile.Wealsoexploredhowthecurrentworkspacecouldbesavedandrecalledforuseinalatersession.

Oneimportanttopicexploredwashowtoworkwithfilesthatdonotfollowabasicformat.Weexaminedthecommandsnecessarytoreaddata,inbothtextandbinaryformswherethedatafilefollowsapredefinedstructure.

Thefinaltopicexploredwashowtousenetworkconnectionstoreadinformation.Thisincludedtheuseofasocketconnectortoallowaccesstorelativelywell-structuredinformation.Wealsoexploredmoreprimitiveoptionsthatallowustocreatesocketsandmanipulatetheminamorebasicform.

Inthepreviouschapters,weexaminedthebasicwaystostoredata.Inthechapterthatfollows,Chapter4,CalculatingProbabilitiesandRandomNumbers,wegetourfirstglimpseofthefunctionsavailabletohelpusunderstandhowtointerpretdata.Wewillexploresomeoftheoptionsavailabletoworkwithvariouspredefinedprobabilitydistributions.

Chapter4.CalculatingProbabilitiesandRandomNumbersInthischapter,weprovideabroadoverviewofthefunctionsrelatedtoprobabilitydistributions.Thisincludesfunctionsassociatedwithprobabilitydistributions,RandomNumberGeneration,andissuesassociatedwithsampling.Thechapterisdividedintofiveparts:

Distributionfunctions:ThissectiongivesyouabriefoverviewoftheideasandconceptsbehindrandomvariablesandapproximatingtheheightofaprobabilitymassfunctionofaprobabilitydensityfunctionforagivendistributionThecumulativedistributionfunction:ThissectiongivesyouanoverviewofhowtoapproximatethecumulativedistributionforagivendistributionTheinversecumulativedistributionfunction:ThissectiongivesyouanoverviewofhowtoapproximatetheinversecumulativedistributionfunctionforagivendistributionRandomNumberGeneration(RNG):ThissectiongivesyouanoverviewofhowRgeneratespseudorandomnumberswithexamplesofhowtogeneraterandomnumbersforagivendistributionSampling:Thissectiongivesyouanoverviewofsamplingdatafromagivenvector

OverviewThebaseRenvironmenthasoptionsforapproximatingmanyofthepropertiesassociatedwithprobabilitydistributions.Thisisnotacompletediscussion,andamorecompletelistcanbefoundwithintheRenvironmentusingthehelp(Distributions)command.Inthischapter,wewilldiscusshowRcanbeusedtoapproximatedistributionfunctions,howtoapproximatecumulativedistributionfunctions,howtoapproximateinversecumulativedistributionfunctions,RandomNumberGeneration(RNG),andsampling.

Thecommandsusedforthefirstsetoftopicshaveacommonformat,andeachcommandhastheformofaprefixandasuffix.Thesuffixspecifiesthedistributionbyname.Forexample,thenormsuffixreferstothenormaldistribution.AlistofthedistributionsavailableinthebaseRinstallationisgiveninTable1.Theprefixisoneofthefollowing:

d:Thisdeterminesthevalueofthedistributionfunction,forexample,dnormistheheightofthenormal’sprobabilitydistributionfunctionp:Thisdeterminesthecumulativedistribution,forexample,pnormisthecumulativedistributionfunctionforthenormaldistributionq:Thisdeterminestheinversecumulativedistribution,forexample,qnormistheinversecumulativedistributionfunctionforthenormaldistributionr:Thisgeneratesrandomnumbersaccordingtothedistribution,forexample,rnormcalculatesrandomnumbersthatfollowanormaldistribution

Asanexample,todeterminetheprobabilitythataPoissondistributionwillreturnagivenvalue,thecommandtouseisdpois,andthecommandtogettheprobabilitythataPoissondistributionislessthanorequaltoaparticularvalueisppois.

Inthischapter,weassumethatyouarefamiliarwiththeideaofarandomvariable.Inshort,arandomvariableisafunction,andthefunctionassignsanumberforeachoutcomeinthesamplespaceassociatedwithanexperiment.Acontinuousrandomvariableisafunctionthatcanincludeacontinuousrangeofvaluesinthevaluesitcanreturn.Adiscreterandomvariablecanonlyreturnnumbersfromadiscretesetofvalues.ThefollowingtableshowsthedistributionsavailableinR:

Discrete Continuous

Name Suffix Name Suffix

Beta beta X2 chisq

Binomial binom Exponential exp

Cauchy cauchy F f

Geometric geom Gamma gamma

Hypergeometric hyper LogNormal lnorm

Multinomial mutlinom Normal norm

NegativeBinomial nbinom Studentt t

Poisson pois Uniform unif

Weibull weibull

Table1–alistofdistributionsandthesuffixusedinRtorefertothem

DistributionfunctionsWewillfirstdiscussthewaytocalculatethevalueofadistributionfunctioninR.Wewillthendiscussdiscretedistributionsandthencontinuousdistributions.Thedistributionfunctionisusedtodeterminetheprobabilitiesthataparticulareventwilloccur.Inthecaseofadiscretedistribution,thefunctioniscalledaprobabilitymassfunction,andforacontinuousdistributionitiscalledaprobabilitydistributionfunction.Foradiscretedistribution,theprobabilitiesarecalculatedusingasumwheref(i)istheprobabilitymassfunction:

Eachdistributionhasitsownparametersassociatedwithit,andjudicialuseofthehelpcommandishighlyrecommended.Forexample,togetmoreinformationaboutthePoissondistribution,thehelp(dpois)commandcanbeused.InthecaseofthePoissondistribution,therearetwoparametersrequiredforthedpoiscommand.Thefirstisxandthesecondislambda.ThefunctionreturnstheprobabilitiesthataPoissonrandomvariablewiththelambdaparameterreturnsthevaluesgivenbyx.

Forexample,toplottheprobabilitiesforaPoissondistributionwithparameter10,thefollowingcommandsareusedtogeneratethevaluesthatcanbereturned(calledx),andabarplotisusedtodisplaythem:

>x<-0:20

>probabilities<-dpois(x,10.0)

>barplot(probabilities,names.arg=x,xlab="x",ylab="p",+main="Poission

Dist,l=10.0")

Forthecontinuousdistribution,therandomvariablecantakeonarangeofvalues,andinsteadofadding,wefindtheareaunderacurve,f(s),calledtheprobabilitydensityfunction:

Inthenextexample,weusetheX2distribution.TheideabehindtheX2distributionisthatyousamplenindependentrandomvariablesthatfollowastandardnormaldistribution.Youthensquarethevaluesthatweresampledandaddthemup.TheresultisdefinedtobeanX2distribution.Notethatthereisoneparameter,n,anditisgenerallyspecifiedbygivingthenumberofdegreesoffreedom,whichisdefinedtoben-1.Thenumberofdegreesoffreedomisoftenreferredtoasdf.

Forthisexample,thecommandtoobtainanapproximationfortheprobabilitydensityfunctionisdchisq,andittakestwoarguments,xanddf.Here,weplottheprobability

densityfunctionfortwoX2distributionswithtwodifferentparameters.Firstarangeofvalues,x,isdefined,andthentheheightoftheprobabilitydensityfunctionforanX2

distributionwithdf=30isplotted.Next,anotherX2distributionwithdf=35isplottedonthesameplotusingthepointscommand:

>x<-seq(0.0,100.0,by=0.1)

>prob<-dchisq(x,df=30)

>plot(x,prob,main="ChiSquaredDist.",xlab='x',ylab='p',col=2,type="l")

>probTwo<-dchisq(x,df=35)

>points(x,probTwo,col=3,type="l")

CumulativedistributionfunctionsAnotherimportanttoolusedtoapproximateprobabilitiesisthecumulativedistributionfunction.Inmanysituations,wearerequiredtodeterminetheprobabilitythatarandomvariablewillreturnavaluewithinsomegivenrangeofvalues.Thecumulativedistributionallowsustouseashortcuttocalculatetheresultingprobability.Asintheprevioussection,welookatdiscreteandcontinuousdistributionsseparately.Weexaminethedefinitionandlookatexamplesforbothcases.

Foradiscretedistribution,thecumulativedistributionfunctionisdefinedtobethefollowingequation:

Fromthisdefinition,theprobabilitythatarandomvariableisbetweentwonumberscanbedeterminedbythefollowingequation:

Pleasenotethedetailsintheinequality.Inthecaseofdiscretedistributions,itmattersiflessthanorequalisusedasopposedtolessthan.

ForaPoissondistribution,thecommandtodeterminethecumulativedistributionfunctionistheppoiscommand,andithasthesameargumentsasthedpoiscommanddiscussedearlier.Thefollowingexamplemirrorstheexampleintheprevioussection,andthecumulativedistributionfunctionisplottedforaPoissondistributionwithparameter10.0:

>x<-0:20

>cdf<-ppois(x,10.0)

>barplot(cdf,names.arg=x)

Thecumulativedistributionfunctionforacontinuousrandomvariableisdefinedinthesamewayasthatofadiscreterandomvariable.Theonlydifferenceisthatinsteadofasumweuseanintegral,asfollows:

Thisdefinitiongivesthesameresultasbefore,andtheprobabilitythatarandomvariableisbetweentwonumberscanbedeterminedbyusingthefollowingequation:

ThefollowingexamplecomparesthecumulativedistributionfunctionsfortwoX2distributionswherethefirsthas30degreesoffreedomandthesecondhas35degreesoffreedom:

>x<-seq(0.0,100.0,by=0.1)

>cdf<-pchisq(x,df=30)

>plot(x,cdf,main="ChiSquaredDist.",xlab='x',ylab='p',col=2,

+type="l")

>cdfTwo<-pchisq(x,df=35)

>points(x,cdfTwo,col=3,type="l")

InversecumulativedistributionfunctionsThecumulativedistributionfunctionisoftenusedtocalculateprobabilities,butinothercircumstancesthegoalistofindarangeofvaluesgiventheprobability.Inthiscase,theinversecumulativedistributionfunctioncanbeusedtodetermineavalueoftherandomvariablethatcorrespondstoagivenprobability.Theideaisthatgiventheprobability,youwanttosolveforthevalueofaintheexpression:

Forexample,supposethatwehaveaPoissonrandomvariablewithparameter10.0andwishtofindthevalueofaforwhichtheprobabilityoftherandomvariableislessthanais0.5.Usingtheqpoiscommand,wecandeterminethevalue:

>a<-qpois(0.5,10.0)

ThisresultindicatesthattheprobabilitythataPoissonrandomvariablewithparameter10.0islessthan10.0is0.5,orputanotherwaythemedianis10.0.Sincethisisalsothemeanoftherandomvariable,wecanseethatthereisnoskewassociatedwiththedistribution.

WenowdothesamefortheX2distributionwith30degreesoffreedom.Theqchisqcommandcanbeusedtodeterminethemedianforthisdistribution:

>a<-qchisq(0.5,df=30)

[1]29.33603

Inthiscase,weseethatthemedianislessthanthemean,whichis30,sotheX2distributionwith30degreesoffreedomisskewedtotheleftsincethemedianistotheleftofthemean.

GeneratingpseudorandomnumbersAcommontaskinsimulationsistogeneraterandomnumbersthatfollowagivendistribution.Weexplorethisimportanttopic,butitisimportanttomakeafewnotesaboutRandomNumberGeneration.First,andforemost,despitethenomenclature,thenumbersarenotrandombecausetheyaregeneratedusingadeterministicalgorithm.Secondly,whendebuggingandcomparingcodeusedtosimulateastochasticprocess,itisimportanttobeabletogeneratenumbersinarepeatablewaytoensurethattheresultsareconsistent.

Beforediscussinggeneratingrandomnumbers,weprovidesomeminimalbackgroundinformationabouthowRgeneratesrandomnumbers.Thisisacomplextopic,andyoucanfindmoredetailsusingthehelp(RNG)command.Onethingtonoteisthatthe.Random.seedvariablehasthevalueofthecurrentseed,butitisnotdefineduntilyoudosoexplicitlyoracommandiscalledthatrequiresthatarandomnumberbegenerated.Thevariablecanbesetdirectly,butitisbettertochangeitusingtheset.seedcommand.Also,thealgorithmthatisusedtogeneraterandomnumberscanbesetorobtainedusingtheRNGkindcommand.Notethatthesavecommandcanbeusedasaconvenientwaytosavetheseedifrepeatabilityofyourresultsisimportantasyoumakechangestoestablishedcode.

RandomNumberGenerationcanbeadauntingsubject,butweprimarilyfocusonhowtogeneraterandomnumbersaccordingtoagivendistribution.Asbefore,wefirstexamineadiscretedistribution;thePoissondistributionwithparameter10.0.WethenexamineanX2distributionwith30degreesoffreedom.Ineachcase,wegenerateonehundredrandomnumbersandcreateahistogramoftheresults.

Firstweexaminethediscretedistribution.Therpoiscommandcanbeusedtogeneratethenumber.Ittakestwoparameters,thenumberofpointstoapproximateandtheparameterassociatedwiththedistribution:

>numbers<-rpois(100,10.0)

>hist(numbers,main="100SamplesofaPoissonDist.",xlab="x")

Likewise,aX2distributioncanalsobesampled,andtheresultsareplottedusingahistogram:

>numbers<-rchisq(100,df=30)

>hist(numbers,main="100SamplesfromaChi-SquaredDist.",xlab="x")

SamplingThefinaltopicthatwewilldiscussissampling.Thiscanalsobeacomplicatedsubject,anditisoftenusedinbootstrappingandawidevarietyofothertechniques.Becauseofitsprevalence,weprovideitasaseparatesection.

Thesolefocusofthissectionisonthesamplecommand.Itmayseemoddtograntsuchattentiontoasinglecommand,butsamplingisacomplextopicwithmoreopinionsassociatedwithitthantherearestatisticians.Thesamplecommandrequiresatleastoneargument,avectororanumber,anditreturnsasetofvalueschosenatrandom.Theoptionsforthecommandallowyoutospecifyhowmanysamplestotake,whetherornottousereplacement,andaprobabilitydistributionifyoudonotwishtouseauniformmassfunction.

Thesamplefunction’sbehaviordependsonwhetherornotyougiveitavectororanumber.Ifyoupassanumbertoit(thatis,avectoroflength1),itwillsamplefromthesetofwhole,positivenumberslessthanorequaltothatnumber:

>sample(3)

[1]321

>sample(5)

[1]23145

>sample(5.6)

[1]51432

Ifyoupassitavectorwhoselengthisgreaterthan1,itwillsamplefromtheelementsinthevector:

>x<-c(1,3,5,7)

>sample(x)

[1]7153

Ifyoudonotspecifythenumberofsamplestotake,itwillusethenumberofobjectspassedtoit.Thesizeparameterallowsyoutospecifyadifferentnumberofsamples:

>x<-c(1,3,5,7)

>sample(x,size=2)

>sample(x,size=3)

[1]135

>sample(x,size=8)

Errorinsample.int(length(x),size,replace,prob):

cannottakeasamplelargerthanthepopulationwhen'replace=FALSE'

Intheprecedingexample,thenumberofsamplesislargerthanthenumberofelementsavailable.Toavoidanerror,youhavetospecifysamplingwithreplacement:

>x<-c(1,3,5,7)

>sample(x,size=8,replace=TRUE)

[1]35553751

Inthepreviousexamples,thesampleswerefoundusingthedefaultalgorithm.Thedefaultistouseauniformprobabilitymassfunctionmeaningeveryelementhasthesame

likelihoodofbeingchosen.Youcanchangethisbehaviorbyspecifyingavectorofprobabilitiesthathasthelikelihoodofchoosingeachparticularelementofthevector:

>x<-c(1,3,5,7)

>sample(x,size=8,replace=TRUE,p=c(0.05,.10,.15,.70))

[1]73377775

SummaryInthischapter,weexaminedabroadoverviewofsomeoftheprobabilityfunctionsinthebaseRinstallation.Theseincludefunctionstoapproximatethedistributionfunction,thecumulativedistributionfunction,andtheinversecumulativedistribution.Wealsoexaminedhowtogeneratepseudo-randomnumbersforthevariousdistributions.Thefinaltopicexploredwastheuseofthesamplecommand,whichisusedforsamplingfromagivendatasetstoredasavector.

Inthenextchapter,westepbackfromthemoremathematicalideasexploredinthischapterandlookattheprogrammingfacilitiesthatcanbeusedtomanipulatestrings.Thisisanimportanttopicasitisnotuncommonfordatasetstoincludestringvariables,anditisoftennecessarytoextractoraddinformationtothevariableswithinadataset.

Chapter5.CharacterandStringOperationsThischapterwillprovideyouwithabroadoverviewoftheoperationsavailableforthemanipulationofcharacterandstringobjects.Thisisarelativelyconcisechapter,andthefocusisonbasicoperations.Thereareroughlytwopartsinthischapter:

Basicstringoperations:ThissectiongivesyouabroadoverviewofthemostbasicstringoperationsRegularexpressions:Thissectiongivesyouabriefintroductionofthreecommandsthatmakeuseofregularexpressions

BasicstringoperationsInsomesituations,dataiskeptintheformofcharactersorstrings,whereasinsomeothersituationsthestringsmustbeparsedorinvestigatedaspartofastatisticalanalysis.Becauseoftheprevalenceofdataintheformofstrings,theRlanguagehasarichsetofoptionsavailableformanipulatingstrings.Inthischapter,weinvestigatesomeoftheoptionsavailable,andourinvestigationisdividedintotwoparts.Theyareasfollows:

Inthefirstpart,weexamineanumberofbasicstringoperationswhosefunctionisfocusedonparticularoperationsInthesecondpart,weexaminethefunctionsthatarebasedonregularexpressionsthatofferapowerfulsetofwiderangingoperations

Theuseofregularexpressionsrepresentapowerfulsetoftoolsforstringmanipulation,butthecostisgreatercomplexity.Inthischapter,weonlyfocusonthemostbasicusesofthesefunctionsastheirmostcommonusestendtobepartsofmorecomplexcodethatmayuseacomplicatedcombinationofthecommands.

Asawaytomaketheconnectionbetweenthecommands,weassumeacommondatasetthroughoutthischapter.Inparticular,weassumethatwehaveasetofURLs:

>urls<-c("https://duckduckgo.com?q=Johann+Carl+Friedrich+Gauss",

"https://search.yahoo.com/search?p=Jean+Baptiste+Joseph+Fourier",

"http://www.bing.com/search?q=Isaac+Newton",

"http://www.google.com/search?q=Brahmagupta")

WewillexaminevariouswaystopullinformationfromeachURL.SuchataskmaybenecessaryeithertopullinformationfromawebsiteortoperformananalysisontheURLsthemselves.

SixfocusedtasksWefirstexaminesixspecifictasks.Theseoperationsaretodeterminethelengthofastring,locationofasubstring,extractorreplaceasubstring,changethecaseofastring,splitastringintoseparateparts,andexpressacombinationofobjectsasasinglestring.Pleasenotethattheexampleswillmakeextensiveuseofthedefinitionoftheurlvectorthatisdefinedintheprevioussection.

DeterminingthelengthofastringThencharcommandisusedtodeterminethelengthofastring.Thisoptioncanbeusedaspartofasimplestatisticforasetofstringsorcanbepartofaprogrammingtoolwhenitisnecessarytoiterateoverastring.Youcanspecifyhowthecountiscalculatedwithoptionstocountthenumberofcharacters,bytes,orwidthoftheresultingstring.Inmostcases,thesevalueswillbethesamebutcandifferdependingontheUnicodesettingsforyourenvironment:

>nchar(urls)

[1]52624142

Thencharcommandtriestocoerceitsargumenttoastring,whichmeansthatitinterpretsthevaluesofobjectswhosetypeisNAasthestringNA.Anothersideeffectisthatitcanreturnanerrorwhenusedonfactors:

>>nchar(c("one",NA,1234))

[1]324

>nchar(as.factor(c("a","b","a","a","b","c")))

Errorinnchar(as.factor(c("a","b","a","a","b","c"))):

'nchar()'requiresacharactervector

IfyouarerunningRwithinaUnicodeenvironmentsuchasUTF-8,thiscommandmayreturnanerror.Ifthisisthecase,trytheallowNA=TRUEoptiontoseewhethertheresultsareappropriate.

Onelastnote;thereisanadditionalcommand,nzchar,whichteststodeterminewhetherthecharacterwidthofthestringhaszerolength.Thisfunctionreturnsalogicalvalue,anditistrueifthestringdoesnothaveazerolength.Asanexample,supposeyouhavealistoffiletypeswithanemptystringbeinganunrecognizedfiletype.Youmaywanttousenzchartocreateamasktoskiporignoreanemptystring:

>fileTypes<-c("txt","","html","txt")

>nzchar(fileTypes)

[1]TRUEFALSETRUETRUE

>fileTypes[nzchar(fileTypes)]

[1]"txt""html""txt"

LocationofasubstringInsomecircumstances,itisnecessarytodeterminethelocationwithinastringfortheoccurrenceofasubstring.Forexample,itmightbenecessarytosearchasetoffilenamestodeterminewhethertheycontainamatchforapredeterminedparameter.Thecommandtoreturnthelocationofasubstringistheregexpcommand.Thiscommandhasmanyoptions,anditisexploredinmoredetailinthefollowingsection.Here,weuseitinitsmostbasicformtodeterminethelocationofasubstring.

Usingthedefinitionofthevector,urls,asanexample,wemaywishtofindthelocationofthecolonsintheURLs.Thecolonsarethedelimiterbetweentheprotocolandthehostname,andwecannotassumeitwillalwaysbeinthesameposition:

>colons<-regexpr(":",urls)

>colons

[1]6655

attr(,"match.length")

[1]1111

attr(,"useBytes")

[1]TRUE

>colons[2]

>colons[3]

Notethattheassumedpositionofthefirstcharacterisoneandnotzero.

ExtractingorchangingasubstringTherearetwocommandsavailabletochangeasubstringwithinastring.Thetwocommandsaresubstrandsubstring,andtheirargumentsareidentical.ThesubstringcommandiscompatiblewithS,butwefocusontheRcommandsubstr.Thecommandhastwoforms.Oneformcanbeusedtoextractasubstring,andtheotherformcanbeusedtochangethevalueofasubstring.

First,weexaminetheoptiontoextractasubstring.Thecommandtakesastring,thelocationofthestartofthesubstring,andthelocationoftheendofthesubstring.Makinguseofthepreviousexample,wenowusetheurlsvectordefinedatthestartofthechapteranddeterminetheprotocolforeachURL:

>protocols<-substr(urls,1,colons-1)

>protocols

[1]"https""https""http""http"

Asubstringcanbereplacedbycombiningthesubstrcommandandtheassignmentoperator.Unfortunately,thelengthofthestringinsertedmustbethesamelengthasthestringbeingreplaced,whichcanreturnnonintuitiveresults.Here,wereplaceeachoftheprotocolsintheurlsvectorwithamailtoprotocol:

>colons<-regexpr(":",urls)

>mailto<-urls

>substr(mailto,1,colons-1)<-c("mailto","mailto","mailto")

>mailto

[1]"mailt://duckduckgo.com?q=Johann+Carl+Friedrich+Gauss"

[2]"mailt://search.yahoo.com/search?p=Jean+Baptiste+Joseph+Fourier"

[3]"mail://www.bing.com/search?q=Isaac+Newton"

[4]"mail://www.google.com/search?q=Brahmagupta"

Notethatthestring“http”hasfewercharactersthanthestring“mailto,”andthecommandhastruncatedthenewstringthatissubstitutedintotheoriginalstring.

TransformingthecaseInsomeanalyses,thecaseofalettermaynotbeconsideredimportant.Inthesesituations,itmaybenecessarytoconvertthecaseofastring.Thetolowerandtouppercommandscanbeusedtoensurethatastringisintheexpectedform:

>tolower(urls[1])

[1]"https://duckduckgo.com?q=johann+carl+friedrich+gauss"

>toupper(urls[2])

[1]"HTTPS://SEARCH.YAHOO.COM/SEARCH?P=JEAN+BAPTISTE+JOSEPH+FOURIER"

Anadditionalcommand,chartr,enablesmorefine-grainedcontrolofcharacterreplacement.Theideaisthattherearesomecharactersintheoriginalstringthatwewanttoreplace,andweknowwhatthereplacementcharactersare.Forexample,goingbacktotheurlsvectordefinedpreviously,wemaywanttoreplacetheoccurrencesof=witha#charactertodelimittheendoftheURLandthestartoftheargumentlist.Supposewealsowanttoreplacethe+characterswithspaces.Wecandosobyusingthechartrcommand,wherethefirstargumentisastringwhosecharacterelementswillbechanged,thesecondargumentisastringthatcontainsthereplacementcharacters,andthethirdargumentisthestringtochange:

>>chartr("=+","#",urls)

[1]"https://duckduckgo.com?q#JohannCarlFriedrichGauss"

[2]"https://search.yahoo.com/search?p#JeanBaptisteJosephFourier"

[3]"http://www.bing.com/search?q#IsaacNewton"

[4]"http://www.google.com/search?q#Brahmagupta"

SplittingstringsAstringcanbedividedintomultiplepartsusingthestrsplitcommand.Thisisusefulifyouhavedatainapredefinedformatandwishtodividethestringsintocomponentpiecesforaseparateanalysis.Usingtheurlsvectordefinedearlier,wemaywishtodivideeachURLintoitsprotocolandtherestoftheinformation:

>splitURL<-strsplit(urls,":")

>splitURL

[1]"https"

[2]"//duckduckgo.com?q=Johann+Carl+Friedrich+Gauss"

[1]"https"

[2]"//search.yahoo.com/search?p=Jean+Baptiste+Joseph+Fourier"

[1]"http"

[2]"//www.bing.com/search?q=Isaac+Newton"

[1]"http"

[2]"//www.google.com/search?q=Brahmagupta"

>splitURL[[1]]

[1]"https"

>splitURL[[1]][2]

Notethatitreturnsalist,andeachentryinthelistisavectorofstringsthathavebeensplit.

CreatingformattedstringsThesprintffunctionallowsyoutotakeacombinationofobjectsandexpressthemasaformattedstring.Thepaste,format,andprintcommandshavebeenexaminedinChapter3,SavingDataandPrintingResults,andthosefunctionscanbeusedtoaccomplishsimilarresults.TheprimarydifferenceisthatthesprintffunctionactsliketheClanguage’ssprintffunction.Forexample,supposewehavealoopthatperformsananalysisonthetextfoundateachoftheURLsfoundintheurlsvectordefinedearlier.Aspartoftheanalysis,wemayhavetheresultofacalculationstoredinavariableandwishtousethatnumberinthetitleforagraph.Inthefollowingexample,wesimplysetthevalue,though,tokeeptheexamplemorestreamlined:

>calculation<-123.0

>theTitle<-sprintf("URL:%s,Count=%d",urls[n],calculation)

>theTitle

[1]"URL:https://duckduckgo.com?q=Johann+Carl+Friedrich+Gauss,Count=123"

The%scharactersrefertoastringintheargumentlist,and%dreferstoanintegernextintheargumentlist.ThesedefinitionsfollowthesamespecificationastheClanguagedefinitionofsprintf.

RegularexpressionsThecommandsexaminedintheprevioussectionlackflexibility,buttheyarestraightforwardintheirimplementation.Theuseofregularexpressions,ontheotherhand,offersamoreelegantapproachinmanycircumstances,buttheycanbemorecomplex.Webrieflyexamineafewcommandsthatallowstringmanipulationsviaregularexpressions,andweassumethatyouarefamiliarwithregularexpressions.TogetmoreinformationaboutregularexpressionsinRyoucanusethehelp(regularexpression)command.

Inthissection,wewillfocusonthegregexprandgsubcommands.Thereareanumberofothercommandsthatarelistedwhenyouenterthehelp(gregexpr)command.Also,thecommandshaveanumberofoptions,butwewillonlyexaminetheirmostbasicforms.Asaquicknote,thepatternsubmittedtothegrepcommanddiscussedearliercanalsobearegularexpression.

Thegregexprcommandisageneralcommandthatreturnsthenumberofresultswithrespecttotheregularexpression.Inparticular,itwillreturnthelocationofamatch,whetherornotamatchwasfound,andthenumberofcharactersinmatch.Thefirstargumenttothefunctionisapatternandthenthestringstomatch.Thefunctionreturnsalistthathasinformationaboutmatch.Inthefollowingexample,weexaminetheresultsofsearchingforthe=delimiterinthefirstentryinoururlsvectordefinedearlier:

>loc<-gregexpr("=",urls[[1]])

attr(,"match.length")

attr(,"useBytes")

[1]TRUE

>loc[[1]][1]

Notethatthefunctionreturnsalist.Sincetheuseofasinglebracereturnsanotherlist,weusethedoublebracestoensurethatwereturntheelementwithinthelistasavector.

Thesubcommandcanbeusedtoreplacealloccurrencesofapatternwithinastring.Thisfunctiontakesthreearguments,thepattern,thereplacementstring,andthestringsthatareusedtoperformtheoperation:

>sub("\\?.*$","",urls)

[1]"https://duckduckgo.com""https://search.yahoo.com/search"

[3]"http://www.bing.com/search""http://www.google.com/search"

Notethattwobackslashesmustbeusedinthepreviousexample.Thefirstbackslashisusedtoindicatethatthenextcharacterisasymboltobeinterpreted,andifasecondbackslashisused,thenitmeanstointerpretthepairasasinglebackslash.

SummaryInthischapter,wehaveexploredavarietyofwaystomanipulatestringvariables.Twobroadcategorieshavebeenexplored.Thefirstsetoffunctionsprovideanumberoffunctionswithanarrowrangeoffunctionality.Thesefunctionsarerelativelystraightforwardbutmustoftenbeusedtogethertoaccomplishcomplextasks.Thesecondsetoffunctionsmakeuseofregularexpressions,andtheycanbeusedtoaccomplishacomplexsetoftasksusingasmallnumberofsteps.

Inthenextchapter,wemoveontothetopicofworkingwithvariablesassociatedwithtime.ThisincludestranslatingstringsintoR’sbuilt-intimetypes,anditalsoincludesthekindofoperationsthatcanbeperformedontimevariables.

Chapter6.ConvertingandDefiningTimeVariablesThischapterprovidesabroadoverviewofthewaystoconvertandstoretimevariables.Thisisamundanetopic,butitiscommontohavedatathatcontainsdateandtimeinformationinawidevarietyofforms.Thereareroughlythreepartsinthischapter:

Convertingstringstotimedatatypes:ThissectiongivesyouanintroductiontothemethodsavailabletotakeatimestampintextformatandconvertitintooneofR’sinternaltimeformatsConvertingtimedatatypestostrings:ThissectiongivesyouanintroductiontothemethodsavailabletotakeatimedatatypeandconvertittoastringsothatitcanbesavedtoafileinastandardformatOperationsontimedatatypes:Thissectiongivesyouanoverviewofthemethodsandtechniquesusedtoperformbasicarithmeticontimedatatypes

IntroductionandassumptionsDateandtimeformationsareoftensavedaspartoftheinformationwithinadataset.Convertingtheinformationintoadateortimevariableisoneofthelessexcitingchorestoperform,anditissomethingthatrequiresagreatdealofcare.Inthischapter,wewilldiscussthewaysoftransformingstringsanddatatypesanddemonstratehowtoperformbasicarithmeticoperations.Itisimportanttonotethatworkingwithtimeanddatedataoccursinanumberofdifferentcontexts,andthereareanumberofdifferentlibraries,suchaschron,lubridate,anddate,tohelpyouworkwithtimeanddatavariables.

Ourfocushere,though,isonR’sbuilt-infunctionsusedtoworkwithtimeanddatedata.Itisimportanttonotethatthecommandsexploredherecanbesensitivetosmallvariationswithinadatafile,andyoushouldalwaysdoublecheckyourworkespeciallywithrespecttotimedata.Itcanbeatedioustask,butitisimportanttomakesurethatthedataiscorrect.Ifyoucommonlyworkwithtimedata,youshouldreadthedetailsfoundusingthehelp(DateTimeClasses)command.

Anothercomplicationisthattimezonescanchange,andthegeneralpracticesassociatedwithtimedatacanchange.Forexample,newtimezonescanbechanged,created,orremoved,oraregioncanchangeitstimezone.Youshouldalwaysensurethatyourpracticesassociatedwithtimedataareconsistentwiththepracticeusedtogeneratethedatathatyouhave.

ConvertingstringstotimedatatypesThefirsttasktoexamineistotakeastringandconvertittoeachoftheinternaltimeformats.ThestrptimecommandwilltakeastringandconvertittoaPOSIXlttimevariable.IfyouwishtoconvertastringtoaPOSIXctdatatype,youcancasttheresultofstrptimeusingtheas.POSIXctcommand.WefirstfocusonconvertingastringtoaPOSIXltdatatypeandprovideanexampleattheendofthissectiontobeconvertedtoaPOSIXctdatatype.

Toconvertastringtoatimedatatype,theformatforthestringmustbespecified,andtheformattingoptionsmustconformtotheISOC99/POSIXstandard.Thestringincludesasequenceofliteralcharactersandapartiallistofconversionsubstrings,whichisgiveninTable1.Forexample,the%Y-%m-%d%H:%M:%Sstringindicatesthatthedateshouldlooklike2014-05-1409:54:10whenreferringtoMay14,2014at9:54inthemorning.Astringisassumedtoincludeanynumberofpredefinedstrings.Anythingelseisconsideredtobealiteralcharacterthatmustappearexactlyasitappearsintheformatstring.

Oncetheformatstringhasbeenspecified,thestrptimecommandcanbeusedtoconvertastringintothePOSIXltdatatype.Theargumentsforthecommandarethestringstoconvert,followedbytheformatstring:

>theTime<-c("08:30:001867-07-01","18:15:001864-10-27")

>converted<-strptime(theTime,"%H:%M:%S%Y-%m-%d")

>converted

[1]"1867-07-0108:30:00""1864-10-2718:15:00"

>typeof(converted[1])

[1]"list"

>converted[1]-converted[2]

Timedifferenceof976.5938days

Thecommandalsoacceptsanoptionaltimezoneoption,asfollows:

>theTime<-c("08:30:001867-07-01","18:15:001864-10-27")

>converted<-strptime(theTime,"%H:%M:%S%Y-%m-%d",+tz="Canada/Eastern")

>converted

[1]"1867-07-0108:30:00EST""1864-10-2718:15:00EST"

>converted[1]-converted[2]

TheresultsreturnedbythestrptimecommandareofthePOSIXltdatatype.Theycanbeconvertedusingtheas.POSIXctcommand:

>theTime<-c("08:30:001867-07-01","18:15:001864-10-27")

>converted<-strptime(theTime,"%H:%M:%S%Y-%m-%d",tz="Canada/Eastern")

>converted

[1]"1867-07-0108:30:00EST""1864-10-2718:15:00EST"

>typeof(converted[1])

[1]"list"

>otherTime<-as.POSIXct(converted)

>otherTime

[1]"1867-07-0108:30:00EST""1864-10-2718:15:00EST"

>typeof(otherTime[1])

[1]"double"

>cat(otherTime[1],"\n")

-3234681000

Notethatwhenthedatevariablesareprinted,theyareconvertedtoahuman-readableformat.Thisisconvenient,butitcanhidetheunderlyingdatatype.Again,youshouldbecarefulaboutvariablesthathaveatimedatatypesinceitiseasytolosetrackofhowtheRenvironmentisactuallytreatingthevalues.

Anotherimportantconcernthatyoushouldbewaryofisthatthestrptimecommandwillnotgiveavisibleindicationwhenanerroroccurs.ItwillreturnNAinplaceofeacherror,asfollows:

>aTime<-c("2014-05-0508:00:00","2014/05/0508:00:00")

>internal<-strptime(aTime,"%Y/%m/%d%H:%M:%S")

>internal

[1]NA"2014-05-0508:00:00"

>is.na(internal)

[1]TRUEFALSE

Anotherwaytosavethedateandtimeinformationinafileistospecifythedateandtimeinonecolumn.Iftheinformationfromthefileisstoredasadataframe,thenanewcolumncanbeaddedthatcontainstheinformationsavedinaninternalformat:

>fileInfo<-data.frame(time=c("2014-01-0100:00:00",

+"2013-12-3123:59:50","2013-12-3123:55:12"),

+happiness=c(1.0,0.9,0.8))

>fileInfo

timehappiness

12014-01-0100:00:001.0

22013-12-3123:59:500.9

32013-12-3123:55:120.8

>fileInfo$internalTime<-strptime(fileInfo$time,"%Y-%m-%d%H:%M:%S")

>fileInfo

timehappinessinternalTime

12014-01-0100:00:001.02014-01-0100:00:00

22013-12-3123:59:500.92013-12-3123:59:50

32013-12-3123:55:120.82013-12-3123:55:12

>summary(fileInfo)

timehappinessinternalTime

2013-12-3123:55:12:1Min.:0.80Min.:2013-12-3123:55:12

2013-12-3123:59:50:11stQu.:0.851stQu.:2013-12-3123:57:31

2014-01-0100:00:00:1Median:0.90Median:2013-12-3123:59:50

Mean:0.90Mean:2013-12-3123:58:20

3rdQu.:0.953rdQu.:2013-12-3123:59:55

Max.:1.00Max.:2014-01-0100:00:00

Thevariouscommandsusedtoconvertastringtoatimeordatevariablehavealargevarietyofoptions.TheseoptionscanbefoundwithRusingthehelp(trptime)command.Alistoftheoptionscanalsobefoundinthefollowingtable:

Formatstring Meaning Format

string Meaning

%a Abbreviationforthenameofthedayoftheweek %p Indicatorfor“A.M.”or“P.M.”

%A Fullnameofthedayoftheweek %SSecondsasanumber(00-61);leapsecondsareallowed

%b Abbreviationforthenameofthemonth %U Weakoftheyearasanumber(00-53)

%B Fullnameofthemonth %wWeekdayasanumber(0=Sunday,1=Monday,…,6=Saturday)

%cThedateandtimeintheformat%a%b%e%H:%M:%S%Y

%x Dateintheform“%y/%m/%d”

%d Dayofthemonthasanumber(01through31) %X Timeintheformat“%H:%M:%S”

%HHoursasanumber(00-23);notethat24isallowedasanexceptionwhenusedas24:00:00

%yYearastwodigits(“00-99”),and00-68refertothe2000swhile69-99refertothe1900s

%I Hoursin12hourformatasanumber(01-12) %Y Yearasfourdigits(>=1582)

%j Dayoftheyearasanumber(001-366) %zOffsetfromUTC(-0500isfivehoursbehindUTC)

%m Monthasanumber(01-12) %ZTimezoneasastring(onlyavailableforconvertingtimetoastring)

%M Minuteasanumber(00-59)

Table1–charactersusedtodefinetheformatsofdates

Moreoptionsareavailableandcanbeviewedusingthehelp(strftime)commandwithintheRenvironment.

ConvertingtimedatatypestostringsAdateandtimevariablecanbeconvertedtoastringusingthestrftimecommand.Itsformatissimilartothestrptimecommand,exceptithasoneadditionaloptiontodeterminewhetherornottoincludetimezoneinformationintheresultingstring.Thecommandisaconveniencefunction,anditcallseithertheformat.POSIXltorformat.POSIXctcommanddependingonthedatatypeofthetimevariable.Wefocusonthestrftimecommandbecauseitisfamiliartopeoplefromawidervarietyofprogrammingexperiences.

Thestrftimecommandrequiresatimevariableandaformat.Itreturnsastringinthegivenformat(someformatoptionsaregiveninTable1):

>theTime<-c("08:30:001867-07-01","18:15:001864-10-27")

>converted<-strptime(theTime,"%H:%M:%S%Y-%m-%d",

+tz="Canada/Eastern")

>converted

[1]"1867-07-0108:30:00EST""1864-10-2718:15:00EST"

>typeof(converted)

[1]"list"

>backAgain<-strftime(converted,"%j-%B")

>backAgain

[1]"182-July""301-October"

>typeof(backAgain[1])

[1]"character"

OperationsontimedatatypesAvarietyofarithmeticoperationsareavailableforthetimedatatypes,andyoushouldbeespeciallywarywhenperforminganyoperations.Theunitsthatarereturnedcanvarydependingonthecontext.Itisextremelyeasytolosetrackoftheunitsandmakeaspuriouscomparison.Inthissection,I’llintroducesomeofthebasicoperationsandthendiscussthedifftimecommand.Itispossibletoperformanyoperationwithoutthedifftimecommand,butthecommandhasanimportantadvantage:itallowsyoutoexplicitlydefinetheunits.

Whenyouperformsimplearithmeticontimedatatypes,itactsinthewayyoumightexpect:

>earlier<-strptime("2014-01-0100:00:00","%Y-%m-%d%H:%M:%S")

>later<-strptime("2014-01-0200:00:00","%Y-%m-%d%H:%M:%S")

>later-earlier

Timedifferenceof1days

>timeDiff<-later-earlier

>timeDiff

Timedifferenceof1days

>as.double(timeDiff)

>earlier+timeDiff

[1]"2014-01-02EST"

Notethatinthepreviousexample,theunitsusedaregivenindays.Onesmallchange,though,resultsinadifferentkindofresult:

>later-earlier

Timedifferenceof12hours

>timeDiff

>as.double(timeDiff)

TheRenvironmentwillkeeptrackofthedifferenceanditsunits.Ifyoulookatthedifftimevariableinthepreviousexample,itcontainsinformationaboutwhatitmeans:

>attributes(timeDiff)

$units

[1]"hours"

$class

[1]"difftime"

>attr(timeDiff,"units")

[1]"hours"

Youcanspecifytheunitsbycastingtheresultasanumericvalueandprovidingtheunitstouse:

>timeDiff

>as.numeric(timeDiff,units="weeks")

[1]0.07142857

>as.numeric(timeDiff,units="secs")

[1]43200

Becauseofpotentialambiguities,itisusuallyadvantageoustousethedifftimecommand.Thedifftimecommandoffersawiderangeofoptions,andyoushouldcarefullyreaditshelppage,help(difftime),toseetheoptionsanddetails.Initsmostbasicuse,youcanfindthedifferencebetweentwotimes,andyoucanspecifywhatunitstouse:

>timeDiff<-difftime(later,earlier,units="sec")

>timeDiff

Timedifferenceof43200secs

>timeDiff<-difftime(later,earlier,units="day")

>timeDiff

Theunitsthatareavailableareauto,secs,mins,hours,days,orweeks.

Onefinalnote;thedifftimedatatypeoffersaconvenientwaytoperformsometimearithmeticoptions.Theas.difftimecommandcanbeusedtospecifyatimeinterval,andyoucanspecifytheunits,soitismorelikelythattheresultsareconsistentwithyourexpectations:

>oneHour=as.difftime(1,units="hours")

>later+oneHour

[1]"2014-01-0113:00:00EST"

SummaryAbroadoverviewoftheoptionsavailabletoconvertbetweenstringsandthetwotimedatatypeswasgiveninthischapter.ThestrptimecommandisusedtoconvertastringintoaPOSIXltvariable.Thestrftimecommandisusedtoconvertatimedatatypeintoastring.Finally,thebasicoperatorsusedtoperformarithmeticoperationsbetweentwotimevariableswasdiscussed,withanemphasisonusingthedifftimecommand.Wealsoexploredtheuseofthedifftimedatatype.

Chapter7.BasicProgrammingInthepreviouschapters,weexploredthebasicaspectsofhowRstoresinformationandthedifferentwaystoorganizeinformation.Wewillnowexplorethewaythatoperationscanbedefinedandexecuted,andwriteaprograminR.TheabilitytocreatealgorithmsthatcombinefunctionstocompletecomplicatedtasksisoneofR’sbestfeatures.Wecontinuetheexplorationofprogramminginthenexttwochaptersandfocusonobject-orientedapproaches.Thischapterisdividedintofourparts:

Conditionalexecution:Inthissection,wewillintroduceif-then-elseblocksanddiscusslogicaloperatorsLoopconstructs:Inthissection,wewillexplorethreedifferentwaystoimplementloopsFunctions:Inthissection,wewilldiscusshowtodefinefunctionsinRandexploresomeoftheimportantconsiderationsassociatedwithfunctionsScriptexecution:Inthissection,wewilldiscusshowtoexecuteasetofcommandsthathavebeensavedinafile

ConditionalexecutionThefirstcontrolconstructexaminedistheifstatement.Anifstatementwilldeterminewhetherornotacodeblockshouldbeexecuted,andithasanoptionalelsestatement.Anadditionalifstatementcanbechainedtoanelsestatementtocreateacascadeofoptions.

Initsmostbasicform,anifstatementhasthefollowingform:

if(condition)

codeblock

Theconditioncanbealogicalstatementconstructedfromtheoperatorsgiveninthenexttable,oritcanbeanumericresult.Iftheconditionisnumeric,thenzeroisconsideredtobeFALSE,otherwisethestatementisTRUE.Thecodeblockcaneitherbeasinglestatementorasetofstatementsenclosedinbraces:

>#checkif1islessthan2

>if(1<2)

+cat("oneissmallerthantwo.\n")

oneissmallerthantwo.

>if(2>1){

+cat("Buttwoisbiggeryet.\n")

Buttwoisbiggeryet.

Notethatacommentisincludedinthepreviouscode.The#characterisusedtodenoteacomment.Anythingafterthe#characterisignored.

Theifstatementcanbeextendedusinganelsestatement.TheelsestatementallowsyoutospecifyacodeblocktoexecuteiftheconditiondoesnotevaluatetoTRUE:

>if(1>2){

+cat("Oneisthemostbiggestnumber\n")#Saysomethingwrong

+}else{

+cat("Oneistheloneliestnumber\n")#Saysomethinglesswrong

Oneistheloneliestnumber

ThefirstthingtonoteabouttheseexamplesisthataKernighanandRitchie(K&R)indentationstyleisadopted.WeadoptedtheK&Rstylebecausetheelsestatementmustbeonthesamelineastheclosingbrace.Thesecondthingtonoteisthatanotherifstatementcanbeappendedtotheelsestatementsothatastandardif-then-elseblockcanbeconstructed:

>if(0){

+cat("Yes,thatisFALSE.\n")

+}elseif(1){

+cat("YesthatisTRUE\n")

+}else{

+cat("Whatever")

YesthatisTRUE

OnepotentialissueisthatRdoesnothaveascalartypeandassumesmostdatatypesare

arrangedinvectors.Thiscanleadtopotentialproblemswithalogicalstatement.ThefirstelementinthevectorisusedtodecidewhetherawholestatementisTRUEorFALSE.Fortunately,Rwillgiveawarningiftheconditionevaluatestoavectoroflengthgreaterthanone:

>x<-c(1,2)

>if(x<2){

+cat("Ohyesitis\n")

Ohyesitis

Warningmessage:

Inif(x<2){:

theconditionhaslength>1andonlythefirstelementwillbeused

Thisissomethingtokeepinmindwhendecidingwhichlogicaloperatortouseinanifstatement.Forexample,the|operatorwillperformalogicalORonallelementsofthevectors,butthe||operatorwillonlyperformalogicalORonthefirstelementsofthevectors:

>x<-c(FALSE,FALSE,TRUE)

>y<-c(FALSE,TRUE,TRUE)

[1]FALSETRUETRUE

[1]FALSE

Avarietyoflogicaloperatorsarerecognized.Someprovidecomparisonsbetweenallentriesinavectorandothersareforcomparisonsonlyforthefirstelementsinthevectors.Alistoftheoperatorsisgiveninthefollowingtable:

Operator Description Operator Description

< Lessthan(vector) | Or(vector)

> Greaterthan(vector) || Or(firstentryonly)

<= Lessthanorequal(vector) ! Not(vector)

>= Greaterthanorequal(vector) & And(vector)

== Equalto(vector) && And(firstentryonly)

!= Notequalto(vector) xor(a,b) Exclusiveor(vector)

Table1–Thelogicaloperatorsincludingcomparisonoperators

LoopconstructsAnotherimportantprogrammingtaskistocreateasetofinstructionsthatcanberepeatedinastructuredway.TherearethreeloopconstructsinR:for,while,andrepeatloops.Wewillexploreeachoftheseloopconstructsanddiscussthebreakandnextcommands,whichcancontrolhowtheinstructionswithinaloop’scodeblockareexecuted.Moredetailsareavailableusingthehelp(Control)command.

TheforloopAforlooptakesavector,anditrepeatsablockofcodeforeachvalueinthevector.Thesyntaxisasfollows:

for(varinvector)

codeblock

Theforlooprepeatsasetofinstructionsforeveryvaluewithinavectorintheappropriateorder.Itcanbememoryintensiveifyouneedtorepeattheloopalotoftimesandthevectorisnotalreadystoredintheworkspace.Anexampleofasimpleforloopisgivenhere:

>for(lupeinseq(1,2,by=0.33))

+cat("Thevalueoflupeis",lupe,"\n")

Thevalueoflupeis1

Thevalueoflupeis1.33

ThewhileloopAwhileloopwillexecuteacodeblockaslongasagivenexpressionistrue.Thesyntaxisasfollows:

while(condition)

codeblock

Theyareoftenusedwhenthenumberofiterationsisnotknowninadvanceandtheloopisrepeateduntilsomecriteriaismet.Also,thewhileloophassomeadvantagesovertheforloop.Itcanbemoreefficient,especiallywithrespecttomemory,anditismoreflexible(RMemory,PleaserefertoRMemory:HadleyWickham,memory,2014,http://adv-r.had.co.nz/memory.htmlformoreinformationonthistopic).Forexample,insteadofconstructingalargevectortoiterateoveritselements,asingleindexcanbeused.Onthedownside,itcanbehardertoread,anditcanrequirealittlemorecarewhenwritingthecode.Anexampleisgivenhere:

>lupe<-1.0;

>while(lupe<=2.0)

+cat("Thevalueoflupeis",lupe,"\n")

+lupe<-lupe+0.33

Thevalueoflupeis1

TherepeatloopArepeatloopisusedtodenoteablockofcodethatwillberepeatedlyexecuteduntilanexplicitbreakoutoftheblockisexecuted.Theprimaryadvantageoftherepeatloopisthatthestartofthecodeblockwillalwaysbeexecuted.Onedisadvantageisthatitcanbedifficulttoread.Thesyntaxisrelativelysimple:

repeat

codeblock

YoumustuseabreakcommandtotellRtoexitthecodeblock.Thebreakcommandisdescribedinthenextsubsectioninmoredetail.Anexampleofarepeatloopisgiveninthefollowingexample,anditisabriefexampleofsimulationofarandomwalkinthecomplexplane:

positions<-complex(0)#Initializethehistoryofpositions

currentPos<-0.0+0.0i#Startattheorigin

NUMBERSTEPS<-50#Numberofstepstotake

angleFacing<-0.0#directionitisfacing

stdDev<-1.0#stddev.ofthechangeintheangle

step<-as.integer(0)

repeat

##Updatethecurrenttimestep

step<-step+as.integer(1)

##Checktoseeifitistimetostop

if(step>MAXNUMBER)

##Addnewpeopletothelineandupdatethelength

angle<-angle+rnorm(1,0.0,stdDev)

currentPos<-currentPos+exp(angle*1.0i)

positions<-c(positions,currentPos)

plot(Re(positions),Im(positions),type="l")

BreakandnextstatementsThebreakandnextstatementsareusedtoinfluencewhichpartofthecodeinthecurrentloopwillbeexecuted.Thebreakstatementwillmovetotheveryendofthecurrentblockanditwillstoptheexecutionoftheloop.Thenextstatementwillactasiftheendofthecodeblockwasreachedandstartoveratthebeginningofthecodeblocktobeginthenextiteration.

Asademonstration,webuildonthesimulationoftherandomwalkintheprevious

example.Wealterthemodelbyplacingarestrictionontheposition.Ifastepmovestotheleft-handpartoftheplane,itisignored:

positions<-complex(0)#Initializethehistoryofpositions

currentPos<-0.0+0.0i#Startattheorigin

NUMBERSTEPS<-50#Numberofstepstotake

angleFacing<-0.0#directionitisfacing

stdDev<-1.0#stddev.ofthechangeintheangle

step<-as.integer(0)

repeat

##Addnewpeopletothelineandupdatethelength

newAngle<-angle+rnorm(1,0.0,stdDev)

proposedStep<-currentPos+exp(newAngle*1.0i)

if(Re(proposedStep)<0.0)

next#Ignorethisstep.Itmovestoneg.realparts

##updatetheposition

angle<-newAngle

currentPos<-proposedStep

positions<-c(positions,currentPos)

##Updatethecurrenttimestep

step<-step+as.integer(1)

##Checktoseeifitistimetostop

if(step>MAXNUMBER)

plot(Re(positions),Im(positions),type="l")

FunctionsAnothercommonprogrammingtaskistodefineafunctionorsubroutinethatcanbeexecutedwithasinglecall.Definingandusingfunctionscanbecomplicatedbecauseofthetechnicaldetailsassociatedwithworkingwithvariablesthatexistindifferentcontextsbutmayhavethesamename.AnotherproblemthatarisesisthateverythinginRisanobject.Uptothispoint,wehavequietlyignoredthisissue,butitisatechnicalissuethatwemustnowconsider.Inthissection,wewillfirstdemonstratehowtodefineafunction.Wewillthendiscussthedetailsabouthowargumentsarepassedtoafunction.Finally,wediscussthetechnicaldetailsofhowRdetermineswhatavariablenamemeans.

Beforewegetintothosedetails,wewillprovideanoteabouthowRkeepstrackoffunctions.Whenwedefineavariable,Rtreatsthatvariableasanobjectthatcanbeaccessedusingthenameweassigntothevariable.Likewise,whenyoudefineanewfunction,itisassignedavariablename,andthevariableisanobject.

DefiningafunctionAspreviouslymentioned,whenafunctionisdefined,itisassignedtoanobjectandtreatedlikeanyothervariable.Theformatforafunctiondefinitionisasfollows:

function(arg1,arg2,…)

codeblock

Thiswillcreateanobject,andyoumustassignavariablenametotheobject.Ifyouprintoutthevalueofthevariable,itwillprintoutthedefinitionofthefunction.Inthefollowingexample,supposeweneedafunctionusedtosimulatearandomwalkinthecomplexplane.Thefunctiontakesthecurrentpositionandaddsaunitstepinarandomdirection:

>updatePosition<-function(currentPos)

+newDirection<-exp(1i*runif(1,0.0,2.0*pi))

+currentPos+newDirection

>updatePosition(0.0)

[1]0.9919473-0.1266517i

>updatePosition

function(currentPos)

newDirection<-exp(1i*runif(1,0.0,2.0*pi))

currentPos+newDirection

Oneoddityassociatedwithfunctionsisthatthevalueitreturnsisthelastexpressionevaluatedwithinthecodeblock.

Therearetimeswhenyouwantafunctiontoperformoperationsthatimpactmorethanonevariable.Insuchcases,youmayneedtoreturnacombinationofresults.Fordifficultresultsthatcannotbeexpressedasavector,youcanreturntheresultasalist.Inthe

followingexample,weextendthepreviousexampleandwishtoreturnthenewpositionaswellastheupdateddirectionofmovement:

>updatePosition<-function(currentPos,angle,stdDev)

+angle<-angle+rnorm(1,0,stdDev)

+list(newPos=currentPos+exp(angle*1.0i),

+newAngle=angle)

>pos<-updatePosition(2.0,0.0,1.0)

$newPos

[1]2.986467+0.163962i

$newAngle

[1]0.164706

>pos$newPos

[1]2.986467+0.163962i

Itispossibletoexplicitlyspecifythevaluereturnedbyafunctionusingthereturncommand.Thereturncommandtakesatmostoneargument.Thecommandwillexitthefunctionandreturnthevaluegivenintheargumentifitexists.Inthefollowingexample,webuildonourexampleofarandomwalkinthecomplexplane.Here,weassumethattheleft-handsideoftheplaneisnotreachable,andiftherealpartofastepisnegative,thenthestepmovesintheoppositedirection:

+angle<-angle+rnorm(1,0.0,stdDev)

+newStep<-exp(angle*1.0i)

+if(Re(currentPos+newStep)<0.0)

+#Thiswouldbeamoveinthelefthandpartofthe

#plane.

+#Moveintheoppositedirection.

+return(list(newPos=currentPos-newStep,

+newAngle=angle+pi))

+#Allisgood.Acceptthismove.

+return(list(newPos=currentPos+newStep,

+angle=angle))

>pos<-updatePosition(-0.1+2i,0.0,1.0)

>pos$newPos

[1]0.459425+2.828881i

ArgumentstofunctionsWehavediscussedhowtodefineanewfunctionandbrieflydiscussedhowtopassargumentstoafunction.Wenowfocusonsomedetailsaboutpassingargumentstoafunction.First,wenotethattheargumentsthatarepassedtoafunctionarepassedasvaluesandnotreferences.Anychangesyoumaketoanargumentdonotimpactthe

variableoutsidethefunction.Inthefollowingexample,wegobacktoourfunctiontoupdatethepositionforarandomwalkinthecomplexplane.Wepasstheangletothefunctionthatischangedwithinthefunctionbutnotoutsideofthefunction:

+angle<-angle+rnorm(1,0.0,stdDev)

+currentPos+exp(1i*angle)

>angle<-0.0

>updatePosition(1+2i,angle,1.0)

[1]0.250178+2.661639i

>angle

Anotherimportantpointisthatyoucanprovidedefaultvaluesforsomearguments.Ifadefaultvalueisgivenforavariable,thenitisnotrequiredwhilecallingthefunction:

>updatePosition<-function(currentPos,angle=0.0)

+print(noquote(paste("Angleis",angle)))

+angle<-angle+runif(1,0.0,2.0*pi)

>updatePosition(1+2i)

[1]Angleis0

[1]0.091507+2.417901i

>updatePosition(1+2i,pi)

[1]Angleis3.14159265358979

[1]0.029198+2.239884i

Therearecircumstancesinwhichyoumightwishtocheckwhetheraparticularargumenthasbeenspecifiedwhenthefunctioniscalled.Thiscanbedoneusingthemissingcommand.Inthenextexample,wetestwhetherornottheangleisprovided:

+if(missing(angle))

+warning("Usingthedefaultdrift:",angle)

>updatePosition(1+2i)

[1]0.552725+1.105604i

Warningmessage:

InupdatePosition(1+(0+2i)):Usingthedefaultdrift:0

>updatePosition(1+2i,pi)

[1]0.054151+1.675394i

Notethatthewarningcommandwasusedtoprintoutamessage.Whenexecutinga

function,itmaybebeneficialtoprintawarningortostoptheexecutionofthefunctionduetoanerrorcondition.Thestopandwarningcommandscanbeusedforthesesituations.Thewarningcommandprintsoutawarningandcontinuesexecutionasnormal.Thestopcommandwillprintoutamessageandexitthefunction:

+if(abs(angle)>2.0*pi)

+stop("Iarbitrarilydonotlikeanglesthatbig")

>pos1<-updatePosition(1+2i)

>pos2<-updatePosition(1+2i,3.0*pi)

ErrorinupdatePosition(1+(0+2i),3*pi):

Iarbitrarilydonotlikeanglesthatbig

Error:object'pos2'notfound

Notethatinthelastline,thestopcommandwascalledandthefunctiondidnotreturnavalue.Theresultisthatthevariablepos2doesnotexist.Becarefulthough,asifthevariablepos2hadbeenpreviouslydefined,itwouldretainitspreviousvalue.

Intheprecedingexamples,thereisanassumptionabouttheorderoftheargumentswhencallingafunction.Inthepreviousexamples,theargumentsarematchedaccordingtotheordertheyappearinthefunctioncall.Youcancircumventthisconventionbyspecifyingthenamewhenyoucallthefunction.ThecaveatisthatRdoesnotrequirethatthenameshouldmatchexactly,anditwilltrytomatchthenamesusingthefirstcharactersinthename.Ifthematchisambiguous,youwillgetanerrormessage:

>matching<-function(argOne,argTwo)

+return(paste("Igotthis:",argOne,'',argTwo))

>matching(argTwo="second",argOne="First")

[1]"Igotthis:Firstsecond"

>matching(argT="2nd",argO="1st")

[1]"Igotthis:1st2nd"

>matching(argT="two",arg="one")

Errorinmatching(argT="two",arg="one"):

argument2matchesmultipleformalarguments

Thelastissuetodiscussishowtolimitthepotentialvaluesthatanargumentmayhave.Thedefaultvaluesforanargumentcanbegivenasavectorofvalues.Ifnoargumentisgiven,itdefaultstothefirstentryinthevector.Ifyouwishtolimitthevaluestobeoneofthevaluesinthevector,youcanusethematch.argfunctiontotestthevalue:

>updatePosition<-function(currentPos,angle=0.0,

+dist=c("uniform","normal"))

+dist<-match.arg(dist)

+print(dist)

+#Updatepositioncodewouldgobelow

>updatePosition(0.0,0.0)

[1]"uniform"

>updatePosition(0.0,0.0,"uniform")

[1]"uniform"

>updatePosition(0.0,0.0,"neither")

Errorinmatch.arg(dist):'arg'shouldbeoneof"uniform","normal"

ScopeAnimportantquestionwhendealingwithafunctionishowtodecidewhatasymbolmeans.Thisideaisreferredtoasscope,andthelanguageusedtodescribetheideasassociatedwithscopecanbeconfusing.Unfortunately,itissomethingthatneedstobeconsidered,andwetrytodiscusssomeoftheideashere.ItisalsoimportanttonotethatthedetailsdiscussedhererepresentanareainwhichRisnotconsistentwiththeS_PLUSlanguage,sobecarefulaboutgeneralizingtheseideas.Forfurtherinformation,youcanenterthedemo(scoping)commandintheRenvironmentandabriefdemonstrationofthenotionofscopeisgiven.Ifyouenterthehelp(environment)command,youcanalsofindmoredetails.

ThebasicideaisthatRmaintainsahierarchyofenvironments.Eachenvironmenthasalistofsymbolsthatareassociatedwiththatenvironment.Youcancreateanewenvironmentthatisembeddedwithinanotherenvironment.Thenew.envcommandisusedtocreateanenvironment.Thisenvironmentholdsitsownvariables,andvariablescanbecreatedusingtheassigncommand.Thevaluescanbeobtainedusingthegetcommand:

>envOne<-new.env()

>typeof(envOne)

[1]"environment"

[1]"envOne"

>ls(envOne)

character(0)

>assign("bubba",12,envir=envOne)

[1]"envOne"

>ls(envOne)

[1]"bubba"

>envOne$bubba

>get("bubba",envOne)

>bubba

Error:object'bubba'notfound

Notethatintheprecedingexample,weusedtheoptionalenvironmentargumenttothelscommand,whichspecifieswhichenvironmenttouse.TheenvironmentisusedtoguideR

inhowtointerpretthemeaningofasymbol.TheRenvironmentmaintainsapaththatitusestosearchinaparticularordertofindasymbol.Onewaytomanipulatethepathistousetheattachanddetachcommands.Theattachanddetachcommandshavenumerousoptions,butwefocusonhowtouseitwithenvironments.Wealsoprovideawarningthatusingthesecommandscanleadtoconfusionaboutthemeaningofasymbol,andyoushouldexercisecautionwhenusingthesecommands:

character(0)

>one<-2

[1]"one"

>envTwo<-new.env()

>assign("two",3,envir=envTwo)

Error:object'two'notfound

>attach(envTwo)

[1]"envTwo""one"

>detach(envTwo)

Error:object'two'notfound

Thereasonweexplorethistopichereisthatwhenyoudefineafunction,anewenvironmentiscreatedthatexistswithinthefunction.Whenyouuseasymbolwithinafunction,itcanbeambiguousastowhatitmeans.Ifthatsymbolhasbeenpreviouslydefinedwithinthefunction,thenitistreatedasalocalvariable.Ifthatsymbolexistsintheparentenvironment,thenitispossibletogetaccesstoitorchangeitsvalue.InChapter1,DataTypes,itwasbrieflynotedthatthe<-operatorisusedtoassignavariableinthelocalcontext.The<<-operatorisusedtotellRtofirstsearchtheparentenvironment:

>one<-2

>changeOne<-function(a)

+one<-a

+return(one)

>changeOne(3)

>realyChangeOne<-function(a)

+one<<-a

+return(one)

>realyChangeOne(3)

Again,<<-tellsRtousetheparentofthecurrentenvironment.Thatmeansthatifyoucreateafunctionwithinafunction,theuseofthe<<-operatorwithintheinnermostfunctionwilllookforavariableintheoriginal(outermost)function.Thisideaisexaminedinthefollowingexample:

>market<-function(rutabagas)

+money<-0

+return(list(

+numberRutabagas=function()

+return(rutabagas)

+revenue=function()

+return(money)

+harvestRutabagas=function(amount)

+rutabagas<<-rutabagas+amount

+sellRutabagas=function(amount)

+if(rutabagas>=amount)

+rutabagas<<-rutabagas-amount

+money<<-money+amount*0.5

+warning("Wedonothavethatmanyrutabagas")

+return(rutabagas)

>farmerJoe<-market(20)

>farmerJoe$numberRutabagas()

>farmerJoe$sellRutabagas(6)

Warningmessage:

InfarmerJoe$sellRutabagas(15):Wedonothavethatmanyrutabagas

>farmerJoe$harvestRutabagas(10)

Notethattheprecedingexamplegivesusourfirsttasteofobject-orientedprogramming.WewillexplorethisinmoredetailinChapter8,S3Classes,andwillbuildontheidea.

ExecutingscriptsThefinaltopicishowtoexecuteasetofcommandsthathavebeensavedinafileusingthecommandlineinaninteractiveRsession.Allofourexampleshavebeencontrived,andthereasonforthisistotrytofocusonaspecificidea.TherealpowerofRthoughistheabilitytoputtogetherasetofcommandsandhavethemexecutedinorder.Thiscanbeaccomplishedusingthesourcecommand.

Weneedtohaveafiletoexecute.Weassumethatyouhavethefilegivenhere.Youcancreatethisfileusinganyeditorcapableofsavingsimpletextfiles,andweassumethatthenameofthefileissimpleExecute.R:

#FilesimpleExecute.R

#Thisisasimpleexampleusedtodemonstratethesourcecommand.

#Thisscriptwillpromptthepersonrunningittoenteranumber,

#anditwillfindthesquarerootofthenumber.

#Itteststheoriginal

#numbertomakesureitispositiveandprintsoutanappropriate

#warningmessageifitisnegative.

x<-as.double(readline("Whatisthevalueofx?"))#Readinanumber

cat("Igotthenumber",format(x,digits=6),".\n")

if(x<0)

#Thenumberisnegative.Whataretheythinking?

print("Whywouldyougivemeanegativenumber?")

x<-abs(x)

#Findthesquarerootandassignitthevariable"y."

y<-sqrt(x)

Youcanexecutethefileusingthesourcecommand.OneimportantthingisthattheRenvironmentmustfinditonyourlocalmachine.Youcaneitherspecifythesearchpathoryoucanspecifythecurrentworkingdirectory.TheeasiestwaytodothisdependsonhowyouarerunningR,theinterfaceyouareusing,andyouroperatingsystem.Weassumethatyoucanspecifythecurrentworkingdirectory(folder),andthefilegivenearlierisinthatdirectory.Onceyouspecifythecurrentworkingdirectory,youcanexecutethecommandsusingthesourcecommand,asfollows:

>source('simpleExecute.R')

Whatisthevalueofx?2.3

Igotthenumber2.3.

>source('simpleExecute.R')

Whatisthevalueofx?-2.3

Igotthenumber-2.3.

[1]"Whywouldyougivemeanegativenumber,jerk?"

>source('simpleExecute.R',echo=TRUE)

>x<-as.double(readline("Whatisthevalueofx?"))

Whatisthevalueofx?-2.3

>cat("Igotthenumber",format(x,digits=6),".\n")

Igotthenumber-2.3.

>if(x<0)

+print("Whywouldyougivemeanegativenumber,jerk?")

+x<-abs(x)

[1]"Whywouldyougivemeanegativenumber,jerk?"

>y<-sqrt(x)

Thecommandhasanumberofoptions.Oneoptionnotexploredhereistheverboseoption.Thisisahelpfuloptionfordebugging,andyoushouldtrytoaddtheverbose=TRUEoption.Anexampleisomittedbecausereferringtoitasverboseisanunderstatement.

SummaryThischapterintroducedbasicideastospecifyoptionalexecutionofcertaincommandsandthethreebasicloopconstructs.WehadtotakeasidetriptodiscusstheideaofscopeandexplorehowRfindsandinterpretsthemeaningofavariablename.Youcancombinetheseideastocreateandimplementalgorithmsandexecutecommandsinafile.

Thischapteralsoincludesourfirsttasteofobject-orientedprogramminginthesenseofanS3class.WebuildonthisideainthenextchapterwheretheS3classisformallydefined.Indoingso,weexplorehowexistingfunctionscanbeextendedtoaccommodateargumentsthatincludeaclassthatwehaveconstructed.

Chapter8.S3ClassesThisisthesecondchapterinourintroductiontoprogramming.Intheprecedingchapter,weexploredthebasiccontrolstructuresthathelpustodefinethecodethatisexecuted,andwehadourfirsttasteofobjects.Wewillnowbuildontheideaofobject-orientedprogramming,concentratingonS3classes.Therearetwoapproaches,S3andS4classes.Itiscommonforsomepeopletouseonlyoneexclusively.

Thischapterisdividedintothreeparts:

Definingclassesandmethods:ThissectionwillgiveageneralideaofhowmethodsaredefinedwhosefunctiondependsontheclassnameoftheprimaryargumentObjectsandinheritance:Inthissection,wewilldiscussthewayinwhichobjectsofagivenclasscanbedefined;wewillalsointroducetheideaofinheritanceinthecontextofS3classesEncapsulation:Inthissection,wewilldiscusstheimportanceofencapsulationwithrespecttoaclassandhowitishandledwithinthecontextofanRclass

Atfirstglance,S3objectsdonotappeartobehavelikeobjectsasdefinedinotherlanguages.ThedefinitionisanoddimplementationcomparedtoJavaorC++.Ontheplusside,S3objectsarerelativelysimpleandcanofferapowerfulwaytodealwithawidevarietyofcircumstances.

Wehaveseenavarietyofdatastructuresaswellasfunctions,andinthischapter,wewillseehowtheclassattributecanbeusedtodictatehowafunctionrespondswhenalistispassedtoafunction.Theideaisthattheclassattributeforanobjectisavectorofnames,andthevectorrepresentsanorderedsetofnamestosearchwhendecidingwhatactionafunctionshouldtake.Wewillbuildonandextendoneexamplethroughoutthischapter.Theideaisthatwewishtocreateasetofclassesthatcanbeusedtosimulatearandomvariable,whichfollowsageometricdistribution.Therewillbetwoclasses.Thefirstclassisforafaircoin,inwhichweflipthecoinuntilheadsistossed.Thesecondclassisforafair,six-sideddie,inwhichwerolluntila1isrolled.

DefiningclassesandmethodsTheclasscommandissimilartootherattributecommands,anditcanbeusedtoeithersetorgetinformationaboutanobject’sclass.Anobject’sclassisavector,andeachiteminthevectoristhenameofaclass.Thefirstelementintheclassvectoristheobject’sbaseclass,anditinheritsfromtheotherclassesasyoureadfromlefttoright.

Wefirstfocusonthesituationwhereanobjecthasasingleclassandwillexamineinheritanceinthesectionthatfollows.Theexampleexaminedthroughoutthischapterisusedtosimulateoneexperimentthatfollowsageometricdistribution.Theideaisthatyourepeatsomeexperimentandstopwhenthefirstsuccessoccurs.First,weexaminetwoclasses,andweconstructafunctionthatwilltakeanactiondependingontheclassname.Thefirstclassisusedtorepresentafair,six-sideddie.Thediewillberolled,givinganintegerbetween1and6inclusive,andtheexperimentstopswhena1isreturned.Thesecondclassrepresentsafaircoin.ThecoinwillbeflippedreturningeitheranHoraT,andtheexperimentstopswhenHisreturned.

Thetwoclassdefinitionsareillustratedinthefollowingfigure.Eachclasskeepstrackofthetrials,andtheresultsarekeptinavector.Thetwomethodsincludeamethodtoresetthehistory,butmorewillbeaddedwhenweexamineinheritance.Inthisexample,wearenotcreatingmethodsinthetraditionalsensebutarecreatingfunctionsthattakeappropriateactionbasedontheclassnameoftheargumentpassedtothem.Havealookatthefollowingdiagram:

Themethodsassociatedwiththedieandcoinclasses

First,wedefinethetwoclasses.Eachclassiscomposedofalist,andtheclassnamesaresettoDieandCoinrespectively.(Thenamesarestringsthatwemakeup.)Eachclassconsistsofalistwithasinglenumericvectorthatinitiallyhasalengthofzero.Ineachofthefollowingcases,thelistiscreatedmanually,andaclassnameisdefined.Wecouldhaveusedavector,butweusedalistsothattheexamplesareconsistentwiththewayweextendtheclasseslater:

>oneDie<-list(trials=character(0))

>class(oneDie)<-"Die"

>oneCoin<-list(trials=character(0))

>class(oneCoin)<-"Coin"

First,wedefinetwosetsoffunctions.Thefirstsetoffunctionsresetsthehistory,andthesecondsetperformsasingleBernoullitrial.Wefirstfocusonaroutinetoresetandinitializethehistory,anddefineafunctioncalledreset.Theresetfunctionmakesuseofthreedifferentfunctions.ThefirstusestheUseMethodcommand,whichwilltellRtosearchfortheappropriatefunctiontocall.Thedecisionisbasedontheclassnameoftheobjectpassedtoitasthefirstargument.TheUseMethodcommandlooksforotherfunctionswhosenameshavetheformresetTrial.class_name,wheretheclass_namesuffixmustexactlymatchthenameoftheclass.Theexceptionisthedefaultsuffixthatisexecutedifnootherfunctionisfound:

reset<-function(theObject)

UseMethod("reset",theObject)

print("ResettheTrials")

reset.default<-function(theObject)

print("Uhoh,notsurewhattodohere!\n")

return(theObject)

reset.Die<-function(theObject)

theObject$trials<-character(0)

print("Resetthedie\n")

return(theObject)

reset.Coin<-function(theObject)

theObject$trials<-character(0)

print("Resetthecoin\n")

return(theObject)

Notethatthefunctionsreturntheobjectpassedtothem.RecallthatRpassesargumentsasvalues.Anychangesyoumaketothevariablearelocaltothefunction,sothenewvaluemustbereturned.WecannowcalltheresetTrialfunction,anditwilldecidewhichfunctiontocall,giventheargumentpassedtoit.Havealookatthefollowingcode:

>oneDie$trials=c("3","4","1")

>oneDie$trials

[1]"3""4""1"

>oneDie<-reset(oneDie)

Resetthedie

>oneDie

$trials

character(0)

attr(,"class")

[1]"Die"

>oneCoin$trials=c("H","H","T")

>oneCoin<-reset(oneCoin)

Resetthecoin

>oneDie$trials

character(0)

>#Lookatanexamplethatwillfailandusethedefaultfunction.

>v<-c(1,2,3)

>v<-reset(v)

[1]"Uhoh,notsurewhattodohere!\n"

[1]123

NotethattheprintcommandaftertheUseMethodcommandinthefunctionresetTrialisnotexecuted.Whenthereturnfunctioniscalled,anycommandsthatfollowtheUseMethodcommandarenotexecuted.

DefiningobjectsandinheritanceTheexamplesgivenintheprevioussectionshouldinvokeatwingeofshameforthosefamiliarwithobject-orientedprinciples,andyoushouldbeassuredthatIfeltappropriatelyembarrassedtosharethem.Itwasdone,though,tokeeptheintroductiontoS3classesassimpleaspossible.Oneissueisthatthetwoclassesarecloselyrelated,andthefunctionsincludeagreatdealofrepeatedcode.Wewillnowexaminehowinheritancecanbeusedtoavoidthisproblem.

Inthissection,wedefineabaseclass,GeometricTrial,andthenredefinetheroutinessothattheDieandCoinclassescanbederivedfromthebaseclass.Indoingso,wecandemonstratehowinheritanceisimplementedinthecontextofanS3class.Additionally,werespecttheideaofencapsulation,whichistheprinciplethatanobjectofagivenclassshouldupdateitsownelementsusingmethodsfromwithintheclass.Weexplorethisissueingreaterdetailinthesectionthatfollows.

Wewillnowrethinkthewholeclassstructure.Thedieandthecoinarecloselyrelated,andtheonlydifferenceistheresultreturnedfromasingletrial.Wereimaginetheclassestotakeadvantageofthecommonalitiesbetweenthecoinandthedie.Thenewclassstructureisshowninthefollowingdiagram:

Inadditiontothechangeintheclasses,wealsochangethewayinwhichtheclassesaredefined.Inthiscase,wedefinefunctionsthatwillactasconstructorsforeachclass.Eachconstructorwillusetheclasscommandtoappendthenameoftheclasstotheobject’sclassattribute.Aspreviouslymentioned,theclassattributeforanobjectisavector.When

youcalltheUseMethodcommand,Rwillsearchforafunctionwhoseclassmatchesthefirstelementinthevector.Ifitdoesnotfindthatfunction,itlooksforafunctionthatmatchesthesecondelement,anditproceedsuntilitreachesthelastelementinthevector.Ifitdoesnotfindanything,itcallsthedefaultfunction.Withthisinmind,wenowexaminenewdefinitionsoftheclasses.Ratherthanmanuallycreatingtheclass,wedefinefunctionsthatwillcreatealistrepresentingtheclass,appendaclassnametotheclassattribute,andthenreturnthelist.Therearethreeclasses,andwewilldefineonefunctionforeachclass.ThefirstfunctionisusedtodefineaconstructorforanobjectoftheGeometricTrialclass:

GeometricTrial<-function()

#Createthebasicdatastructure-alistthatkeepstrackof

#asetoftrials.

#Createthebasicmethodsaspartofalisttobereturned.

me=list(

#Definethehistorytokeeptrackofthetrials.

history=character(0)

#Definemyclassidentifierandreturnthelist.

class(me)<-append(class(me),"GeometricTrial")

return(me)

Priortoreturningthelist,theappendfunctionisusedtoaddthenewclassnametotheendofthecurrentclassattribute.ThisideaisusedinclassesthatarederivedfromtheGeometricTrialclassesaswell.TheconstructorfortheDieandCoinclassescannowbedefined,andbothconstructorsexplicitlycalltheconstructorfortheparentclass,performanyactionsassociatedwiththecurrentclass,andthenappendthecurrentclassnametotheclassattribute:

Die<-function()

#Definetheobjectbyfirstcallingtheconstructorforthebaseclass

me<-GeometricTrial()

#Addtheclassnametotheendofthelistofclassnames

class(me)<-append(class(me),"Die")

return(me)

Coin<-function()

#Definetheobjectbycallingtheconstructorforthebaseclass

me<-GeometricTrial()

#Addtheclassnametotheendofthelistofclassnames

class(me)<-append(class(me),"Coin")

return(me)

TheGeometricTrialclassincludesfourmethods.Theresetmethodbehavesexactlylike

theresetmethoddiscussedintheprevioussection.ThegetHistorymethodisanaccessorforadataelementandisdiscussedinthefollowingsection.Wewillnowdiscussthesimulationmethod,andadiscussiononthesingleTrialmethodwillfollow.

Thesimulationmethodisusedtosimulateasingleexperiment.Thehistoryisfirstcleared,andthesingleTrialmethodisrepeatedlycalleduntilasuccessfulresultisreturned.Wefirstdefinethebasesimulationfunction,thedefaultsimulationfunction,andthenthesimulationfunctionusedbytheGeometricTrialclass,asfollows:

simulation<-function(theObject)

UseMethod("simulation",theObject)

simulation.default<-function(theObject)

warning("Defaultsimulationmethodcalledonunrecognizedobject.")

return(theObject)

##Defineamethodtorunasimulationofageometrictrial.

simulation.GeometricTrial=function(theObject)

theObject<-reset(theObject)#Resetthehistory

#beforethetrial.

repeat

##performasingletrialandaddittothehistory

thisTrial<-singleTrial(theObject)

theObject<-appendEvent(theObject,thisTrial$result)

if(thisTrial$success)

break#Thetrialresultedinasuccess.Time

#tostop!

}#Thetrialwasnotasuccess.Keepgoing.

return(theObject)

Theefforttodefineadefaultfunctionmaynotappeartobeaworthwhileendeavor.However,thispracticeisgenerallyemployedtoensurethatthesystemcanresponsiblyreactifthemethodsyoudefinearecalledbymistake.

ThefinalstepistodefinethesingleTrialmethods.Thismethodisexecutedbythechildclasses,DieandCoin.Again,thebaseanddefaultmethodsarecreated.Inthiscase,though,therearealsomethodsforeachofthethreeclasses.ThebasefunctioncallstheUseMethodfunction,whichscrollsthroughtheclassattributeforthefirstfunctiontocall.WeuseamethodfortheGeometricTrialclasstodemonstratetheorderofthecallsaswellastheNextMethodfunction.TheNextMethodfunctioncontinuesthesearchintheclassattributeandwillcallthenextfunctionbasedontheclassnamesthatfollowthecurrentclass:

singleTrial.default=function(theObject)

##Justgenerateadefaultsuccess

warning("UnrecognizedobjectfoundforthesingleTrialmethod")

return(list(result="1",success=TRUE))

singleTrial.GeometricTrial=function(theObject)

NextMethod("singleTrial",theObject)

singleTrial.Coin=function(theObject)

##Performasinglecoinflip

value<-as.character(

cut(as.integer(1+trunc(runif(1,0,2))),c(0,1,2),labels=c("H","T")))

return(list(result=value,success=(value=="H")))

singleTrial.Die=function(theObject)

##Performasingledieroll

value<-as.integer(1+trunc(runif(1,0,6)))

return(list(result=value,success=(value==1)))

WiththesemethodsdefinedandthegetHistorymethoddefinedinthefollowingsection,theclasswillbecomplete.ObjectsoftheCoinandDieclasscanbecreated,andsimulationscanbeexecuted,asfollows:

>coin<-Coin()

>coin<-simulation(coin)

>getHistory(coin)

Levels:H

>coin<-simulation(coin)

>getHistory(coin)

[1]TTH

Levels:HT

>die<-Die()

>die<-simulation(die)

>getHistory(die)

Levels:1

>die<-simulation(die)

>getHistory(die)

[1]655621

Levels:1256

EncapsulationThefinalmethodforthegetHistoryclasswillnowbedefined.Itisdefinedinaseparatesectiontostressanimportantpoint.AnS3objectisgenerallyabasicdatastructure,suchasavectororalistthathasanadditionalclassattributedefined.Thefunctionsthataredefinedfortheclassreacttotheclassattributeinapredictableway.

Onesideeffectisthateveryelementofanobjectfromagivenclassispublicdata.Theelementscontainedwithinanobjectcanalwaysbeaccessed.TheresultisthatwhenprogramminginR,wemusttakeextrastepstomaintaindisciplinewithrespecttoaccessingthedataelementsmaintainedbyanobject.Codethatdirectlyaccessesdataelementswithinanobjectmayworkwhenfirstwritten,butanychangetotheclassconstructorrisksbreakingcodeintheothermethodsdefinedforaclass.

Withrespecttoourpreviousexample,wehaveanaccessor,thegetHistorymethod.Ifwehaveanobject,calledoneDie,fromtheDieclass,wecaneasilygetthehistoryusingoneDie$history.Ifwelaterdecidetochangethedatastructureusedtostorethehistory,thenanycodedirectlyaccessingthisvariableislikelytofail.

Instead,wewriteanaccessormethod,getHistory,whichisdesignedtoreturnavectorthathasthehistoryintheformofavectoroffactors.Itisimportanttomaintaindisciplineandonlyusethismethodtogetacopyofthehistory.Havealookatthefollowingcode:

getHistory<-function(theObject)

UseMethod("getHistory",theObject)

getHistory.default<-function(theObject)

return(factor())#Justreturnanemptyvectoroffactors

getHistory.GeometricTrial<-function(theObject)

return(as.factor(theObject$history))

AfinalnoteThereisonefinalnotetoshareaboutS3classes.IfyouhaveusedR,youmostlikelyhaveusedthem.Manyfunctionsaredefinedtoreactaccordingtotheclassnameoftheirfirstargument.Havealookatthefollowingdiagram:

Acommonexampleofthisistheplotcommand.Ifyoutypetheplotcommandwithoutarguments,youcanseeitsdefinition,asfollows:

function(x,y,...)

UseMethod("plot")

<bytecode:0x32fdd50>

<environment:namespace:graphics>

Theplotcommandwillreactdifferentlydependingonwhatkindofobjectyoupassedtoit.Ifyouwishtoseewhatclassestheplotcommandcanhandle,youcanusethemethodscommandtolistthem:

>methods(plot)

[1]plot.HoltWinters*plot.TukeyHSD*plot.acf*

[4]plot.data.frame*plot.decomposed.ts*plot.default

[7]plot.dendrogram*plot.density*plot.ecdf

[10]plot.factor*plot.formula*plot.function

[13]plot.hclust*plot.histogram*plot.isoreg*

[16]plot.lm*plot.medpolish*plot.mlm*

[19]plot.ppr*plot.prcomp*plot.princomp*

[22]plot.profile.nls*plot.spec*plot.stepfun

[25]plot.stl*plot.table*plot.ts

[28]plot.tskernel*

Non-visiblefunctionsareasterisked

OneofthegreatestadvantagesoftheS3classdefinitionisthatitissimpletobuildonwhatisalreadyavailable.Intheexamplefromtheprevioussection,IwouldliketohavetheplotcommandreactappropriatelyaccordingtowhetherornotIpassitaclassofthetypeDieorCoin.AssumingthatIhavethepreviousclasses,DieandCoin,defined,Imerelyhavetodefinetwonewplotfunctions,asfollows:

>plot.Die<-function(theDie,theTitle)

+plot(theDie$getHistory(),

+xlab="ValueAfterADieRoll",ylab="Frequency",

+main=theTitle)

>plot.Coin<-function(theCoin,theTitle)

+plot(theCoin$getHistory(),

+xlab="ValueAfterCoinFlip",ylab="Frequency",

+main=theTitle)

>plot(aCoin,"ThisHereTrial")

>plot(aDie,"AMoreBetterTrial")

Itiscommontousethisideatoextendanumberofcommands.Somecommonexamplesincludetheprintandtheformatfunctions.

SummaryWehaveexploredhowtocreateS3classes,andwedidsointhecontextoftwoexamples.Thefirstexamplefocusedonhowtodefinefunctionsthatwillreactbasedontheclassnameofthefirstargumentgiventothefunction.Thefirstexampledidnotmakefulluseofbasicobject-orientedprinciples,asitisanattempttosimplyintroducetheideaofS3classes.Thesecondexampleextendedthefirstexampletoprovideasimpleexampleofhowinheritanceisimplemented.ItdemonstratedhowinheritanceisimplementedinthecontextofanS3class.ItalsoprovidedademonstrationofhowencapsulationisimplementedundertheframeworkofanS3class.

Onedownsidetotheapproachisthatthereislittletypechecking.Itispossibletomakechangestoanobjectthatcanmakeitinconsistentwiththeoriginaldefinition.Whenachangeismadetoanobject,nochecksareimplementedtoensurethatanobjecthasthepropertiesthatareexpectedofit.

OnewaytoavoidthisissueistomakeuseofS4classes.TheapproachassociatedwithS4classesisexaminedinthenextchapter.AnotheradvantageisthattheS4approachwilllookmorefamiliartothosealreadyfamiliarwithobject-orientedapproachestoprogramming.

Chapter9.S4ClassesThischapteristhethirdpartinourintroductiontoprogramming.WeexaminedS3classesinthepreviouschapter.WewillnowexamineS4classes.TheapproachassociatedwithS3classesismoreflexible,andtheapproachassociatedwithS4classesisamoreformalandstructureddefinition.

Thischapterisroughlydividedintofourparts:

Classdefinition:Thissectiongivesyouanoverviewofhowaclassisdefinedandhowthedata(slots)associatedwiththeclassarespecifiedClassmethods:ThissectiongivesyouanoverviewofhowmethodsthatareassociatedwithaclassaredefinedInheritance:ThissectiongivesyouanoverviewofhowchildclassesthatbuildonthedefinitionofaparentclasscanbedefinedMiscellaneouscommands:Thissectionexplainsfourcommandsthatcanbeusedtoexploreagivenobjectorclass

IntroducingtheAntclassWedefinedtheideaofcontrolflowstructuresinChapter7,BasicProgramming,andintroducedtheideaofanS3classinChapter8,S3Classes.WewillnowintroducetheideaofS4classes,whichisamoreformalwaytoimplementclassesinR.OneoftheoddquirksofS4classesisthatyoufirstdefinetheclassalongwithitsdata,andthenyoudefinethemethodsseparately.

Asaresultofthisseparationinthewayaclassisdefined,wewillfirstdiscussthegeneralideaofhowtodefineaclassanditsdata.Wewillthendiscusshowtoaddamethodtoanexistingclass.Next,wewilldiscusshowinheritanceisimplemented.Finally,wewillprovideafewnotesaboutotheroptionsthatdonotfitnicelyinthecategoriesmentionedearlier.

Inthepreviouschapter,wetookanexampleandthenmodifiedit.TheapproachassociatedwithanS4classislessflexibleandrequiresabitmoreforethoughtintermsofhowaclassisdefined.Wewilltakeadifferentapproachinthischapterandcreateacompleteclassfromthebeginning.Inthiscase,wewillbuildonanideaproposedbyColeandCheshire.Theauthorsproposedacellularautomatasimulationtomimichowantsmovewithinacolony.

Aspartofasimulation,wewillassumethatweneedanAntclass.Wewilldepartfromthepaperandassumethattheantsarenothomogeneous.Wewillthenassumethattherearemale(drones)andfemaleants,andthefemalescanbeeitherworkersorsoldiers.Wewillneedanantbaseclass,whichisdiscussedinthefirsttwosectionsofthischapterasameanstodemonstratehowtocreateanS4class.Inthethirdsection,wewilldefineahierarchyofclassesbasedontheoriginalAntclass.Thishierarchyincludesmaleandfemaleclasses.Theworkerclasswilltheninheritfromthefemaleclass,andthesoldierclasswillinheritfromtheworkerclass.

DefininganS4classWewilldefinethebaseAntclasscalledAnt.Theclassisrepresentedinthefollowingfigure.Theclassisusedtorepresentthefundamentalaspectsthatweneedtotrackforanant,andwefocusoncreatingtheclassanddata.Themethodsareconstructedinaseparatestepandareexaminedinthenextsection.

AclassiscreatedusingthesetClasscommand.Whencreatingtheclass,wespecifythedatainacharactervectorusingtheslotsargument.Theslotsargumentisavectorofcharacterobjectsandrepresentsthenamesofthedataelements.Theseelementsareoftenreferredtoastheslotswithintheclass.

Someoftheargumentsthatwewilldiscusshereareoptional,butitisagoodpracticetousethem.Inparticular,wewillspecifyasetofdefaultvalues(theprototype)andafunctiontocheckwhetherthedataisconsistent(avalidityfunction).Also,itisagoodpracticetokeepallofthestepsnecessarytocreateaclasswithinthesamefile.Tothatend,weassumethatyouwillnotbeenteringthecommandsfromthecommandline.Theyareallfoundwithinasinglefile,sotheformattingoftheexampleswillreflectthelackoftheRworkspacemarkers.

ThefirststepistodefinetheclassusingthesetClasscommand.Thiscommanddefinesa

newclassbyname,anditalsoreturnsageneratorthatcanbeusedtoconstructanobjectforthenewclass.Thefirstargumentisthenameoftheclassfollowedbythedatatobeincludedintheclass.Wewillalsoincludethedefaultinitialvaluesandthedefinitionofthefunctionusedtoensurethatthedataisconsistent.ThevalidityfunctioncanbesetseparatelyusingthesetValiditycommand.ThedatatypesfortheslotsarecharactervaluesthatmatchthenamesoftheRdatatypeswhichwillbereturnedbytheclasscommand:

#DefinethebaseAntclass.

Ant<-setClass(

#Setthenameoftheclass

"Ant",

#Namethedatatypes(slots)thattheclasswilltrack

slots=c(

Length="numeric",#thelength(size)ofthisant.

Position="numeric",#thepositionofthisant.

#(a3vector!)

pA="numeric",#Probabilitythatanantwill

#transitionfromactiveto

#inactive.

pI="numeric",#Probabilitythatanantwill

#transitionfrominactiveto

#active.

ActivityLevel="numeric"#Theant'scurrentactivity

#level.

#Setthedefaultvaluesfortheslots.(optional)

prototype=list(

Length=4.0,

Position=c(0.0,0.0,0.0),

pA=0.05,

pI=0.1,

ActivityLevel=0.5

#Makeafunctionthatcantesttoseeifthedataisconsistent.

#(optional)

validity=function(object)

#Checktoseeiftheactivitylevelandlengthis

#non-negative.

#Seethediscussiononthe@notationinthetextbelow.

if(object@ActivityLevel<0.0){

return("Error:Theactivitylevelisnegative")

}elseif(object@Length<0.0){

return("Error:Thelengthisnegative")

return(TRUE)

Withthisdefinition,therearetwowaystocreateanAntobject:oneisusingthenewcommandandtheotherisusingtheAntgenerator,whichiscreatedafterthesuccessfulexecutionofthesetClasscommand.Notethatinthefollowingexamples,thedefaultvaluescanbeoverriddenwhenanewobjectiscreated:

>ant1<-new("Ant")

Anobjectofclass"Ant"

Slot"Length":

Slot"Position":

[1]000

Slot"pA":

[1]0.05

Slot"pI":

[1]0.1

Slot"ActivityLevel":

[1]0.5

Wecanspecifythedefaultvalueswhencreatinganewobject.

>ant2<-new("Ant",Length=4.5)

Slot"Length":

[1]4.5

Slot"Position":

[1]000

Slot"pA":

[1]0.05

Slot"pI":

[1]0.1

[1]0.5

TheobjectcanalsobecreatedusingthegeneratorthatisdefinedwhencreatingtheclassusingthesetClasscommand.

>ant3<-Ant(Length=5.0,Position=c(3.0,2.0,1.0))

Slot"Length":

Slot"Position":

[1]321

Slot"pA":

[1]0.05

Slot"pI":

[1]0.1

[1]0.5

>class(ant3)

[1]"Ant"

attr(,"package")

[1]".GlobalEnv"

>getClass(ant3)

Slot"Length":

Slot"Position":

[1]321

Slot"pA":

[1]0.05

Slot"pI":

[1]0.1

[1]0.5

Whentheobjectiscreatedandavalidityfunctionisdefined,thevalidityfunctionwilldeterminewhetherthegiveninitialvaluesareconsistent:

>ant4<-Ant(Length=-1.0,Position=c(3.0,2.0,1.0))

ErrorinvalidObject(.Object):

invalidclass"Ant"object:Error:Thelengthisnegative

Error:object'ant4'notfound

Inthelaststeps,theattemptedcreationofant4,anerrormessageisdisplayed.Thenewvariable,ant4,wasnotcreated.Ifyouwishtotestwhethertheobjectwascreated,youmustbecarefultoensurethatthevariablenameuseddoesnotexistpriortotheattemptedcreationofthenewobject.Also,thevalidityfunctionisonlyexecutedwhenarequesttocreateanewobjectismade.Ifyouchangethevaluesofthedatalater,thevalidityfunctionisnotcalled.

Beforewemoveontodiscussmethods,weneedtofigureouthowtogetaccesstothedatawithinanobject.Thesyntaxisdifferentfromotherdatastructures,andweuse@toindicatethatwewanttoaccessanelementfromwithintheobject.Thiscanbeusedtogetacopyofthevalueortosetthevalueofanelement:

>adomAnt<-Ant(Length=5.0,Position=c(-1.0,2.0,1.0))

>adomAnt@Length

>adomAnt@Position

[1]-121

>adomAnt@ActivityLevel=-5.0

>adomAnt@ActivityLevel

Notethatintheprecedingexample,wesetavaluefortheactivitylevelthatisnotallowedaccordingtothevalidityfunction.Sinceitwassetaftertheobjectwascreated,nocheckisperformed.ThevalidityfunctionisonlyexecutedduringthecreationoftheobjectorifthevalidObjectfunctioniscalled.

Onefinalnote:itisgenerallyabadformtoworkdirectlywithanelementwithinanobject,andabetterpracticeistocreatemethodsthatobtainorchangeanindividualelementwithinanobject.Itisabestpracticetobecarefulabouttheencapsulationofanobject’sslots.TheRenvironmentdoesnotrecognizetheideaofprivateversuspublicdata,andtheonusisontheprogrammertomaintaindisciplinewithrespecttothisimportantprinciple.

DefiningmethodsforanS4classWhenanewclassisdefined,thedataelementsaredefined,butthemethodsassociatedwiththeclassaredefinedonaseparatestage.MethodsareimplementedinamannersimilartotheoneusedforS3classes.Afunctionisdefined,andthewaythefunctionreactsdependsonitsarguments.Ifamethodisusedtochangeoneofthedatacomponentsofanobject,thenitmustreturnacopyoftheobject,justaswesawwithS3classes.

Thecreationofnewmethodsisdiscussedintwosteps.Wewillfirstdiscusshowtodefineamethodforaclasswherethemethoddoesnotyetexist.Next,wewilldiscusssomepredefinedmethodsthatareavailableandhowtoextendthemtoaccommodateanewclass.

DefiningnewmethodsThefirststeptocreateanewmethodistoreservethename.Somefunctionsareincludedbydefault,suchastheinitialize,printorshowcommands,andwewilllaterseehowtoextendthem.Toreserveanewname,youmustfirstusethesetGenericcommand.Attheveryleast,youneedtogivethiscommandthenameofthefunctionasacharacterstring.Asintheprevioussection,wewillusemoreoptionsasanattempttopracticesafeprogramming.

Themethodstobecreatedareshowninprecedingfigure.Thereareanumberofmethods,butwewillonlydefinefourhere.Allofthemethodsareaccessors;theyareusedtoeithergetorsetvaluesofthedatacomponents.Wewillonlydefinethemethodsassociatedwiththelengthslotinthistext,andyoucanseetherestofthecodeintheexamplesavailableonthewebsite.Theothermethodscloselyfollowthecodeusedforthelengthslot.Therearetwomethodstosettheactivitylevel,andthosecodesareexaminedseparatelytoprovideanexampleofhowamethodcanbeoverloaded.

First,wewilldefinethemethodstogetandsetthelength.Wewillfirstcreatethemethodtogetthelength,asitisalittlemorestraightforward.ThefirststepistotellRthatanewfunctionwillbedefined,andthenameisreservedusingthesetGenericcommand.ThemethodthatiscalledwhenanAntobjectispassedtothecommandisdefinedusingthesetMethodcommand:

setGeneric(name="GetLength",

def=function(antie)

standardGeneric("GetLength")

setMethod(f="GetLength",

signature="Ant",

definition=function(antie)

return(antie@Length)

NowthattheGetLengthfunctionisdefined,itcanbeusedtogetthelengthcomponentforanAntobject:

>GetLength(ant2)

[1]4.5

Themethodtosetthelengthissimilar,butthereisonedifference.Themethodmustreturnacopyoftheobjectpassedtoit,anditrequiresanadditionalargument:

setGeneric(name="SetLength",

def=function(antie,newLength)

standardGeneric("SetLength")

setMethod(f="SetLength",

signature="Ant",

definition=function(antie,newLength)

if(newLength>0.0){

antie@Length=newLength

}else{

warning("Error-invalidlengthpassed");

return(antie)

Whensettingthelength,thenewobjectmustbesetusingtheobjectthatispassedbackfromthefunction:

>ant2@Length

[1]4.5

>ant2<-SetLength(ant2,6.25)

>ant2@Length

[1]6.25

PolymorphismThedefinitionofS4classesallowsmethodstobeoverloaded.Thatis,multiplefunctionsthathavethesamenamecanbedefined,andthefunctionthatisexecutedisdeterminedbythearguments’types.WewillnowexaminethisideainthecontextofdefiningthemethodsusedtosettheactivitylevelintheAntclass.

Twoormorefunctionscanhavethesamename,butthetypesoftheargumentspassedtothemdiffer.Therearetwomethodstosettheactivitylevel.Onetakesafloatingpointnumberandsetstheactivitylevelbasedtothevaluepassedtoit.TheothertakesalogicalvalueandsetstheactivityleveltozeroiftheargumentisFALSE;otherwise,itsetsittoadefaultvalue.

TheideaistousethesignatureoptioninthesetMethodcommand.Itissettoavectorofclassnames,andtheorderoftheclassnamesisusedtodeterminewhichfunctionshouldbecalledforagivensetofarguments.Animportantthingtonote,though,isthattheprototypedefinedinthesetGenericcommanddefinesthenamesofthearguments,andtheargumentnamesinbothmethodsmustbeexactlythesameandinthesameorder:

setGeneric(name="SetActivityLevel",

def=function(antie,activity)

standardGeneric("SetActivityLevel")

setMethod(f="SetActivityLevel",

signature=c("Ant","logical"),

definition=function(antie,activity)

if(activity){

antie@ActivityLevel=0.1

}else{

antie@ActivityLevel=0.0

return(antie)

setMethod(f="SetActivityLevel",

signature=c("Ant","numeric"),

definition=function(antie,activity)

if(activity>=0.0){

antie@ActivityLevel=activity

}else{

warning("Theactivitylevelcannotbenegative")

return(antie)

Oncethetwomethodsaredefined,Rwillusetheclassnamesoftheargumentsto

determinewhichfunctiontocallinagivencontext:

>ant2<-SetActivityLevel(ant2,0.1)

>ant2@ActivityLevel

[1]0.1

>ant2<-SetActivityLevel(ant2,FALSE)

>ant2@ActivityLevel

Therearetwoadditionaldatatypesrecognizedbythesignatureoption:ANYandmissing.Thesecanbeusedtomatchanydatatypeoramissingvalue.Alsonotethatwehaveleftouttheuseofellipses(…)fortheargumentsintheprecedingexamples.The…argumentmustbethelastargumentandisusedtoindicatethatanyremainingparametersarepassedastheyappearintheoriginalcalltothefunction.Ellipsescanmaketheuseoftheoverloadedfunctionsinamoreflexiblewaythanindicated.Moreinformationcanbefoundusingthehelp(dotsMethods)command.

ExtendingtheexistingmethodsThereareanumberofgenericfunctionsdefinedinabasicRsession,andwewillexaminehowtoextendanexistingfunction.Forexample,theshowcommandisagenericfunctionwhosebehaviordependsontheclassnameoftheobjectpassedtoit.Sincethefunctionnameisalreadyreserved,thesetGenericcommandisnotusedtoreservethefunctionname.

Theshowcommandisastandardexample.Thecommandtakesanobjectandconvertsittoacharactervaluetobedisplayed.Thecommanddefineshowothercommandsprintoutandexpressanobject.Intheprecedingexample,anewclasscalledcoordinateisdefined;thiskeepstrackoftwovalues,xandy,foracoordinate,andwewilladdonemethodtosetthevaluesofthecoordinate:

#Definethebasecoordinatesclass.

Coordinate<-setClass(

"Coordinate",

slots=c(

x="numeric",#thexposition

y="numeric"#theyposition

prototype=list(

x=0.0,

#(optional)

#Thisisnotcalledifyouhaveaninitializefunctiondefined!

#Checktoseeifthecoordinateisoutsideofacircleof

#radius100

print("Checkingthevalidityofthepoint")

if(object@x*object@x+object@y*object@y>100.0*100.0){

return(paste("Error:Thepointistoofar",

"awayfromtheorigin."))

return(TRUE)

#Addamethodtosetthevalueofacoordinate

setGeneric(name="SetPoint",

def=function(coord,x,y)

standardGeneric("SetPoint")

setMethod(f="SetPoint",

signature="Coordinate",

def=function(coord,x,y)

print("Settingthepoint")

coord@x=x

coord@y=y

return(coord)

Wewillnowextendtheshowmethodsothatitcanproperlyreacttoacoordinateobject.Asitisreserved,wedonothavetousethesetGenericcommandbutcansimplydefineit:

setMethod(f="show",

def=function(object)

cat("ThecoordinateisX:",object@x,"Y:",object@y,"\n")

Asnotedpreviously,thesignatureoptionmustmatchtheoriginaldefinitionofafunctionthatyouwishtoextend.YoucanusethegetMethod('show')commandtoexaminethesignatureforthefunction.Withthenewmethodinplace,theshowcommandisusedtoconvertacoordinateobjecttoastringwhenitisprinted:

>point<-Coordinate(x=1,y=5)

[1]"Checkingthevalidityofthepoint"

>print(point)

ThecoordinateisX:1Y:5

>point

Anotherimportpredefinedmethodistheinitializecommand.Iftheinitializecommandiscreatedforaclass,thenitiscalledwhenanewobjectiscreated.Thatis,youcandefineaninitializefunctiontoactasaconstructor.Ifaninitializefunctionisdefinedforaclass,thevalidatorisnotcalled.YouhavetomanuallycallthevalidatorusingthevalidObjectcommand.Alsonotethattheprototypefortheinitializecommandrequiresthenameofthefirstargumenttobeanobject,andthedefaultvaluesaregivenfortheremainingargumentsincaseanewobjectiscreatedwithoutspecifyinganyvaluesfortheslots:

setMethod(f="initialize",

def=function(.Object,x=0.0,y=0.0)

print("Checkingthepoint")

.Object=SetPoint(.Object,x,y)

validObject(.Object)#youmustexplicitlycallthe

#inspector

return(.Object)

Now,whenyoucreateanewobject,thenewinitializefunctioniscalledimmediately:

>point<-Coordinate(x=2,y=3)

[1]"Checkingthepoint"

[1]"Settingthepoint"

[1]"Checkingthevalidityofthepoint"

>point

Usingtheinitializeandvalidityfunctionstogethercanresultinsurprisingcodepaths.Thisisespeciallytruewheninheritingfromoneclassandcallingtheinitializefunctionofaparentclassfromthechildclass.Itisimportanttotestcodestoensurethatthecodeisexecutingintheorderthatyouexpect.Personally,Itrytouseeithervalidatororconstructor,butnotboth.

InheritanceTheAntclassdiscussedinthefirstsectionofthischapterprovidedanexampleofhowtodefineaclassandthendefinethemethodsassociatedwiththeclass.Wewillnowextendtheclassbycreatingnewclassesthatinheritfromthebaseclass.TheoriginalAntclassisshownintheprecedingfigure,andnow,wewillproposefourclassesthatinheritfromthebaseclass.TwonewclassesthatinheritfromAntaretheMaleandFemaleclasses.TheWorkerclassinheritsfromtheFemaleclass,whiletheSoldierclassinheritsfromtheWorkerclass.Therelationshipsareshowninthefollowingfigure.Thecodeforallofthenewclassesisincludedinourexamplecodesavailableatourwebsite,butwewillonlyfocusontwoofthenewclassesinthetexttokeepourdiscussionmorefocused.

RelationshipsbetweentheclassesthatinheritfromthebaseAntclass

Whenanewclassiscreated,itcaninheritfromanexistingclassbysettingthecontainsparameter.Thiscanbesettoavectorofclassesformultipleinheritance.However,wewillfocusonsingleinheritanceheretoavoiddiscussingthecomplicationsassociatedwithdetermininghowRfindsamethodwhentherearecollisions.AssumingthattheAntbaseclassgiveninthefirstsectionhasalreadybeendefinedinthecurrentsession,thechildclassescanbedefined.Thedetailsforthetwoclasses,FemaleandWorker,arediscussedhere.

First,theFemaleAntclassisdefined.Itaddsanewslot,Food,andinheritsfromtheAntclass.BeforedefiningtheFemaleAntclass,weaddacaveatabouttheAntclass.ThebaseAntclassshouldhavebeenavirtualclass.WewouldnotordinarilycreateanobjectoftheAntclass.Wedidnotmakeitavirtualclassinordertosimplifyourintroduction.Wearewisernowandwishtodemonstratehowtodefineavirtualclass.TheFemaleAntclasswillbeavirtualclasstodemonstratetheidea.WewillmakeitavirtualclassbyincludingtheVIRTUALcharacterstringinthecontainsparameter,anditwillnotbepossibletocreateanobjectoftheFemaleAntclass:

#Definethefemaleantclass.

FemaleAnt<-setClass(

"FemaleAnt",

slots=c(

Food="numeric"#Thenumberoffoodunitscarried

prototype=list(

Food=0

#(optional)

print("Validity:FemaleAnt")

#Checktoseeifthenumberofoffspringisnon-negative.

if(object@Food<0){

return("Error:Thenumberoffoodunitsisnegative")

return(TRUE)

#ThisclassinheritsfromtheAntclass

contains=c("Ant","VIRTUAL")

Now,wewilldefineaWorkerAntclassthatinheritsfromtheFemaleAntclass:

#Definetheworkerantclass.

WorkerAnt<-setClass(

"WorkerAnt",

slots=c(

Foraging="logical",#Whetherornottheantisactively

#lookingforfood

Alarm="logical"#Whetherornottheantisactively

#announcinganalarm.

prototype=list(

Foraging=FALSE,

Alarm=FALSE

#(optional)

print("Validity:WorkerAnt")

return(TRUE)

#ThisclassinheritsfromtheFemaleAntclass

contains="FemaleAnt"

Whenanewworkeriscreated,itinheritsfromtheFemaleAntclass:

>worker<-WorkerAnt(Position=c(-1,3,5),Length=2.5)

>worker

Anobjectofclass"WorkerAnt"

Slot"Foraging":

[1]FALSE

Slot"Alarm":

[1]FALSE

Slot"Food":

Slot"Length":

[1]2.5

Slot"Position":

[1]-135

Slot"pA":

[1]0.05

Slot"pI":

[1]0.1

[1]0.5

>worker<-SetLength(worker,3.5)

>GetLength(worker)

[1]3.5

Wehavenotdefinedtherelevantmethodsintheprecedingexamples.Thecodeisavailableinoursetofexamples,andwewillnotdiscussmostofittokeepthisdiscussionmorefocused.Wewillexaminetheinitializemethod,though.ThereasontodosoistoexplorethecallNextMethodcommand.ThecallNextMethodcommandisusedtorequestthatRsearchesforandexecutesamethodofthesamenamethatisamemberofaparentclass.

Wechosetheinitializemethodbecauseacommontaskistobuildachainofconstructorsthatinitializethedataassociatedfortheclassassociatedwitheachconstructor.WehavenotyetcreatedanyoftheinitializemethodsandstartwiththebaseAntclass:

signature="Ant",

def=function(.Object,Length=4,Position=c(0.0,0.0,0.0))

print("Antinitialize")

.Object=SetLength(.Object,Length)

.Object=SetPosition(.Object,Position)

#validObject(.Object)#youmustexplicitlycallthe

#inspector

return(.Object)

Theconstructortakesthreearguments:theobjectitself(.Object),thelength,andthepositionoftheant,anddefaultvaluesaregivenincasenoneareprovidedwhenanewobjectiscreated.ThevalidObjectcommandiscommentedout.Youshouldtryuncommentingthelineandcreatenewobjectstoseewhetherthevalidatorcaninturncalltheinitializemethod.Anotherimportantfeatureisthattheinitializemethodreturnsacopyoftheobject.

TheinitializecommandiscreatedfortheFemaleAntclass,andtheargumentstotheinitializecommandshouldberespectedwhentherequesttocallNextMethodforthenextfunctionismade:

signature="FemaleAnt",

print("FemaleAntinitialize")

.Object<-callNextMethod(.Object,Length,Position)

inspector

return(.Object)

ThecallNextMethodcommandisusedtocalltheinitializemethodassociatedwiththeAntclass.TheargumentsarearrangedtomatchthedefinitionoftheAntclass,anditreturnsanewcopyofthecurrentobject.

Finally,theinitializefunctionfortheWorkerAntclassiscreated.ItalsomakesuseofcallNextMethodtoensurethatthemethodofthesamenameassociatedwiththeparentclassisalsocalled:

signature="WorkerAnt",

print("WorkerAntinitialize")

.Object<-callNextMethod(.Object,Length,Position)

#inspector

return(.Object)

Now,whenanewobjectoftheWorkerAntclassiscreated,theinitializemethodassociatedwiththeWorkerAntclassiscalled,andeachassociatedmethodforeachparentclassiscalledinturn:

>worker<-WorkerAnt(Position=c(-1,3,5),Length=2.5)

[1]"WorkerAntinitialize"

[1]"FemaleAntinitialize"

[1]"Antinitialize"

MiscellaneousnotesIntheprevioussections,wediscussedhowtocreateanewclassaswellashowtodefineahierarchyofclasses.Wewillnowdiscussfourcommandsthatarehelpfulwhenworkingwithclasses:theslotNames,getSlots,getClass,andslotcommands.Eachcommandisbrieflydiscussedinturn,anditisassumedthattheAnt,FemaleAnt,andWorkerAntclassesthataregivenintheprevioussectionaredefinedinthecurrentworkspace.

Thefirstcommand,theslotnamescommand,isusedtolistthedatacomponentsofanobjectofsomeclass.Itreturnsthenamesofeachcomponentasavectorofcharacters:

>worker<-WorkerAnt(Position=c(1,2,3),Length=5.6)

>slotNames(worker)

[1]"Foraging""Alarm""Food""Length"

[5]"Position""pA""pI""ActivityLevel"

ThegetSlotscommandissimilartotheslotNamescommand.Thedifferenceisthattheargumentisacharactervariablewhichisthenameoftheclassyouwanttoinvestigate:

>getSlots("WorkerAnt")

ForagingAlarmFoodLengthPosition

"logical""logical""numeric""numeric""numeric"

pApIActivityLevel

"numeric""numeric""numeric"

ThegetClasscommandhastwoforms.Iftheargumentisanobject,thecommandwillprintoutthedetailsfortheobject.Iftheargumentisacharacterstring,thenitwillprintoutthedetailsfortheclasswhosenameisthesameastheargument:

>getClass(worker)

Anobjectofclass"WorkerAnt"

Slot"Foraging":

[1]FALSE

Slot"Alarm":

[1]FALSE

Slot"Food":

Slot"Length":

[1]5.6

Slot"Position":

[1]123

Slot"pA":

[1]0.05

Slot"pI":

[1]0.1

[1]0.5

>getClass("WorkerAnt")

Class"WorkerAnt"[in".GlobalEnv"]

Slots:

Name:ForagingAlarmFoodLength

Position

Class:logicallogicalnumericnumeric

numeric

Name:pApIActivityLevel

Class:numericnumericnumeric

Extends:

Class"FemaleAnt",directly

Class"Ant",byclass"FemaleAnt",distance2

KnownSubclasses:"SoldierAnt"

Finally,wewillexaminetheslotcommand.Theslotcommandisusedtoretrievethevalueofaslotforagivenobjectbasedonthenameoftheslot:

>slot(worker,"Position")

[1]123

SummaryWeintroducedtheideaofanS4classandprovidedseveralexamples.TheS4classisconstructedinatleasttwostages.Thefirststageistodefinethenameoftheclassandtheassociateddatacomponents.Themethodsassociatedwiththeclassarethendefinedinaseparatestep.

Inadditiontodefiningaclassanditsmethod,theideaofinheritancewasexplored.Apartialexamplewasgiveninthischapter;itbuiltonabaseclassdefinedinthefirstsectionofthechapter.Additionally,themethodtocall-associatedmethodsinparentclasseswasalsoexplored,andtheexamplemadeuseoftheconstructor(orinitializemethod)todemonstratehowtobuildachainofconstructors.

Finally,fourusefulcommandswereexplained.Thefourcommandsoffereddifferentwaystogetinformationaboutaclassoraboutanobjectofagivenclass.

Formoreinformation,youcanrefertoMobileCellularAutomataModelsofAntBehavior:MovementActivityofLeptothoraxallardycei,BlaineJ.ColeandDavidCheshire,TheAmericanNaturalist.

Chapter10.CaseStudy–CourseGradesThischapterisourfirstcasestudy.Webringtogethertheideasfromthepreviouschaptersandprovideanextendedexample.Somenewideasareintroduced,andtheyshouldmakemoresenseinthecontextofafullexample.

Thischapterisroughlydividedintofourparts:

TheCourseclass:ThissectiongivesyouanoverviewoftheS4classthatwillcontainalistofgradesforacourse.Thegradesarekeptinalistwitheachgradedtaskasaseparateobject.Theassignmentclasses:ThissectiongivesyouanoverviewoftheS4classesusedtokeepthegradesforaspecificgradedtask.Thisclasshastwoderivedclasses.Onederivedclassistokeeptrackofgradesthathaveanumericscore,andtheotheristokeeptrackofgradesthatconsistofletters.Extendingexistingfunctions:Thissectionincludesabriefdiscussionofhowexistingfunctionscanbeextendedtoreactinanappropriatewaytoanobjectthatisoneoftheassignmentclasses.Wefocusonthesummary,plot,andshowcommands.Extendingoperations:Thissectionincludesadiscussiononhowtoextendarithmeticandaccessfunctionsbybuildingonexistingmethods.

OverviewAllthepreviouschaptersfocusonspecifictopics,andherewebringtogetheranumberofdifferenttopicstoexamineanextendedexample.AlloftheclassesexaminedhereareS4classes.TheseclassesareusedtoreadinaCSVfilethatcontainsthegradesforstudentsinaclass.Thereisoneclassthatdefineshowtoreadagradefileandhowtointerpretacolumn.Anothersetofclassesisdefinedandtheclassesareusedtotracktheinformationforasingleassignment.Anobjectofthisclasswillincludeallthescoresfortheassignmentsofallstudents.

WefirstdiscussthenewCourseclass,whichisusedtokeeptrackofalltheassignments.Next,wediscusstheassignmentclassesthatareusedtokeeptrackofthegradesforasingleassignment.Oncetheclassesaredefined,thedetailsonextendingthesummary,plot,andshowcommandsaregiventodemonstratehowcommoncommandscanbeextendedtoreactwhenpassedanewlydefinedobject.Finally,somebasicarithmeticandaccessoroperatorsareredefinedsothattheRsystemwillreactinanappropriatemannerwhenusingfamiliaroperations,suchasadditionormultiplication.

TheCourseclassincludesobjectswhosetypeisoneoftheassignmentclasses,anditseemsmorenaturaltodefinetheassignmentclassesfirst.Theactionsoftheassignmentclassesaremoreintricateandincludemoredetails,whiletheCourseclassisrelativelystraightforward.Tokeeptheintroductiontotheclassesmoregentle,we’llfirstdiscusstheCourseclass.

TheCourseclassTheCourseclassisusedtokeeptrackofallofthegradesforacourse.Thisclasswillreadintheinformationfromafile,decidewhethergradesarenumericgradesorlettergrades,andcreateanappropriateassignmentobjecttoholdthegrades.Thedetailsfortheclassareshowninthefollowingfigure.We’llfirstdiscussthedataandthenthemethodsassociatedwiththeclass.We’llthengivesomedetailsonhowtodefinetheclass.Mostofthecodeassociatedwiththeaccessorsisomittedforthesakeofbrevity,butthefullcodeisavailableonthewebsiteassociatedwiththisbook.

First,therearethreedatastructures(slots)associatedwiththeclass.ThefirstisthenameoftheCSVfilethatcontainsallofthegradesfortheclass.Thesecondisavectorofprefixesusedtodeterminewhatkindofgradedtasksareinthefile.Thelastisalistthatcontainsalloftheassignments.

Therearesevenmethods.Allbutoneareusedtosetandgetthevaluesforthethreeslots.Theaccessorroutinestogetandsetthefilenamearegivenhere,andtheothersarenotincludedinthetexthere.Thelastroutineisusedtoreadthegrades,anditisacomplexmethodthatreadsthedatafromthefileandthendetermineswhethertheassignmenthasnumericalorlettergrades.Itthendefinesanappropriateassignmentobject.

ThedefinitionoftheCourseclassTheCourseclassdefinitionisgivenandthentheaccessorsfortheslotsarestated.ItisanS4class.Inthiscase,wedonotprovideanycheckstoensurethattheinformationintheslotsisconsistenttoreducethecomplexityoftheexample:

###############################################

#CreatetheCourseclasstokeeptrackofallgrades

Course<-setClass(

#Setthenamefortheclass

"Course",

#Definetheslots

slots=c(

GradesFile="character",

GradeTypes="character",

Grades="list"

prototype=list(

GradesFile="",

GradeTypes=c("test","hw","quiz","project"),

Grades=list()

return(TRUE)

Eachoftheslotsincludesasetofaccessorfunctionstoassistinretrievingorsettinginformationtrackedbytheclass.Weonlyprovidethedetailsforonesetofaccessors.Themethodstosetandgetthefilenamearegiven.NeitherofthesemethodsexistinthedefaultRenvironmentsotheymustbecreatedfirst,asfollows:

#DefinethemethodsusedtoretrieveorsetthevalueswithinaCourse

object.

setGeneric(name="GetFileName",

def=function(course)

standardGeneric("GetFileName")

setMethod(f="GetFileName",

signature="Course",

definition=function(course)

return(course@GradesFile)

setGeneric(name="SetFileName",

def=function(course,fileName)

standardGeneric("SetFileName")

setMethod(f="SetFileName",

signature="Course",

definition=function(course,fileName)

course@GradesFile=fileName

return(course)

ReadinggradesfromafileThelastmethodexaminedisthemethodtoreadthedatafromagivenfile.WewillexaminetheGradeslistwithintheCourseclassbeforeprovidingalisting.Thisslotisalist,andeachelementwithinthelistisanassignmentobject.(Theassignmentclassesarediscussedinthenextsectionofthischapter.)ThenameoftheobjectwithinthelististhesameasthenamefoundinthefirstrowoftheCSVfile.

TheReadGradesmethodfirstreadsthecsvfile.Itthengoesthroughthecolumnsthatwerereadfromthefile,andthenamesofthecolumnsareassumedtobeinthefirstrowofthefile.Ifthefirstlettersinthenameofacolumnmatchoneofthestringsthatareinthevector(foundintheGradeTypesslot),thenitisassumedthatthecolumnrepresentsagradeditem.Therearetwokindsofassignments:numericalgradesorlettergrades.Ifacolumnfromthedatafileisdeterminedtobeagradeditem,thenitstypeischecked.Ifthetypeisanumerictype,thenitisassumedthatthecoursetypeisforanumericgrade(anobjectfromtheNumericGradeclass);otherwise,itisassumedtobealettergrade(anobjectfromtheLetterGradeclass):

setGeneric(name="ReadGrades",

def=function(course)

standardGeneric("ReadGrades")

setMethod(f="ReadGrades",

signature="Course",

definition=function(course)

grades<-read.csv(GetFileName(course))

convertedGrades<-list()

courseTypes<-GetGradeTypes(course)

for(gradeIteminnames(grades))

#Gothrougheachcolumnfromthefile.

for(typeincourseTypes)

#gothrougheachcoursetypeanddetermineif

#thiscolumnisaquiz/test/hw/?

if(length(grep(type,gradeItem))>0)

#Theprefixforthenamematchesoneof

#thepredefinedtypes.

if((class(grades[[gradeItem]])=="numeric")||

(class(grades[[gradeItem]])=="integer")){

thisItem<-NumericGrade()

#print(paste("Thisitem,",gradeItem,

#",isanumericgradeitem.",

#class(thisItem)))

}else{

thisItem<-LetterGrade()

#print(paste("Thisitem,",gradeItem,

#",isalettergrade.",

#class(thisItem)))

#Convertthevaluesintotheir

#respectivegrades.

thisItem<-SetValue(thisItem,

grades[[gradeItem]])

#print(paste("class:",class(thisItem)))

convertedGrades[gradeItem]<-thisItem

return(SetGrades(course,convertedGrades))

TheCourseclassisusedtoorganizethegradesforawholeclass.Thescoresforanindividualassignmentarekeptinoneoftheassignmentclasses,whichisexaminedinthefollowingsection.

TheassignmentclassesTherearetwoassignmentclasses,andtheyarebothderivedfromtheassignmentclass.ThefirstclassistheNumericGradeclass,whichkeepstrackofanumericgrade.ThesecondclassistheLetterGradeclass,whichkeepstrackofalettergrade.Thedetailsoftheclassesareshowninthenextfigure.ThedefinitionoftheAssignmentclassisgiveninthefollowingfigure,andthedetailsabouttheaccessorsareomittedforthesakeofbrevity.Thecompletecodeisavailableonthewebsiteforthisbook.

ThedetailsoftheNumericGradeandLetterGradeclassesaregiveninseparatesubsectionsinthischapter.Oncetheclassesaregiven,examplesareprovidedtodemonstratehowtousetheCourseclasstoreadthegradesfromafileandcreatethenecessaryassignmentobjects.

TheassignmentclassisthebaseclassfortheNumericGradeandLetterGradeclasses.Theclassonlyhastwoslots:thenameandanumber.Thenameisusedtodisplaytheinformationrelatedtotheassignment,andthenumbercanfurtheridentifythesetofgrades.Forexample,thegradesfortest3fromaclassmighthaveaname,Test,andnumbersettothevalueof3.

Thedefinitionfortheclassisgivenhere:

###############################################

#Createthebaseassignmentclass

#Thisisusedtorepresentthegradesforoneassignment.

Assignment<-setClass(

#Setthenamefortheclass

"Assignment",

#Definetheslots

slots=c(

Name="character",

Number="numeric"

prototype=list(

Name="Test",

Number=as.integer(1)

if(object@Number<0){

object@Number<-0

warning(paste("Anegativenumberfortheassignment",

"numberispassed.Itissettozero.")

return(TRUE)

TheprimarypurposeoftheAssignmentclassistoactasthebaseclassforothertypesofassignments.Thetwoclasses,NumericGradeandLetterGrade,thatinheritfromtheassignmentclassarediscussedinthefollowingsections.

TheNumericGradeclassThefirstclassweexaminethatinheritsfromtheAssignmentclassistheNumericGradeclass.Thisclassisusedtoretainthegradesforanassignmentwhosegradesarenumbers.Theclasshasonlyoneslot,anumericvectorofgrades.Thedefinitionisgivenhere:

##############################################

#Createtheclasstokeeptrackofthegradesthatarenumericin

#nature.

#Thisisusedtorepresentthenumericgradesforoneassignment.

NumericGrade<-setClass(

#Setthenamefortheclasswithanumericgradeassociatedwith

"NumericGrade",

#Definetheslots

slots=c(

Value="numeric"

prototype=list(

Value=0.0

#(optional)

if(object@Value<0){

object@Value<-0

warning(paste("Anegativenumberfortheassignmentvalue",

"ispassed.Itissettozero."))

return(TRUE)

#ThisclassinheritsfromtheAssignmentclass

contains="Assignment"

Themethodsfortheclassincludetheroutinestosetandgetthevaluesofthegradesaswellasamethodtoprintoutareport.Themethodtogetthegradesisgivenhereasanexample:

#CreatethemethodstoretrieveandsetthevaluesoftheNumericGrade

class.

setGeneric(name="GetValue",

def=function(assignment)

standardGeneric("GetValue")

setMethod(f="GetValue",

signature="NumericGrade",

definition=function(assignment)

return(assignment@Value)

Thereisoneothermethodtobedefined,anditisusedtoprintoutareportbasedonthescores.Themethodforthegradereportprintsoutafive-pointsummaryofthegrades,thefrequencyofthegradesdividedintotenpercentintervals(90-100percent,80-90percent,70-80percent,andsoon),astem-leafplotofthegrades,andasortedlistofthegrades.Thereportmethodincludesacalltothesummarycommandtogetthefive-pointsummaryforthegrades.Thesummaryfunctionisoverridden,andthedetailsaregiveninalatersection,Redefiningexistingfunctions.

Thedefinitionofthereportmethodisgivenhere:

setGeneric(name="GradeReport",

def=function(assignment,maxGrade=NA,div=10)

standardGeneric("GradeReport")

setMethod(f="GradeReport",

signature="Assignment",

definition=function(assignment,maxGrade=NA,div=10)

print(noquote(paste("Gradereportfor",

GetName(assignment))))

print(noquote(''))

print(summary(assignment))#Printoutafive

#pointsummaryforthedata

values<-GetValue(assignment)#Gettherawscores.

if(is.na(maxGrade)){

#ThemaxGradewasnotset.Assumethemaxscore

#fromthedataisthemaximumpossible.

maxGrade<-max(values)

warning(paste("Themaxgadeisnotset,anditis",

"assumedtobe",maxGrade))

skip<-maxGrade*div/100;#Setthe

widthoftheintervals.

#Movethemaxgradeuptomakesurethattheleftsided

#cutwillhaveanintervaltocontainthetopscores.

while(maxGrade<=max(values))

maxGrade=maxGrade+skip

#Determinethenumberofintervals.

numLower<-ceiling((maxGrade-min(values))/skip)

#Determineallofthecutoffpoints

bins=c(seq(maxGrade-numLower*skip,

max(c(values,maxGrade)),by=skip))

#Convertthedataintofactors

levs<-cut(values,breaks=bins,right=FALSE)

#Determinethefrequenciesforthedifferentlevels.

gradeFreqs<-table(levs)

print(noquote(''))

print(noquote("StemLeafplotofgrades:"))

print(stem(values))

print(noquote(''))

print(noquote("GradeFrequencies:"))

print(gradeFreqs)

print(noquote(''))

print(noquote("SortedGrades:"))

print(sort(values))

TheLetterGradeclassThesecondclassthatinheritsfromtheAssignmentclassistheLetterGradeclass.Thisclassisusedtokeeptrackofgradesthatareassignedaslettergrades.ItissimilartotheNumericGradeclass,exceptithastwoslots.Thefirstslotisacharactervectorwiththegrades.Thesecondslotisalistthatcontainsthepossiblelettergradesasthename,andthevalueassociatedwitheachlettergradeisitsnumericvalueusedforcalculations.

ThedefinitionoftheLetterGradeclassisgivenhere:

#############################################################

#Createtheclasstokeeptrackofthegradesthatarelettersin

#nature.

#Thisisusedtorepresentthelettergradesforoneassignment.

LetterGrade<-setClass(

#Setthenamefortheclasswithanumericgradeassociated

#withit.

"LetterGrade",

#Definetheslots

slots=c(

Value="character",

Scale="list"

prototype=list(

Value="F",

Scale=list(

'A+'=98,'A'=95,'A-'=92,

'B+'=88,'B'=85,'B-'=83,

'C+'=78,'C'=75,'C-'=73,

'D+'=68,'D'=65,'D-'=63,

'F+'=58,'F'=55,'F-'=53,

"NA"=0)

#Makeafunctionthatcantesttoseeifthedatais#consistent.

#(optional)

#Thisisnotcalledifyouhaveaninitializefunction#defined!

pos<-grep(paste(object@Value,"$",sep=""),

names(object@Scale))

if(length(pos)!=1){

object@Value<-'F-'

warning("Thegradeisnotrecognized.")

return(TRUE)

#ThisclassinheritsfromtheAssignmentclass

contains="Assignment"

Theclasshastheusualaccessormethodstogetandsetthevaluesoftheslots.Wegivethemethodtosetthevalueofthegradesbecauseitisacomplexexercise.Themethodmustconverteachgradetoacharacterbecausetheymaybepassedasafactor.Themethodmustalsogothroughandmakesurethateachgradeistheonethatisalreadydefined.ItdoesthisusingthegrepcommandandthenamesofthelistintheScalesslot,asfollows:

setMethod(f="SetValue",

signature="LetterGrade",

definition=function(assignment,value)

#Loopthrougheachiteminthevectorofvalues.Also,

#convertthevaluetoacharactervector.Weneedthe

#scalelistinsidetheloopsograbacopynowfor

#lateruse.

lupe<-1

value<-as.character(value)

theScale<-GetScale(assignment)

theNames<-names(theScale)

while(lupe<=length(value))

#Determinewhetherthisitemcanbefoundinthe

#listofscaleitems.Expressitasaregular

#expressionandmakesureitisanexactmatch

#byusingfirstandlastplacemarkers.

thePattern<-paste("^",sub("\\+","\\\\+",

value[lupe]),"$",sep="")

pos<-grep(thePattern,theNames)

if(value[lupe]==""){

#Anemptystringwaspassed.

value[lupe]<-"NA"

}elseif(length(pos)!=1){

#Thisitemwasnotfound.PrintawarningandmakeitanNA

warning(paste("Thegrade\"",value[lupe],

"\"isnotrecognized.ItissettoNA.",

sep=""))

value[lupe]<-"NA"

lupe<-lupe+1

assignment@Value<-value

return(assignment)

Thefinalmethodexaminedhereisthemethodtoprintoutagradereport.ItisasimplermethodcomparedtothesamemethodintheNumericGradeclass.Inthiscase,theonlyresultstoprintarethefrequencyofoccurrencesforeachpossiblelettergrade:

setMethod(f="GradeReport",

definition=function(assignment,maxGrade=NA,div=10)

print(noquote(''))

print(noquote(paste("Gradereportfor",

GetName(assignment))))

print(noquote(''))

print(summary(assignment))#Printoutafive

#pointsummaryforthedata

Example–readinggradesfromafileAshortexampleisgiventodemonstratehowtoreadaclassfile.WeassumethataCSVfile,calledshortList.csv,isinthecurrentworkingdirectory.Thefirsttasktoaccomplishistoexecutethefilethatcontainstheclassdefinitions,grades.R.Oncetheclassisdefined,thefilenameisset,andthefileisread.

First,inthisexample,thefilewiththeshortList.csvgradesisassumedtobethefollowing:

section,test1,project1,final

3,75,B+,89

2,68,B,94

3,98,B+,110

2,76,A-,93

1,96,A+,112

1,81,B+,91

2,19,A,70

2,52,B+,70

2,88,A,71

ThedefaultvaluefortheGradeTypesslotintheCourseclassisthefollowing:

GradeTypes=c("test","hw","quiz","project")

AnycolumninthefilewhosenamestartswithoneofthestringsintheGradeTypesvectorisassumedtobearecognizedgrade.Thetest1,test2,hw1,hw2,hw3,quiz1,quiz2,andproject1columnsarerecognizedasbeingagradeditem.Thecolumnwhosenameisfinalisnotinthedefaultvectorandisnotrecognized,sotheGradeTypesslotshouldbereplaced:

>source('grades.R')

>dir(pattern="csv$")

[1]"math100.csv""shortList.csv"

>course<-Course()

>course<-SetGradeTypes(course,c("test","hw","quiz","project",

"final"))

>course<-SetFileName(course,"shortList.csv")

>course<-ReadGrades(course)

>course

Anobjectofclass"Course"

Slot"GradesFile":

[1]"shortList.csv"

Slot"GradeTypes":

[1]"test""hw""quiz""project""final"

Slot"Grades":

$test1

Anobjectofclass"NumericGrade"

Slot"Value":

[1]756898769681195288

Slot"Name":

[1]"Test"

Slot"Number":

$project1

Anobjectofclass"LetterGrade"

Slot"Value":

[1]"B+""B""B+""A-""A+""B+""A""B+""A"

Slot"Scale":

Slot"Name":

[1]"Test"

Slot"Number":

$final

Anobjectofclass"NumericGrade"

Slot"Value":

[1]89941109311291707071

Slot"Name":

[1]"Test"

Slot"Number":

DefiningindexingoperationsAnobjectthatisaCourseclasscanhavemanyassignmentobjectsinitsGradesslot.Wedidnotdefineaspecialmethodtogetaparticularassignment,andnomethodisdefinedtosaveanassignment.Weexaminehowtodothisisinthissection,andthediscussionrevolvesaroundredefiningthe[operation.

Firstweredefinethe[operationusedtogetanassignment.Theideaisthatwewanttogetacopyofanassignmentbyenclosingthenameoftheassignmentasdefinedintheoriginalfilewithinsquarebraces.Todothis,wecanredefinetheoperation.Inthiscase,wewanttobeabletopassanykindofobjectwithinthebraces,whichwillallowustoalsouseintegerstoindexbylocationinthelist:

setMethod("[",

signature(x="Course",i="ANY"),

definition=function(x,i=1)

#print(paste("Getgradeitem",i))

return(x@Grades[[i]])

Wewouldliketoalsobeabletogetagradewithinanassignment.Inthiscase,wewillpasstwoargumentswithinthebraces.Thefirsttaskistogettheassignmentusingthepreviousdefinitionandthengetthegradewithintheassignmentwhoseindexmatchesthesecondargument:

setMethod("[",

signature(x="Course",i="ANY",j="numeric"),

definition=function(x,i=1,j=1)

#print(paste("coursevalue",i,j))

allGrades<-GetValue(x[i])

return(allGrades[[j]])

Weassumethattheoperationsaredefinedinafilecalledops.R.Oncethesemethodsarereadandexecuted,the[operationisusedinthefollowingexampletogetacopyoftest1ortoexaminethethirdgradeintest1:

>source('grades.R')

>source('ops.R')

>source('overriding.R')

>course<-Course()

>course['test1']

[1]Assignment:Test

[1](9)Grades:

[1]756898769681195288

>course['test1',3]

Thefinaltopictoexamineinthissectionistheuseofthe[operationtosetthevalueofanentry.Theapproachissimilartothemethodsusedtogetinformationgivenearlier.TheonlydifferenceisthatinsteadofusingthesetMethodfunction,weusethesetReplacemethod,andthelastargumenttothefunctionisthevaluetosetthecorrespondingentryintheappropriateobject:

setReplaceMethod("[",

signature("NumericGrade"),

definition=function(x,i,value)

#print(paste("gradevalue",i,value))

x@Value[i]=value

return(x)

setReplaceMethod("[",

signature("Course"),

definition=function(x,i,j,value)

#print(paste("coursegrade",i,j,value))

grades<-x@Grades[[i]]

grades[j]<-value

x@Grades[i]<-grades

return(x)

Withthesedefinitionsinplace,avaluewithinthecourseforaspecificgradecanbeeasilyset,asfollows:

>source('grades.R')

>source('ops.R')

>course<-Course()

>print(course['test1'])

[1]Assignment:Test

[1](9)Grades:

[1]756898769681195288

>print(course['test1',3])

>course['test1',3]<-99.1

>print(course['test1',3])

[1]99.1

RedefiningexistingfunctionsAsmentionedpreviously,thesummary,show,andplotcommandsareextendedtoreactinanappropriatewaywhenpassedaNumericGradeorLetterGradeobject.ExtendingthesefunctionsisdonebysimplydefininganewmethodforthesefunctionsusingthesetMethodcommand.Eachofthesecommandsalreadyexist,soitisnotnecessarytoreservethenamesusingthesetGenericcommand.Thatis,wesimplydefinethemethodtoassociatewiththecommandwhenpassedanobjectthatisamemberoftheNumericGradeorLetterGradeclass.

Weextendthesummarycommandinthefirstexample.Inthiscase,thefunctionshouldhaveadifferentbehavioriftheobjectpassedtoitisNumericGradeversusLetterGrade.ForanobjectthatisLetterGrade,thesummarycommandretrievesthegradesandprintsoutafrequencytableforthegrades:

setMethod(f="summary",

definition=function(object,...)

#Getthelettergradesasfactorsandreturnthe

#frequencytable.

values<-GetLetterGrade(object)

return(summary(as.factor(values)))

Inthisexample,theonlyotherkindofassignmentthatcanbecreatedisNumericGrade,butinthefuture,theclassmaybeextended.InsteadofcreatingasummaryfunctionsolelyfortheNumericGradeclass,wecreateasummaryfunctionforitsbaseclass,Assignment.Inthisway,anargumenttothesummarycommandthatisderivedfromtheAssignmentclasswillobtaintheobject’sgrades,andthegradesareassumedtobeanumericvector.Thesummarycommandcanthenbeinvokedonthevector:

setMethod(f="summary",

definition=function(object,...)

#Getthegradevaluesandreturnthefivepoint

#summary.

values<-GetValue(object)

return(summary(values))

Inthefollowingexample,thenecessaryfilesareread,andtheinformationfromtheshortList.csvfileisread.Copiesoftwodifferentassignments,test1andproject1,arefound,andasummaryforeachobjectisprinted:

>source('grades.R')

>source('ops.R')

Creatingagenericfunctionfor'plot'frompackage'graphics'inthe

globalenvironment

Creatingagenericfunctionfor'summary'frompackage'base'intheglobal

environment

>course<-Course()

>x<-course['test1']

>summary(x)

Min.1stQu.MedianMean3rdQu.Max.

19.0068.0076.0072.5688.0098.00

>p<-course['project1']

>summary(p)

AA+A-BB+

Thefilesavailableintherepositoryforthisbookcontaindefinitionsfortheshow,summary,andplotcommands.Weexamineonemorehere.TheplotcommandforaNumericGradeobjectisabitmorecomplicatedthantheotherexamples.Theplotcommandobtainsthegradesfromtheobjectpassedtoit,anditprintsoutahistogram,addsaboxplotatthetopofthehistogram,andtherugcommandisusedtodisplaythedatavaluesonthesameplot.

Theargumentstothecommandincludethemaximumgradefortheassignment.Thisargumentisrequiredbecauseinsomecircumstances,extracreditisavailableandsomestudentsmayachieveahigherscorethanisallocatedfortherestoftheclass,andinothersituations,nostudentmayachieveaperfectscore.Ifthisargumentisnotprovided,thenthemaximumscoreisused.

Asecondargumentisincludedthatindicateswhatpercentagestouse.Thedefaultis10,whichindicatesthatthebreaksinthehistogramaremadeatthe10percentmarksforthescores.Forexample,ifthedefaultof10isused,thenthescoresinthe90-100rangearecountedtogether.

Thefinalargumentistheellipsessymbol(…),whichindicatesthatotherargumentscanbepassedtothefunction.Thesamesymbolisusedintheplotcommandwithinthemethod.Theideaisthatalloftheextraparametersarepassedtotheplotcommand.Thisallowsustosetawidearrayofplotparametersthroughthemethodwithouthavingtocatchanyspecialcases:

setMethod(f="plot",

definition=function(x,maxGrade=NA,div=10,...)

#print("Plottinganassignment")

values<-GetValue(x)#Gettherawscores

if(is.na(maxGrade)){

#ThemaxGradewasnotset.Assumethemaxscorefrom

#thedataisthemaximumpossible.

maxGrade<-max(values)

warning(paste("Themaxgadeisnotset,",

"anditisassumedtobe",

maxGrade))

skip<-maxGrade*div/100;#Setthewidthofthe

#intervals.

#Determinethenumberofintervals.

numLower<-ceiling((maxGrade-min(values))/skip)

#Determinethecutoffvaluesbetweenthebinsinthe

#histogram.

bins=c(seq(maxGrade-numLower*skip,

max(c(values,maxGrade)),by=skip))

if(max(bins)<max(values)){

#Thebinsdonotincludethemaximumvalue.Adjust

#theboundontheuppermostbin.

bins[length(bins)]<-max(values);

#Convertthedataintofactors

levs<-cut(values,breaks=bins,right=FALSE)

#Determinethefrequenciesforthedifferentlevels.

gradeFreqs<-table(levs)

#Getthemaxfrequency

top<-max(gradeFreqs)

#Plotthehistogram.

hist(values,breaks=bins,

freq=TRUE,

ylim=c(0,top+1),axes=FALSE,

col=grey((seq(length(bins)-1,1,by=-1)/

(length(bins)-1))),

right=FALSE,...)

#Addaboxplotacrossthetop

boxplot(values,horizontal=TRUE,at=top+0.5,add=TRUE,

axes=FALSE)

#Plottherawdataasastripchartacrossthebottom

rug(values)

#Turnononlytheleftandloweraxes.

axis(side=1,at=bins)

axis(side=2,at=seq(0,top+1,by=1))

Inthefollowingexample,alargerdatafileisread.Theresultsfromtest2areplotted,andthemethodautomaticallygeneratestheplotasdescribedearlier:

>source('grades.R')

>source('ops.R')

>course<-Course()

>course<-SetFileName(course,"math100.csv")

>x<-course['test2']

>plot(x,maxGrade=100,main='StudentScoresFromTest2')

Warningmessage:

Inplot.histogram(r,freq=freq1,col=col,border=border,angle=

angle,:

theAREASintheplotarewrong—ratheruse'freq=FALSE'

Notethatawarningmessageisprinted.Themaximumgradeisspecifiedas100,buttherearesomestudentsintheclasswhoachieveda102becauseofextracredit.Thebreaksinthehistogramincludethestudentswhoachievedahighergradethanthemaximumgradeinthetop10percent,andthatsetofvalueshasadifferentwidththantheothers.Theresultingplotisshowninthefollowingscreenshot:

Thehistogramcreatedbytheplotcommandgiventheinformationinthemath-100.csvgradefile

RedefiningarithmeticoperationsThelasttopicthatwe’regoingtoexamineishowtodefinebasicarithmeticoperationsonassignments.Inparticular,we’llexaminehowtoperformarithmeticoperationsontwoNumericGradeobjectsandhowtoaddasetofvaluestoaNumericGradeobject.ThiscanbedoneintheRenvironmentbyextendingoneofthesetsofgenericgroupsthatisusedtocollectsimilaroperations.ThedifferentgroupsincludetheArith,Compare,Ops,Logic,Math,Math2,Summary,andComplexgroups.TheOpsgroupincludestheArith,Compare,andLogicoperations,andweextendthisgrouptoincludeobjectsfromtheNumericGradesclass.

WeusethesetMethodfunctionfortheOpsgrouptodefineoperationsinvolvingNumericGradeobjects.TheresultingfunctionobtainsthegradesandperformstherequisiteoperationusingthecallGenericcommand.ThequirkofthesystemisthatitmustreturnaNumericGradeobject,andthelaststepistosetthevalueofthegradesforthefirstNumericGradeobjectsenttothefunction:

setMethod("Ops",signature(e1="NumericGrade",e2="NumericGrade"),

function(e1,e2){

theSum<-callGeneric(GetValue(e1),GetValue(e2))

return(SetValue(e1,theSum))

setMethod("Ops",signature(e1="NumericGrade",e2="numeric"),

function(e1,e2){

theSum<-callGeneric(GetValue(e1),e2)

setMethod("Ops",signature(e1="numeric",e2="NumericGrade"),

function(e1,e2){

theSum<-callGeneric(e1,GetValue(e2))

Inthefollowingexample,thegradesfromthemath100.csvfileareread.Acopyofthescorestakenfromthetest1andtest2assignmentsareobtained,andthesimpleaverageisfoundbyaddingthemanddividingbytwo:

>source('grades.R')

>source('ops.R')

>course<-Course()

>course<-SetFileName(course,"math100.csv")

>x<-course['test1']

>y<-course['test2']

>z<-(x+y)/2

>print(z)

[1]Assignment:Test

[1](327)Grades:

[1]80.076.095.583.098.087.045.566.594.076.563.5

[13]66.052.585.535.582.590.589.551.071.053.580.0

[25]39.061.50.072.584.070.097.576.052.073.068.5

[37]44.086.064.059.061.558.043.595.553.560.077.5

[49]93.552.597.555.098.068.063.046.574.526.578.5

[61]84.592.578.578.084.511.051.096.073.095.094.0

[73]93.00.086.044.575.051.557.5100.048.064.557.0

[85]75.576.562.019.052.052.579.071.563.024.091.0

[97]0.073.062.00.066.588.577.591.548.063.553.5

[109]80.50.055.585.040.579.00.092.560.591.541.5

[121]67.076.076.560.585.564.587.531.087.00.098.5

[133]91.086.548.054.554.034.056.50.061.584.544.5

[145]64.059.575.560.561.583.066.052.572.066.572.5

[157]98.064.578.577.019.080.533.053.544.074.074.0

[169]45.083.060.078.070.079.060.096.568.592.063.5

[181]62.564.082.590.057.571.054.029.545.079.527.5

[193]74.571.00.00.031.077.049.048.552.0100.575.5

[205]79.062.092.557.583.00.045.572.576.585.034.5

[217]100.574.582.00.063.079.547.567.580.558.082.0

[229]68.061.562.054.568.569.087.544.065.553.047.0

[241]64.093.052.051.581.067.092.585.036.076.578.0

[253]42.586.076.043.074.090.544.540.545.050.548.0

[265]87.088.059.559.548.062.094.080.071.064.065.0

[277]61.00.057.033.091.0100.066.091.598.070.060.0

[289]91.081.542.080.067.064.564.555.588.573.581.5

[301]59.565.077.558.589.5100.544.585.50.070.590.0

[313]81.064.551.076.554.588.058.556.023.036.088.5

[325]56.066.516.5

SummaryAnextendedexamplemakinguseofS4classeswasexaminedinthischapter,andasetofclassesaredefinedtoreadandtrackthegradesforaclass.TheCourseclasskeepstrackofanumberofassignments,anditreadsthecontentsofaCSVfileandautomaticallydetermineswhichcolumnsaregradesandwhetherornottheyarenumericalgradesorlettergrades.

Thegradesforaparticularassignmentarekeptinoneoftwoclasses.TheNumericGradeclasskeepstrackofnumericalgrades,andtheLetterGradeclasskeepstrackoflettergrades.BothclassesarederivedfromtheAssignmentbaseclass.

Anumberofexamplesweregiven,andhighlightsfromthecodeweregiven.Thefullsetofcodecanbefoundonthewebsiteassociatedwiththisbook,andwerecommendthatyoucloselyexaminethecode.Thethreefilesthatincludethedefinitionsforthisclassarethegrades.R,ops.R,andoverriding.Rfiles.

Inthenextchapter,anothersetofclassesaredeveloped.Theclassesinthatchapterprovideanexampleofasetofclassesthatcanbeusedtogeneratetheresultsfromastochasticprocessandmanagetheresultsfromalargenumberofsimulations.Theclassescanbeusedtogenerateresultsfromeitheradiscreteorcontinuousprocess,andthedistributionoftheresultscanbeexplored.

Chapter11.CaseStudy–SimulationWewillnowexamineasetofS3classesdesignedtoimplementaMonte-Carlosimulation.Theexamplescriptsincludeaclasstogenerateasinglesimulationaswellasaclasstorunandcollecttheresultsfrommultiplesimulations.Thesimulationclassincludestwoderivedclasses.Oneisusedforsimulationsofadiscretestochasticprocess,andtheotherisforsimulationsofastochasticdifferentialequation.

Thissectionisroughlydividedintothefollowingparts:

ThesimulationclassesTheMonte-CarloclassExamples

Wewillfirstexamineasetofsimulationclassesthataredesignedtogenerateasinglesimulationofastochasticsystem.Theclassesmakeuseofabaseclasstomanagetheparameters.Twoclassesarederivedfromthebaseclass.Thefirstderivedclassisusedtosimulateadiscretestochasticsystem.Thesecondderivedclassisusedtoapproximateastochasticdifferentialequation.

Afterexaminingthesimulationclasses,amasterclassisusedtomanagetheMonte-Carlosimulations.TheMonte-Carloclassacceptsasimulationclassandcollectstheresultsfrommultiplesimulations.Ourfocusisonthebasicstructureoftheclasses,sowedonotdiscussstatisticalmethodsforthedata.

Webrieflyexamineanexampleusingtheclassesinthelastsectionofthischapter.Theexamplefocusesonhowtocreateanobjectfromthediscretestochasticsimulationclassandhowtogenerateresults.

ThesimulationclassesThesimulationclassesconsistofthreeparts.TheSimulationclassisthebaseclass,andtwoclasses,DiscreteSimulationandContinuousSimulation,arederivedfromthebaseclass.Weassumethatthedefinitionsfortheseclassesarekeptinaseparatefile,simulationS3.R.

Thebaseclass,Simulation,isusedtomanagetheparametersandresultsforasinglesimulation.Thedataincludesthefinaltimeusedinthesimulationandaccessormethodsaredefinedforthedata:

########################Createthebasesimulationclass

##Thisisusedtorepresentasinglesimulation

Simulation<-function()

##Createthelistusedtorepresentan

##objectforthisclass

me=list(

simulationResults=matrix(0)

##Setthenamefortheclass

class(me)<-append(class(me),"Simulation")

return(me)

}#Setthedatavaluesthataretheresultofasimulation.

setSimulation<-function(theSimulation)

UseMethod("setSimulation",theSimulation)

setSimulation<-function(theSimulation,simulationResults)

##SetthevalueofthevariabletheSimulation

theSimulation$simulationResults<-simulationResults

return(theSimulation)

##methodtoreturnthedatafromthecurrentsetofresults.

getFinalValues<-function(theSimulation)

UseMethod("getFinalValues",theSimulation)

getFinalValues<-function(theSimulation)

##Getthevalueofthedatapairatthelasttimestep

size<-dim(theSimulation$simulationResults)

return(c(theSimulation$simulationResults[size[1],1],

theSimulation$simulationResults[size[1],2]))

Notethattheclassincludesanadditionmethod,getFinalValues,whichisusedto

retrievethelastvaluesapproximatedinthesimulation.ThismethodisrequiredbytheMonte-Carloclassdefinedinthenextsection.Theresultsareusedtodeterminetherequisitestatistics.

ThenexttwoclassesaretheDiscreteSimulationandContinuousSimulationclasses.TheDiscreteSimulationclassisusedtogenerateanapproximationtothediscretestochasticsystemgivenbythefollowingequation:

Intheprecedingequation,a,b,N1,g,d,andN2areconstants,andW1andW2arenormallydistributedrandomvariableswithameanofzeroandastandarddeviationofone.TheContinuousSimulationclassisusedtogenerateanapproximationusingtheMilsteinschemetothestochasticdifferentialequation:

Intheprecedingequation,a,b,N1,g,d,andN2areconstants,andW1andW2areindependentWienerprocesses.

Thecompletescriptsforalloftheclassescanbefoundinthecodethataccompaniesthistext.Intheinterestofbrevity,weonlylookatonederivedclass:theDiscreteSimulationclass.Thedefinitionoftheclassisgivenhere:

#############################################################

##Createasimulationforadiscretesimulation.

##Thisisusedtorepresenttheresultsfromadiscretesimulation.

DiscreteSimulation<-function()

##Definethebaseclassandgettheenvironment

me<-Simulation()

me$N<-0

##Setthenamefortheclasswithanumericgradeassociatedwith

class(me)<-append(class(me),"DiscreteSimulation")

return(me)

ThefinalmethodspecifictothisclassisthesingleSimulationmethodthatisusedtoconductonesimulationandsavetheresults.Themethodsnecessarytodefinethediscretesimulationclassareasfollows:

#themethodstodotheactualsimulations.

singleSimulation<-function(simulation,N,T,x0,y0,alpha,beta,

gamma,delta,noiseOne,noiseTwo)

UseMethod("singleSimulation",simulation)

singleSimulation.DiscreteSimulation<-function(

simulation,N,T,x0,y0,alpha,beta,gamma,delta,noiseOne,noiseTwo)

##Makeanapproximationforonerunofthediscretemodel

##withthegivenparameters.Storetheapproximationin

##thesimulationslotwhendone.

##initializethenecessaryvariables.

x<-matrix(data=double(N*2),nrow=N,ncol=2)

x[1,1]<-x0

x[1,2]<-y0

lupe<-2

##GothroughandmakeNiterationsofthestochasticmodel.

while(lupe<=N)

dW<-rnorm(2,mean=0,sd=1)#Generatetworandomnumbers

#withanormaldist.

##Takeonestepofthediscretemodel

x[lupe,1]<-alpha*x[lupe-1,1]+beta*x[lupe-1,1]*x[lupe-1,2]+

noiseOne*dW[1]

x[lupe,2]<-gamma*x[lupe-1,1]+delta*x[lupe-1,2]+

noiseTwo*dW[2]

lupe<-lupe+1

##Savethesimulationandreturntheresult.

simulation<-setSimulation(simulation,x)

return(simulation)

TheMonte-CarloclassThefinalclassexaminedistheMonteCarloclass.TheMonteCarloclassisusedtokeeptrackoftheresultsfrommultiplesimulations.Here,weprovideapartiallistofthecodefortheclassandthenprovidethemethodsusedtogeneratemultiplesimulations.

First,thecoderequiredtodefinetheclassisgivenhere:

############################################################

#CreatetheMonteCarloclass

#Thisclassisusedtomakemanysimulations

MonteCarlo<-function()

#Definetheslots

me=list(

##Firstdefinetheparametersforthestochasticmodel

alpha=0,

beta=0,

gamma=0,

delta=0,

noiseOne=0,

noiseTwo=0,

##Definethedatatotrackandthenumberoftrials

xData=0,

yData=0

##Setthenamefortheclass

class(me)<-append(class(me),"MonteCarlo")

return(me)

#Definethemethodusedtoinitializethedatapriortoarun.

prepare<-function(monteCarlo,number)

UseMethod("prepare",monteCarlo)

prepare.MonteCarlo<-function(monteCarlo,number)

##Setthenumberoftrialsandinitializethevaluesto

##zeroes.

monteCarlo$xData<-double(number)

monteCarlo$yData<-double(number)

return(monteCarlo)

Theclassrequiresanadditionalmethod.Thesimulationsmethodisusedtocreatemultiplesimulationsandrecordtheresults:

simulations<-function(monteCarlo,number,simulation)

UseMethod("simulations",monteCarlo)

simulations.MonteCarlo<-function(monteCarlo,number,simulation)

##Setthenumberoftrialsandinitializethevalues

monteCarlo<-prepare(monteCarlo,number)

params<-getParams(monteCarlo)#gettheparameters

##Performthesimulations

lupe<-0

while(lupe<number)

lupe<-lupe+1#incrementthecount

##Performasinglesimulation.

simulation<-singleSimulation(

simulation,

params[1],params[2],params[3],params[4],params[5],

params[6],params[7],params[8],params[9],params[10])

##Getthelastvaluesofthesimulationandrecordthem.

values<-getFinalValues(simulation)

monteCarlo<-setValue(monteCarlo,values[1],values[2],lupe)

return(monteCarlo)

ExamplesWewillnowprovideabriefexampletodemonstratehowtousetheSimulationandMonteCarloclassesdescribedintheprevioussections.Wefocusonthediscretesimulationclass,butadditionalexamplescanbefoundinthecodethataccompaniesthistext.Inthisexample,weassumethatthedefinitionsforthesimulationandMonteCarloclassesarecontainedintwofiles,simulationS3.RandmonteCarloS3.R.

TheMonte-CarlosimulationscanbecreatedbyfirstcreatinganobjectfromtheMontyCarloclassandsettingthevaluesoftheparametersasfollows:

>source('simulationS3.R')

>source('monteCarloS3.R')

>monty<-MonteCarlo()

>monty$setParams(100,1,

1.0,2.0,

1.2,-0.3,0.65,0.2,

0.03,0.04)

NowthatanobjectfromtheMonteCarloclassisdefined,anobjectfromtheDiscreteSimulationclassiscreated,andthesimulationobjectisusedtogeneratetheresultsfrom500simulations:

>a<-DiscreteSimulation()

>monty<-simulations(monty,500,a)

Atthispoint,themontyobjecthastheresultsfrom500simulations.TheresultscanbefoundusingthegetValuesmethod,asfollows:

>summary(results[,1])

0.64800.77920.82310.82130.85951.0110

>summary(results[,2])

0.49780.62320.65900.66320.70640.8918

Alternatively,methodscanbedefinedthatextendexistingmethods.Forexample,thehistfunctioncanbeextendedtoaccommodateanobjectfromtheMonteCarloclass:

#themethodstoplottheresults

hist.MonteCarlo<-function(x,main="",...)

par(mfrow=c(2,1))

values<-getValues(x)

isValid<-(!is.na(values[,1]))&&(!is.infinite(values[,1]))

hist(values[isValid,1],xlab="x",main=main,...)

hist(values[isValid,2],xlab="y",main="",...)

Withthisdefinition,ahistogramcanbeeasilycreated:

hist(monty,main="ResultsfromaDiscreteSimulation")

SummaryAnexampleofanS3classwasdefinedthatcanbeusedtokeeptrackofasetofsimulations.Theclassesincludeaseparateclassusedtocreateasinglesimulation.Thesinglesimulationcanbeanapproximationofeitheradiscreteorcontinuousstochasticprocess.Anotherclassisdevelopedthatcankeeptrackoftheresultsfromalargenumberofsimulations.

Inthenextchapter,welookatanotherextendedexample.ThefocusinthenextchapterisoncreatingasetofS3classestoprovideageneralwaytohandleregressiontasksforavarietyofdatatypes.Youcandownloadthischapterfromhttps://www.packtpub.com/sites/default/files/downloads/6682OS_Case_Study_Regression.pdf

AppendixA.PackageManagementAbriefoverviewofworkingwithpackagesisprovidedhere.Thisisgivenasareferencetothebasiccommandsusedtomanagepackages.ItisnotexhaustiveandservesonlyasabriefreferencetomanagepackagesassociatedwithyourinstallationofR.

Theappendixhasfourparts:

AnoverviewonhowtoaccessapackageAnoverviewonhowtoinstallapackageAnoverviewonhowtoremoveapackageAnoverviewonhowtoupdatepackages

OneofR’sgreateststrengthsistheabilitytousespecializedpackages,andawiderangeofpackagesareavailable.Someofthepackagesareincludedintheregularinstallation,andsomepackagesmustbeinstalledandmaintainedseparately.Weprovideabriefoverviewofhowtoinstall,remove,andupgradetheinstalledpackagesonyoursystem.

Wefirstdiscusstwocommandsthatarecommonlyusedwhenworkingwithpackages.Thefirstistheinstalled.packagescommand.Thiscommandwilllistallofthepackagesthatarepartofyourinstallation.Theothercommandisthelibrarycommand.ThelibrarycommandisusedtotellRtomakeuseofthecommandsavailableinagivenlibrary.Inthefollowingexample,thefirstcommanddisplaysinformationaboutthesplinespackage,andthesecondcommandmustbeenteredbeforeusingthesplinepackage:

>library(help="splines")

>library(splines)

Ifapackageisnotpartofyourinstallation,youneedtoinstallit.Thecommandtoinstallapackageis,oddlyenough,theinstall.packagecommand.Inthefollowingexample,thecarpackage,whichisusedforadditionalregressionoptions,isinstalled.Thefulldetailsarenotprovided.YoumustreplytoaseriesofquestionsposedandthenRwillautomaticallyfetchandinstallthepackageforyou:

>install.packages("car")

Apackagecanalsobeeasilyremovedusingtheremove.packagescommand.Inthefollowingexample,weremovethecarpackage:

>remove.packages("car")

Thelasttopicdiscussedishowtoupdateyourpackages.Toupdateallofyourpackages,simplyusetheupdate.packagescommand.Inthefollowingexample,thecommandisenteredandthefulldetailsarenotprovided.Aftersubmittingthecommand,youaregivenalistofpackagesthatcanbeupdatedandyoumustdecidewhichpackageswillbeupdatedonyoursystem:

>update.packages()

IndexA

Antclassabout/IntroducingtheAntclass,DefininganS4class,Inheritance

Antobjectcreating/DefininganS4classnewcommand/DefininganS4classAntgenerator/DefininganS4class

appendfunction/Definingobjectsandinheritanceapplycommand/applyapplycommands

about/Operationsondatastructures,Theapplycommandsapplycommand/applylapplycommand/lapplyandsapplysapplycommand/lapplyandsapplytapplycommand/tapplymapplycommand/mapply

argumentspassing,tofunctions/Argumentstofunctions

arithmeticoperationsredefining/Redefiningarithmeticoperations

arraycommand/Matricesandarraysarrays,datastructure/Matricesandarraysas.charactercommand/Character,Notesontheasandisfunctionsas.difftimecommand/Operationsontimedatatypesas.doublecommand/Doubleas.factorcommand/Factorsas.integercommand/Integeras.listcommand/Listsas.POSIXctcommand/Convertingstringstotimedatatypesasciioption/Theworkspaceasfunction/Notesontheasandisfunctionsassigncommand/Scopeassignment,variables

about/Assignmentassignmentclass

about/Theassignmentclassesname/Theassignmentclassesnumber/Theassignmentclasses

assignmentclassesabout/TheassignmentclassesNumericGradeclass/Theassignmentclasses,TheNumericGradeclass

LetterGradeclass/Theassignmentclasses,TheLetterGradeclassgrades,readingfromfile/Example–readinggradesfromafile

attachcommand/Scopeattributescommand/Lists

BbaseRenvironment

about/Overviewbasicdatastructures

about/Basicdatastructuresvectors/Vectorslists/Listsdataframes/Dataframestables/Tablesmatrices/Matricesandarraysarrays/Matricesandarraysdata,censoring/Censoringdatarows,appending/Appendingrowsandcolumnscolumns,appending/Appendingrowsandcolumns

basicstringoperationsabout/Basicstringoperations

breakcommand/Therepeatloopbreakstatement/Breakandnextstatements

C#character/ConditionalexecutioncallGenericcommand/RedefiningarithmeticoperationscallNextMethodcommand/Inheritancecatcommand/Thecatcommandcbindcommand/Appendingrowsandcolumnsccommand/Vectorscharacter,discretedatatype/Charactercharactercommand/Characterclasscommand/Integer,Definingclassesandmethods,Definingobjectsandinheritance,DefininganS4classclasses

defining/Definingclassesandmethodscolnamescommand/TablescolSumscommand/applycolumns

appending/Appendingrowsandcolumnscommandline

data,enteringfrom/Enteringdatafromthecommandline,Readingtablesfromfiles

commandsdetermining,variabletype/Notesontheasandisfunctionsused,forcastingvariableintoparticulartype/Notesontheasandisfunctions

complex,continuousdatatype/Complexcomplexcommand/Complex,Specialdatatypesconditionalexecution

about/Conditionalexecutioncontinuousdatatypes

about/Continuousdatatypesdouble/Doublecomplex/Complex

ContinuousSimulationclass/ThesimulationclassesCourseclass

about/TheCourseclassdefining/ThedefinitionoftheCourseclassGrades,readingfromfile/Readinggradesfromafile

CSVfiles/CSVfilescumulativedistributionfunctions

about/Cumulativedistributionfunctionscutcommand/Tables

D%dcharacter/Creatingformattedstringsdata

censoring/Censoringdataentering/Enteringdatasaving/Printingresultsandsavingdata

data,enteringfromcommandline/Enteringdatafromthecommandlinetables,readingfromfiles/ReadingtablesfromfilesCSVfiles/CSVfilesfixedwidthfile/Fixed-widthfiles

data.entrycommand/Enteringdatafromthecommandlinedataframes,datastructure/Dataframesdateformation/Introductionandassumptionsdetachcommand/Scopedifftimecommand/Operationsontimedatatypesdimcommand/Matricesandarraysdimoption/Matricesandarraysdircommand/Fileanddirectoryinformationdirectoryinformation

about/Fileanddirectoryinformationdiscretedatatypes

about/Discretedatatypesinteger/Integerlogical/Logicalcharacter/Characterfactors/Factors

DiscreteSimulationclass/Thesimulationclassesdistributionfunctions

about/Distributionfunctionsdouble,continuousdatatype/Doubledoublecommand/Doubledpoiscommand/Cumulativedistributionfunctions

Eelsestatement/Conditionalexecutionencapsulation

about/Encapsulationexamples

used,fordemonstratingSimulationclassusage/Examplesused,fordemonstratingMonteCarloclassusage/Examples

existingfunctionsredefining/Redefiningexistingfunctions

existingmethodsextending/Extendingtheexistingmethods

Ffactorcommand/Factorsfactors,discretedatatype/FactorsFALSEvalue/Logicalfile

Grades,readingfrom/Readinggradesfromafilegrades,readingfrom/Example–readinggradesfromafile

filecommand/Primitiveinput/outputfileinformation

about/Fileanddirectoryinformationfixedwidthfile/Fixed-widthfilesforloop

versuswhileloop/Thewhileloopforloops/Theforloopformatcommand/Theprint,format,andpastecommandsformattedstrings

creating/Creatingformattedstringsfunctions

about/Functionsdefining/Definingafunctionarguments,passingto/Argumentstofunctionsscope/Scope

GGeometricTrialclass

resetmethod/DefiningobjectsandinheritancegetHistorymethod/Definingobjectsandinheritancesimulationmethod/DefiningobjectsandinheritancesingleTrialmethod/Definingobjectsandinheritance

getClasscommandabout/Miscellaneousnotes

getcommand/ScopegetFinalValuesmethod/ThesimulationclassesgetHistorymethod/DefiningobjectsandinheritanceGetLengthfunction/DefiningnewmethodsgetMethod(*show*)command/ExtendingtheexistingmethodsgetSlotscommand

about/Miscellaneousnotesgetwdcommand/Fileanddirectoryinformationgradefile

reading/OverviewGrades

reading,fromfile/Readinggradesfromafilegrades

reading,fromfile/Example–readinggradesfromafilegregexprcommand/Regularexpressionsgrepcommand/TheLetterGradeclass

H(help(factor))command/Factorshelp(Control)command/Loopconstructshelp(DateTimeClasses)command/Introductionandassumptionshelp(Distributions)command/Overviewhelp(dpois)command/Distributionfunctionshelp(regularexpression)command/Regularexpressionshelp(RNG)command/Generatingpseudorandomnumbershelp.searchcommand/Theworkspacehelpcommand/Theworkspace,Primitiveinput/output

Iifstatement/Conditionalexecutionindexingoperations

defining/Definingindexingoperationsinheritance

defining/Definingobjectsandinheritanceabout/Inheritance

initializecommand/Extendingtheexistingmethodsinitializefunction/Extendingtheexistingmethodsinteger,discretedatatype/Integerintegercommand/Integerinversecumulativedistributionfunctions

about/Inversecumulativedistributionfunctionsis.charactercommand/Characteris.factorcommand/Factorsis.integercommand/Integeris.listcommand/Listsis.nacommand/Specialdatatypesisfunction/Notesontheasandisfunctions

KKernighanandRitchie(K&R)/Conditionalexecution

Llambdaparameter/Distributionfunctionslapplycommand/lapplyandsapplylengthofstring

determining/DeterminingthelengthofastringLetterGradeclass/Theassignmentclasses,TheLetterGradeclasslist.dirscommand/Fileanddirectoryinformationlist.filescommand/Fileanddirectoryinformationlistcommand/Listslists,datastructure/Listsloadcommand/Savingaworkspaceloadhistorycommand/Theworkspacelogical,discretedatatype/Logicallogicalcommand/Logicallogicaloperators

</Logical>/Logical<=/Logical&&/Logical>=/Logical!/Logical==/Logical!=/Logical||/Logical|/Logical&/Logicalxor(a,b)/Logical

loopconstructsabout/Loopconstructsforloops/Theforloopwhileloops/Thewhilelooprepeatloops/Therepeatloopbreakstatement/Breakandnextstatementsnextstatement/Breakandnextstatements

lscommand/Savingaworkspace,Scope

Mmapplycommand/mapplymargin.table(A,1)command/Operationsondatastructuresmargin.table(A,2)command/Operationsondatastructuresmargincommand

about/Operationsondatastructuresmatch.argfunction/Argumentstofunctionsmatrices,datastructure/Matricesandarraysmatrixcommand/Matricesandarraysmethods

defining/Definingclassesandmethodsdefining,forS4class/DefiningmethodsforanS4class

methods,S4classnewmethods,defining/Definingnewmethodspolymorphism/Polymorphismexistingmethods,extending/Extendingtheexistingmethods

miscellaneousnotes,S4Classesabout/Miscellaneousnotes

missingcommand/Argumentstofunctionsmodecommand/TheworkspaceMonte-Carloclass

about/TheMonte-Carloclass

N$notation/Vectorsnamescommand/Listsncharcommand/Determiningthelengthofastringnetworkoptions

about/Networkoptionssocket,opening/Openingasocketsocketoperations/Basicsocketoperations

new.envcommand/Scopenewmethods

defining/DefiningnewmethodsNextMethodfunction/Definingobjectsandinheritancenextstatement/BreakandnextstatementsNumericGradeclass/Theassignmentclasses,TheNumericGradeclass

O&&operation/Logical&operation/Logical<-operator

about/Assignment/Scope<<-operator/Scope=operator

about/Assignment?operator/Theworkspaceobjects

defining/Definingobjectsandinheritanceoperations,ondatastructures

about/Operationsondatastructuresapplycommands/Theapplycommands

operators</Conditionalexecution>/Conditionalexecution<=/Conditionalexecution>=/Conditionalexecution==/Conditionalexecution!=/Conditionalexecution

options,help(trptime)command%a/Convertingstringstotimedatatypes%p/Convertingstringstotimedatatypes%A/Convertingstringstotimedatatypes%S/Convertingstringstotimedatatypes%b/Convertingstringstotimedatatypes%U/Convertingstringstotimedatatypes%B/Convertingstringstotimedatatypes%w/Convertingstringstotimedatatypes%c/Convertingstringstotimedatatypes%x/Convertingstringstotimedatatypes%d/Convertingstringstotimedatatypes%X/Convertingstringstotimedatatypes%H/Convertingstringstotimedatatypes%y/Convertingstringstotimedatatypes%I/Convertingstringstotimedatatypes%Y/Convertingstringstotimedatatypes%j/Convertingstringstotimedatatypes%z/Convertingstringstotimedatatypes%m/Convertingstringstotimedatatypes%Z/Convertingstringstotimedatatypes

%M/Convertingstringstotimedatatypesoptions,Renvironment

workspace,saving/Savingaworkspacecatcommand/Thecatcommandprintcommand/Theprint,format,andpastecommandsformatcommand/Theprint,format,andpastecommandspastecommand/Theprint,format,andpastecommands

Ppastecommand/Theprint,format,andpastecommandsplotcommand

about/Afinalnote/Redefiningexistingfunctionspointscommand/Distributionfunctionspolymorphism/Polymorphismppoiscommand/Cumulativedistributionfunctionsprefix

d/Overviewp/Overviewq/Overviewr/Overview

primitiveinput/outputabout/Primitiveinput/output

printcommand/Theprint,format,andpastecommandspseudorandomnumbers

generating/Generatingpseudorandomnumbers

Qq()command/Theworkspaceqchisqcommand/Inversecumulativedistributionfunctionsqpoiscommand/Inversecumulativedistributionfunctions

RRandomNumberGeneration(RNG)

about/Overviewrbindcommand/Appendingrowsandcolumnsread.csvcommand/CSVfilesread.fwfcommand/Fixed-widthfilesread.tablecommand/ReadingtablesfromfilesreadBincommand/Primitiveinput/outputreadCharcommand/Primitiveinput/outputReadGradesmethod/ReadinggradesfromafilereadLinescommand/Primitiveinput/output,Openingasocketregexpcommand/Locationofasubstringregularexpressions

about/Regularexpressionsrepeatloop

advantages/Therepeatloopdisadvantages/Therepeatloop

repeatloops/Therepeatloopreportmethod/TheNumericGradeclassresetfunction/Definingclassesandmethodsresetmethod/DefiningobjectsandinheritanceresetTrialfunction/Definingclassesandmethodsresults

printing/Printingresultsandsavingdatareturncommand/Definingafunctionrmcommand/TheworkspaceRNGkindcommand/Generatingpseudorandomnumbersrownamescommand/Tablesrows

appending/AppendingrowsandcolumnsrowSumscommand/applyrpoiscommand/Generatingpseudorandomnumbers

S%scharacter/Creatingformattedstrings<<-symbol

advantages/AssignmentS3classdefinition

advantages/AfinalnoteS3classes

about/AfinalnoteS3object

about/EncapsulationS4class

defining/DefininganS4classmethods,definingfor/DefiningmethodsforanS4class

samplecommand/Samplingsampling

about/Samplingsapplycommand/lapplyandsapplysave.imagecommand/Theworkspace,Savingaworkspacesavecommand/Theworkspace,Savingaworkspace,Generatingpseudorandomnumberssavehistorycommand/Theworkspacescancommand/Enteringdatafromthecommandlinescope/Scopescripts

executing/Executingscriptsseqcommand/Vectorsset.seedcommand/GeneratingpseudorandomnumberssetClasscommand/DefininganS4classsetGenericcommand/Definingnewmethods,PolymorphismsetMethodcommand/RedefiningexistingfunctionssetValiditycommand/DefininganS4classsetwdcommand/Fileanddirectoryinformationshowcommand/Extendingtheexistingmethodssignatureoption

ANY/Polymorphismmissing/Polymorphism

Simulationclass/Thesimulationclassessimulationclasses

about/ThesimulationclassesSimulationclass/ThesimulationclassesDiscreteSimulationclass/ThesimulationclassesContinuousSimulationclass/Thesimulationclasses

simulationmethod/Definingobjectsandinheritance

simulationsmethod/TheMonte-CarloclasssingleSimulationmethod/ThesimulationclassessingleTrialmethod/Definingobjectsandinheritanceskipoption/Fixed-widthfilesslotcommand

about/Miscellaneousnotesslotnamescommand

about/Miscellaneousnotesslotsargument/DefininganS4classsocketConnectioncommand/Networkoptionssocketoperations/Basicsocketoperationsspecialdatatypes

about/Specialdatatypesasfunction/Notesontheasandisfunctionsisfunction/Notesontheasandisfunctions

sprintffunction/Creatingformattedstringsstopcommand/Argumentstofunctionsstorage.modecommand/Theworkspacestrftimecommand/Convertingtimedatatypestostringsstrings

splitting/Splittingstringsconverting,totimedatatypes/Convertingstringstotimedatatypestimedatatypes,convertingto/Convertingtimedatatypestostrings

strptimecommand/Convertingstringstotimedatatypesstrsplitcommand/Splittingstringssubcommand/Regularexpressionssubstring

location/Locationofasubstringextracting/Extractingorchangingasubstringchanging/Extractingorchangingasubstring

substringcommand/ExtractingorchangingasubstringS_PLUSlanguage/Scope

Ttables,datastructure/Tablestapplycommand/tapplytimedatatypes

strings,convertingto/Convertingstringstotimedatatypesconverting,tostrings/Convertingtimedatatypestostringsoperationson/Operationsontimedatatypes

timeformation/Introductionandassumptionstransformcase

about/TransformingthecaseTRUEvalue/Logicaltypeofcommand/Integer

UUseMethodcommand/Definingclassesandmethods,Definingobjectsandinheritance

Vvectors,datastructure/Vectors

Wwarningcommand/Argumentstofunctionswhileloop

versusforloop/Thewhileloopwhileloops

about/Thewhileloopworkspace

about/Theworkspacesaving/Savingaworkspace

writeBincommand/Primitiveinput/outputwriteCharcommand/Primitiveinput/outputwriteLinescommand/Primitiveinput/output

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