book

Efficient R Programming

by Colin Gillespie, Robin Lovelace

December 2016

Intermediate to advanced

222 pages

5h 20m

English

O'Reilly Media, Inc.

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Conventions Used in This BookUsing Code ExamplesO’Reilly SafariHow to Contact UsAcknowledgmentsColinRobin
PrerequisitesWho This Book Is for and How to Use ItWhat Is Efficiency?What Is Efficient R Programming?Why Efficiency?Cross-Transferable Skills for EfficiencyTouch TypingConsistent Style and Code ConventionsBenchmarking and ProfilingBenchmarkingBenchmarking ExampleProfilingBook ResourcesR PackageOnline VersionReferences
PrerequisitesTop Five Tips for an Efficient R SetupOperating SystemOperating System and Resource MonitoringR VersionInstalling RUpdating RInstalling R PackagesInstalling R Packages with DependenciesUpdating R PackagesR StartupR Startup ArgumentsAn Overview of R’s Startup FilesThe Location of Startup FilesThe .Rprofile FileExample .Rprofile FileThe .Renviron FileRStudioInstalling and Updating RStudioWindow Pane LayoutRStudio OptionsAutocompletionKeyboard ShortcutsObject Display and Output TableProject ManagementBLAS and Alternative R InterpretersTesting Performance Gains from BLASOther InterpretersUseful BLAS/Benchmarking ResourcesReferences
PrerequisitesTop Five Tips for Efficient ProgrammingGeneral AdviceMemory AllocationVectorized CodeCommunicating with the UserFatal Errors: stop()Warnings: warning()Informative Output: message() and cat()Invisible ReturnsFactorsInherent OrderFixed Set of CategoriesThe Apply FamilyExample: Movies DatasetType ConsistencyCaching VariablesFunction ClosuresThe Byte CompilerExample: The Mean FunctionCompiling CodeReferences
PrerequisitesTop Five Tips for Efficient WorkflowA Project Planning TypologyProject Planning and ManagementChunking Your WorkMaking Your Workflow SMARTVisualizing Plans with RPackage SelectionSearching for R PackagesHow to Select a PackagePublicationDynamic Documents with R MarkdownR PackagesReference
PrerequisitesTop Five Tips for Efficient Data I/OVersatile Data Import with rioPlain-Text FormatsDifferences Between fread() and read_csv()Preprocessing Text Outside RBinary File FormatsNative Binary Formats: Rdata or Rds?The Feather File FormatBenchmarking Binary File FormatsProtocol BuffersGetting Data from the InternetAccessing Data Stored in PackagesReferences
PrerequisitesTop Five Tips for Efficient Data CarpentryEfficient Data Frames with tibbleTidying Data with tidyr and Regular ExpressionsMake Wide Tables Long with gather()Split Joint Variables with separate()Other tidyr FunctionsRegular ExpressionsEfficient Data Processing with dplyrRenaming ColumnsChanging Column ClassesFiltering RowsChaining OperationsData AggregationNonstandard EvaluationCombining DatasetsWorking with DatabasesDatabases and dplyrData Processing with data.tableReferences
PrerequisitesTop Five Tips for Efficient OptimizationCode ProfilingGetting Started with profvisExample: Monopoly SimulationEfficient Base RThe if() Versus ifelse() FunctionsSorting and OrderingReversing ElementsWhich Indices are TRUE?Converting Factors to NumericsLogical AND and ORRow and Column Operationsis.na() and anyNA()MatricesExample: Optimizing the move_square() FunctionParallel ComputingParallel Versions of Apply FunctionsExample: Snakes and LaddersExit Functions with CareParallel Code under Linux and OS XRcppA Simple C++ FunctionThe cppFunction() CommandC++ Data TypesThe sourceCpp() FunctionVectors and LoopsMatricesC++ with Sugar on TopRcpp ResourcesReferences
PrerequisitesTop Five Tips for Efficient HardwareBackground: What Is a Byte?Random Access MemoryHard Drives: HDD Versus SSDOperating Systems: 32-Bit or 64-BitCentral Processing UnitCloud ComputingAmazon EC2
PrerequisitesTop Five Tips for Efficient CollaborationCoding StyleReformatting Code with RStudioFilenamesLoading PackagesCommentingObject NamesExample PackageAssignmentSpacingIndentationCurly BracesVersion ControlCommitsGit Integration in RStudioGitHubBranches, Forks, Pulls, and ClonesCode ReviewReferences

PrerequistiesTop Five Tips for Efficient LearningUsing R’s Internal HelpSearching R for TopicsFinding and Using VignettesGetting Help on FunctionsReading R Source CodeswirlOnline ResourcesStack OverflowMailing Lists and GroupsAsking a QuestionMinimal DatasetMinimal ExampleLearning In DepthSpread the KnowledgeReferences

Content preview from Efficient R Programming

Chapter 8. Efficient Hardware

This chapter is odd for a book on R programming. It contains very little code, and yet the chapter has the potential to speed up your algorithms by orders of magnitude. This chapter considers the impact that your computer has on your time.

Your hardware is crucial. It will not only determine how fast you can solve your problem, but also whether you can even tackle the problem of interest. This is because everything is loaded in RAM. Of course, having a more powerful computer costs money. The goal is to help you decide whether the benefits of upgrading your hardware are worth that extra cost.

We begin this chapter with a background section on computer storage and memory and how it is measured. Then we consider individual computer components, and conclude with renting machines in the cloud.

Prerequisites

This chapter will focus on assessing your hardware and the benefit of upgrading. We will use the benchmarkme package to quantify the effect of changing your CPU.

library("benchmarkme")

Top Five Tips for Efficient Hardware

Use the package benchmarkme to assess your CPU’s number-crunching ability; is it worth upgrading your hardware?
If possible, add more RAM.
Double-check that you have installed a 64-bit version of R.
Cloud computing is a cost-effective way of obtaining more computer power.
Solid-state drives typically won’t have much impact on the speed of your R code but will increase your overall productivity because I/0 is much faster. ...