• The Art of R Programming: A Tour of Statistical Software Design See large image

    The Art of R Programming: A Tour of Statistical Software Design (Paperback) By (author) Norman Matloff

    $26.46 - Save $15.37 36% off - RRP $41.83 Free delivery worldwide Available
    Dispatched in 3 business days
    When will my order arrive?
    Add to basket | Add to wishlist |

    DescriptionR 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.


Other books

Other people who viewed this bought | Other books in this category
Showing items 1 to 10 of 10

 

Reviews | Bibliographic data
  • Full bibliographic data for The Art of R Programming: A Tour of Statistical Software Design

    Title
    The Art of R Programming: A Tour of Statistical Software Design
    Authors and contributors
    By (author) Norman Matloff
    Physical properties
    Format: Paperback
    Number of pages: 400
    Width: 182 mm
    Height: 234 mm
    Thickness: 26 mm
    Weight: 764 g
    Language
    English
    ISBN
    ISBN 13: 9781593273842
    ISBN 10: 1593273843
    Classifications

    BIC E4L: COM
    Nielsen BookScan Product Class 3: S10.2
    B&T Book Type: NF
    B&T Modifier: Region of Publication: 01
    B&T Modifier: Subject Development: 20
    B&T Modifier: Academic Level: 05
    B&T General Subject: 229
    B&T Modifier: Text Format: 01
    BIC subject category V2: UMX
    DC22: 005.133
    B&T Approval Code: A93170000
    B&T Merchandise Category: COM
    BISAC V2.8: MAT029000
    LC subject heading:
    Ingram Subject Code: XX
    BISAC V2.8: COM051010
    Warengruppen-Systematik des deutschen Buchhandels: 16330
    B&T Approval Code: A93603200
    Libri: PROG5000
    BISAC V2.8: COM077000
    LC subject heading: ,
    DC22: 519.50285513, 519.50285/5133
    LC classification: QA276.4 .M2925 2011
    Thema V1.0: PBT, UMX
    Illustrations note
    black & white tables, figures
    Publisher
    No Starch Press,US
    Imprint name
    No Starch Press,US
    Publication date
    01 December 2011
    Publication City/Country
    Daly City, California
    Author Information
    Norman Matloff, Ph.D., is a Professor of Computer Science at the University of California, Davis. He is the creator of several popular software packages, as well as a number of widely-used Web tutorials on computer topics. He has written articles for the "New York Times," the "Washington Post," "Forbes Magazine," the "San Francisco Chronicle," and the "Los Angeles Times," among others, and is also the author, with Peter Jay Salzman, of "The Art of Debugging" (No Starch Press).
    Table of contents
    Acknowledgments; Introduction; Why Use R for Your Statistical Work?; Whom Is This Book For?; My Own Background; Chapter 1: Getting Started; 1.1 How to Run R; 1.2 A First R Session; 1.3 Introduction to Functions; 1.4 Preview of Some Important R Data Structures; 1.5 Extended Example: Regression Analysis of Exam Grades; 1.6 Startup and Shutdown; 1.7 Getting Help; Chapter 2: Vectors; 2.1 Scalars, Vectors, Arrays, and Matrices; 2.2 Declarations; 2.3 Recycling; 2.4 Common Vector Operations; 2.5 Using all() and any(); 2.6 Vectorized Operations; 2.7 NA and NULL Values; 2.8 Filtering; 2.9 A Vectorized if-then-else: The ifelse() Function; 2.10 Testing Vector Equality; 2.11 Vector Element Names; 2.12 More on c(); Chapter 3: Matrices and Arrays; 3.1 Creating Matrices; 3.2 General Matrix Operations; 3.3 Applying Functions to Matrix Rows and Columns; 3.4 Adding and Deleting Matrix Rows and Columns; 3.5 More on the Vector/Matrix Distinction; 3.6 Avoiding Unintended Dimension Reduction; 3.7 Naming Matrix Rows and Columns; 3.8 Higher-Dimensional Arrays; Chapter 4: Lists; 4.1 Creating Lists; 4.2 General List Operations; 4.3 Accessing List Components and Values; 4.4 Applying Functions to Lists; 4.5 Recursive Lists; Chapter 5: Data Frames; 5.1 Creating Data Frames; 5.2 Other Matrix-Like Operations; 5.3 Merging Data Frames; 5.4 Applying Functions to Data Frames; Chapter 6: Factors and Tables; 6.1 Factors and Levels; 6.2 Common Functions Used with Factors; 6.3 Working with Tables; 6.4 Other Factor- and Table-Related Functions; Chapter 7: R Programming Structures; 7.1 Control Statements; 7.2 Arithmetic and Boolean Operators and Values; 7.3 Default Values for Arguments; 7.4 Return Values; 7.5 Functions Are Objects; 7.6 Environment and Scope Issues; 7.7 No Pointers in R; 7.8 Writing Upstairs; 7.9 Recursion; 7.10 Replacement Functions; 7.11 Tools for Composing Function Code; 7.12 Writing Your Own Binary Operations; 7.13 Anonymous Functions; Chapter 8: Doing Math and Simulations in R; 8.1 Math Functions; 8.2 Functions for Statistical Distributions; 8.3 Sorting; 8.4 Linear Algebra Operations on Vectors and Matrices; 8.5 Set Operations; 8.6 Simulation Programming in R; Chapter 9: Object-Oriented Programming; 9.1 S3 Classes; 9.2 S4 Classes; 9.3 S3 Versus S4; 9.4 Managing Your Objects; Chapter 10: Input/Output; 10.1 Accessing the Keyboard and Monitor; 10.2 Reading and Writing Files; 10.3 Accessing the Internet; Chapter 11: String Manipulation; 11.1 An Overview of String-Manipulation Functions; 11.2 Regular Expressions; 11.3 Use of String Utilities in the edtdbg Debugging Tool; Chapter 12: Graphics; 12.1 Creating Graphs; 12.2 Customizing Graphs; 12.3 Saving Graphs to Files; 12.4 Creating Three-Dimensional Plots; Chapter 13: Debugging; 13.1 Fundamental Principles of Debugging; 13.2 Why Use a Debugging Tool?; 13.3 Using R Debugging Facilities; 13.4 Moving Up in the World: More Convenient Debugging Tools; 13.5 Ensuring Consistency in Debugging Simulation Code; 13.6 Syntax and Runtime Errors; 13.7 Running GDB on R Itself; Chapter 14: Performance Enhancement: Speed and Memory; 14.1 Writing Fast R Code; 14.2 The Dreaded for Loop; 14.3 Functional Programming and Memory Issues; 14.4 Using Rprof() to Find Slow Spots in Your Code; 14.5 Byte Code Compilation; 14.6 Oh No, the Data Doesn't Fit into Memory!; Chapter 15: Interfacing R to Other Languages; 15.1 Writing C/C++ Functions to Be Called from R; 15.2 Using R from Python; Chapter 16: Parallel R; 16.1 The Mutual Outlinks Problem; 16.2 Introducing the snow Package; 16.3 Resorting to C; 16.4 General Performance Considerations; 16.5 Debugging Parallel R Code; Installing R; Downloading R from CRAN; Installing from a Linux Package Manager; Installing from Source; Installing and Using Packages; Package Basics; Loading a Package from Your Hard Drive; Downloading a Package from the Web; Listing the Functions in a Package; Colophon;