book

The Art of R Programming

by Norman Matloff

October 2011

Beginner to intermediate

400 pages

9h 30m

English

No Starch Press

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Why Use R for Your Statistical Work?Object-Oriented ProgrammingFunctional Programming
How to Run RInteractive ModeBatch Mode

Variable ScopeDefault Arguments
Vectors, the R WorkhorseScalarsCharacter StringsMatricesListsData FramesClasses
The help() FunctionThe example() FunctionIf You Don’t Know Quite What You’re Looking ForHelp for Other TopicsHelp for Batch ModeHelp on the Internet
Scalars, Vectors, Arrays, and MatricesAdding and Deleting Vector ElementsObtaining the Length of a VectorMatrices and Arrays as Vectors
Vector Arithmetic and Logical OperationsVector IndexingGenerating Useful Vectors with the : OperatorGenerating Vector Sequences with seq()Repeating Vector Constants with rep()
Extended Example: Finding Runs of Consecutive OnesExtended Example: Predicting Discrete-Valued Time Series
Vector In, Vector OutVector In, Matrix Out
Using NAUsing NULL
Generating Filtering IndicesFiltering with the subset() FunctionThe Selection Function which()
Extended Example: A Measure of AssociationExtended Example: Recoding an Abalone Data Set
Creating Matrices
Performing Linear Algebra Operations on MatricesMatrix IndexingExtended Example: Image ManipulationFiltering on MatricesExtended Example: Generating a Covariance Matrix
Using the apply() FunctionExtended Example: Finding Outliers
Changing the Size of a MatrixExtended Example: Finding the Closest Pair of Vertices in a Graph
Creating Lists
List IndexingAdding and Deleting List ElementsGetting the Size of a ListExtended Example: Text Concordance
Using the lapply() and sapply() FunctionsExtended Example: Text Concordance, ContinuedExtended Example: Back to the Abalone Data
Creating Data FramesAccessing Data FramesExtended Example: Regression Analysis of Exam Grades Continued
Extracting Subdata FramesMore on Treatment of NA ValuesUsing the rbind() and cbind() Functions and AlternativesApplying apply()Extended Example: A Salary Study
Extended Example: An Employee Database
Using lapply() and sapply() on Data FramesExtended Example: Applying Logistic Regression ModelsExtended Example: Aids for Learning Chinese Dialects
Factors and Levels
The tapply() FunctionThe split() FunctionThe by() Function
Matrix/Array-Like Operations on TablesExtended Example: Extracting a SubtableExtended Example: Finding the Largest Cells in a Table
The aggregate() FunctionThe cut() Function
Control StatementsLoopsLooping Over Nonvector Setsif-else
Deciding Whether to Explicitly Call return()Returning Complex Objects
The Top-Level EnvironmentThe Scope HierarchyMore on ls()Functions Have (Almost) No Side EffectsExtended Example: A Function to Display the Contents of a Call Frame
Writing to Nonlocals with the Superassignment OperatorWriting to Nonlocals with assign()Extended Example: Discrete-Event Simulation in RWhen Should You Use Global Variables?Closures
A Quicksort ImplementationExtended Example: A Binary Search Tree
What’s Considered a Replacement Function?Extended Example: A Self-Bookkeeping Vector Class
Text Editors and Integrated Development EnvironmentsThe edit() Function
Math FunctionsExtended Example: Calculating a ProbabilityCumulative Sums and ProductsMinima and MaximaCalculus
Extended Example: Vector Cross ProductExtended Example: Finding Stationary Distributions of Markovv Chains
Built-In Random Variate GeneratorsObtaining the Same Random Stream in Repeated RunsExtended Example: A Combinatorial Simulation
S3 ClassesS3 Generic FunctionsExample: OOP in the lm() Linear Model FunctionFinding the Implementations of Generic MethodsWriting S3 ClassesUsing InheritanceExtended Example: A Class for Storing Upper-Triangular MatricesExtended Example: A Procedure for Polynomial Regression
Writing S4 ClassesImplementing a Generic Function on an S4 Class
Listing Your Objects with the ls() FunctionRemoving Specific Objects with the rm() FunctionSaving a Collection of Objects with the save() Function“What Is This?”The exists() Function
Accessing the Keyboard and MonitorUsing the scan() FunctionUsing the readline() FunctionPrinting to the Screen
Reading a Data Frame or Matrix from a FileReading Text FilesIntroduction to ConnectionsExtended Example: Reading PUMS Census FilesAccessing Files on Remote Machines via URLsWriting to a FileGetting File and Directory InformationExtended Example: Sum the Contents of Many Files
Overview of TCP/IPSockets in RExtended Example: Implementing Parallel R
An Overview of String-Manipulation Functionsgrep()nchar()paste()sprintf()substr()strsplit()regexpr()gregexpr()
Extended Example: Testing a Filename for a Given SuffixExtended Example: Forming Filenames
Creating GraphsThe Workhorse of R Base Graphics: The plot() FunctionAdding Lines: The abline() FunctionStarting a New Graph While Keeping the Old OnesExtended Example: Two Density Estimates on the Same GraphExtended Example: More on the Polynomial Regression ExampleAdding Points: The points() FunctionAdding a Legend: The legend() FunctionAdding Text: The text() FunctionPinpointing Locations: The locator() FunctionRestoring a Plot
Changing Character Sizes: The cex OptionChanging the Range of Axes: The xlim and ylim OptionsAdding a Polygon: The polygon() FunctionSmoothing Points: The lowess() and loess() FunctionsGraphing Explicit FunctionsExtended Example: Magnifying a Portion of a Curve
R Graphics DevicesSaving the Displayed GraphClosing an R Graphics Device
Fundamental Principles of DebuggingThe Essence of Debugging: The Principle of ConfirmationStart SmallDebug in a Modular, Top-Down MannerAntibugging
Single-Stepping with the debug() and browser() FunctionsUsing Browser CommandsSetting BreakpointsCalling browser() DirectlyUsing the setBreakpoint() FunctionTracking with the trace() FunctionPerforming Checks After a Crash with the traceback() and debugger() FunctionExtended Example: Two Full Debugging SessionsDebugging Finding Runs of OnesDebugging Finding City Pairs
Writing Fast R Code
Vectorization for SpeedupExtended Example: Achieving Better Speed in a Monte Carlo SimulationExtended Example: Generating a Powers Matrix
Vector Assignment IssuesCopy-on-Change IssuesExtended Example: Avoiding Memory Copy
Monitoring with Rprof()How Rprof() Works
ChunkingUsing R Packages for Memory Management
Writing C/C++ Functions to Be Called from RSome R-to-C/C++ PreliminariesExample: Extracting Subdiagonals from a Square MatrixCompiling and Running CodeDebugging R/C CodeExtended Example: Prediction of Discrete-Valued Time Series
Installing RPyRPy Syntax
The Mutual Outlinks Problem
Running snow CodeAnalyzing the snow CodeHow Much Speedup Can Be Attained?Extended Example: K-Means Clustering
Using Multicore MachinesExtended Example: Mutual Outlinks Problem in OpenMPRunning the OpenMP CodeOpenMP Code AnalysisOther OpenMP PragmasThe omp barrier PragmaThe omp critical PragmaThe omp single PragmaGPU Programming
Sources of OverheadShared-Memory MachinesNetworked Systems of ComputersEmbarrassingly Parallel Applications and Those That Aren’tStatic Versus Dynamic Task AssignmentSoftware Alchemy: Turning General Problems into Embarrassingly Parallel Ones
Downloading R from CRAN
Package Basics
Installing Packages AutomaticallyInstalling Packages Manually

Overview

R is the world's most popular language for developing statistical software: Archaeologists use it to track the spread of ancient civilizations, drug companies use it to discover which medications are safe and effective, and actuaries use it to assess financial risks and keep economies running smoothly.

The Art of R Programming takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required, and your programming skills can range from hobbyist to pro.

Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats. You'll also learn to:

•Create artful graphs to visualize complex data sets and functions
•Write more efficient code using parallel R and vectorization
•Interface R with C/C++ and Python for increased speed or functionality
•Find new R packages for text analysis, image manipulation, and more
•Squash annoying bugs with advanced debugging techniques

Whether you're designing aircraft, forecasting the weather, or you just need to tame your data, The Art of R Programming is your guide to harnessing the power of statistical computing.