book

The Art of R Programming

Name: The Art of R Programming
Author: Norman Matloff
ISBN: 9781593273842

by Norman Matloff

October 2011

Beginner to intermediate

400 pages

9h 30m

English

No Starch Press

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Cover Page
Title Page
Copyright Page
BRIEF CONTENTS
CONTENTS IN DETAIL
Acknowledgments
Introduction
Why Use R for Your Statistical Work?Object-Oriented ProgrammingFunctional Programming
Whom Is This Book For?
My Own Background
1. Getting Started
How to Run RInteractive ModeBatch Mode

A First R Session
Introduction to Functions
Variable ScopeDefault Arguments
Preview of Some Important R Data Structures
Vectors, the R WorkhorseScalarsCharacter StringsMatricesListsData FramesClasses
Extended Example: Regression Analysis of Exam Grades
Startup and Shutdown
Getting Help
The help() FunctionThe example() FunctionIf You Don’t Know Quite What You’re Looking ForHelp for Other TopicsHelp for Batch ModeHelp on the Internet
2. Vectors
Scalars, Vectors, Arrays, and MatricesAdding and Deleting Vector ElementsObtaining the Length of a VectorMatrices and Arrays as Vectors
Declarations
Recycling
Common Vector Operations
Vector Arithmetic and Logical OperationsVector IndexingGenerating Useful Vectors with the : OperatorGenerating Vector Sequences with seq()Repeating Vector Constants with rep()
Using all() and any()
Extended Example: Finding Runs of Consecutive OnesExtended Example: Predicting Discrete-Valued Time Series
Vectorized Operations
Vector In, Vector OutVector In, Matrix Out
NA and NULL Values
Using NAUsing NULL
Filtering
Generating Filtering IndicesFiltering with the subset() FunctionThe Selection Function which()
A Vectorized if-then-else: The ifelse() Function
Extended Example: A Measure of AssociationExtended Example: Recoding an Abalone Data Set
Testing Vector Equality
Vector Element Names
More on c()
3. Matrices and Arrays
Creating Matrices
General Matrix Operations
Performing Linear Algebra Operations on MatricesMatrix IndexingExtended Example: Image ManipulationFiltering on MatricesExtended Example: Generating a Covariance Matrix
Applying Functions to Matrix Rows and Columns
Using the apply() FunctionExtended Example: Finding Outliers
Adding and Deleting Matrix Rows and Columns
Changing the Size of a MatrixExtended Example: Finding the Closest Pair of Vertices in a Graph
More on the Vector/Matrix Distinction
Avoiding Unintended Dimension Reduction
Naming Matrix Rows and Columns
Higher-Dimensional Arrays
4. Lists
Creating Lists
General List Operations
List IndexingAdding and Deleting List ElementsGetting the Size of a ListExtended Example: Text Concordance
Accessing List Components and Values
Applying Functions to Lists
Using the lapply() and sapply() FunctionsExtended Example: Text Concordance, ContinuedExtended Example: Back to the Abalone Data
Recursive Lists
5. Data Frames
Creating Data FramesAccessing Data FramesExtended Example: Regression Analysis of Exam Grades Continued
Other Matrix-Like Operations
Extracting Subdata FramesMore on Treatment of NA ValuesUsing the rbind() and cbind() Functions and AlternativesApplying apply()Extended Example: A Salary Study
Merging Data Frames
Extended Example: An Employee Database
Applying Functions to Data Frames
Using lapply() and sapply() on Data FramesExtended Example: Applying Logistic Regression ModelsExtended Example: Aids for Learning Chinese Dialects
6. Factors and Tables
Factors and Levels
Common Functions Used with Factors
The tapply() FunctionThe split() FunctionThe by() Function
Working with Tables
Matrix/Array-Like Operations on TablesExtended Example: Extracting a SubtableExtended Example: Finding the Largest Cells in a Table
Other Factor- and Table-Related Functions
The aggregate() FunctionThe cut() Function
7. R Programming Structures
Control StatementsLoopsLooping Over Nonvector Setsif-else
Arithmetic and Boolean Operators and Values
Default Values for Arguments
Return Values
Deciding Whether to Explicitly Call return()Returning Complex Objects
Functions Are Objects
Environment and Scope Issues
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
No Pointers in R
Writing Upstairs
Writing to Nonlocals with the Superassignment OperatorWriting to Nonlocals with assign()Extended Example: Discrete-Event Simulation in RWhen Should You Use Global Variables?Closures
Recursion
A Quicksort ImplementationExtended Example: A Binary Search Tree
Replacement Functions
What’s Considered a Replacement Function?Extended Example: A Self-Bookkeeping Vector Class
Tools for Composing Function Code
Text Editors and Integrated Development EnvironmentsThe edit() Function
Writing Your Own Binary Operations
Anonymous Functions
8. Doing Math and Simulations in R
Math FunctionsExtended Example: Calculating a ProbabilityCumulative Sums and ProductsMinima and MaximaCalculus
Functions for Statistical Distributions
Sorting
Linear Algebra Operations on Vectors and Matrices
Extended Example: Vector Cross ProductExtended Example: Finding Stationary Distributions of Markovv Chains
Set Operations
Simulation Programming in R
Built-In Random Variate GeneratorsObtaining the Same Random Stream in Repeated RunsExtended Example: A Combinatorial Simulation
9. Object-Oriented Programming
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
S4 Classes
Writing S4 ClassesImplementing a Generic Function on an S4 Class
S3 Versus S4
Managing Your Objects
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
10. Input/Output
Accessing the Keyboard and MonitorUsing the scan() FunctionUsing the readline() FunctionPrinting to the Screen
Reading and Writing Files
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
Accessing the Internet
Overview of TCP/IPSockets in RExtended Example: Implementing Parallel R
11. String Manipulation
An Overview of String-Manipulation Functionsgrep()nchar()paste()sprintf()substr()strsplit()regexpr()gregexpr()
Regular Expressions
Extended Example: Testing a Filename for a Given SuffixExtended Example: Forming Filenames
Use of String Utilities in the edtdbg Debugging Tool
12. Graphics
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
Customizing Graphs
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
Saving Graphs to Files
R Graphics DevicesSaving the Displayed GraphClosing an R Graphics Device
Creating Three-Dimensional Plots
13. Debugging
Fundamental Principles of DebuggingThe Essence of Debugging: The Principle of ConfirmationStart SmallDebug in a Modular, Top-Down MannerAntibugging
Why Use a Debugging Tool?
Using R Debugging Facilities
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
Moving Up in the World: More Convenient Debugging Tools
Ensuring Consistency in Debugging Simulation Code
Syntax and Runtime Errors
Running GDB on R Itself
14. Performance Enhancement: Speed and Memory
Writing Fast R Code
The Dreaded for Loop
Vectorization for SpeedupExtended Example: Achieving Better Speed in a Monte Carlo SimulationExtended Example: Generating a Powers Matrix
Functional Programming and Memory Issues
Vector Assignment IssuesCopy-on-Change IssuesExtended Example: Avoiding Memory Copy
Using Rprof() to Find Slow Spots in Your Code
Monitoring with Rprof()How Rprof() Works
Byte Code Compilation
Oh No, the Data Doesn’t Fit into Memory!
ChunkingUsing R Packages for Memory Management
15. Interfacing R to Other Languages
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
Using R from Python
Installing RPyRPy Syntax
16. Parallel R
The Mutual Outlinks Problem
Introducing the snow Package
Running snow CodeAnalyzing the snow CodeHow Much Speedup Can Be Attained?Extended Example: K-Means Clustering
Resorting to C
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
General Performance Considerations
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
Debugging Parallel R Code
A. Installing R
Downloading R from CRAN
Installing from a Linux Package Manager
Installing from Source
B. Installing and Using Packages
Package Basics
Loading a Package from Your Hard Drive
Downloading a Package from the Web
Installing Packages AutomaticallyInstalling Packages Manually
Listing the Functions in a Package
Index
Colophon
About the Author

Content preview from The Art of R Programming

General Performance Considerations

This section discusses some issues that you may find generally useful in parallelizing R applications. I’ll present some material on the main sources of overhead and then discuss a couple of algorithmic issues.

Sources of Overhead

Having at least a rough idea of the physical causes of overhead is essential to successful parallel programming. Let’s take a look at these in the contexts of the two main platforms, shared-memory and networked computers.

Shared-Memory Machines

As noted earlier, the memory sharing in multicore machines makes for easier programming. However, the sharing also produces overhead, since the two cores will bump into each other if they both try to access memory at the same time. This means that ...

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