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The art of R programming : tour of statistical software design /

by Matloff, Norman S.
Publisher: San Francisco : No Starch Press, c2011Description: xxiii, 373 p. : ill. ; 24 cm.ISBN: 9781593273842.Subject(s): Statistics -- Data processing | R (Computer program language)
Contents:
Introduction -- Why use R for your statistical work? -- Whom is this book for? -- My own background -- Getting started -- How to run R -- A first R session -- Introduction to functions -- Preview of some important R data structures -- Extended example: regression analysis of exam grades -- Startup and shutdown -- Getting help -- Vectors -- Scalars, vectors, arrays, and matrices -- Declarations -- Recycling -- Common vector operations -- Using all() and any() -- Vectorized operations -- NA and NULL values -- Filtering -- A vectorized if-then-else: the ifelse() function -- Testing vector equality -- Vector element names -- More on c() -- Matrices and arrays -- Creating matrices -- General matrix operations -- Applying functions to matrix rows and columns -- Adding and deleting matrix rows and columns -- More on the vector/matrix distinction -- Avoiding unintended dimension reduction -- Naming matrix rows and columns -- Higher-dimensional arrays -- Lists -- Creating lists -- General list operations -- Accessing list components and values -- Applying functions to lists -- Recursive lists -- Data frames -- Creating data frames -- Other matrix-like operations -- Merging data frames -- Applying functions to data frames -- Factors and tables -- Factors and levels -- Common functions used with factors -- Working with tables -- Other factor and table-related functions -- R programming structures -- Control statements -- Arithmetic and Boolean operators and values -- Default values for arguments -- Return values -- Functions are objects -- Environment and scope issues -- No pointers in R -- Writing upstairs -- Recursion -- Replacement functions -- Tools for composing function code -- Writing your own binary operations -- Anonymous functions -- Doing math and simulations in R -- Math functions -- Functions for statistical distributions -- Sorting -- Linear algebra operations on vectors and matrices -- Set operations -- Simulation programming in R --
Object-oriented programming -- S3 classes -- S4 classes -- S3 versus S4 -- Managing your objects -- Input/output -- Accessing the keyboard and monitor -- Reading and writing files -- Accessing the Internet -- String manipulation -- An overview of string-manipulation functions -- Regular expressions -- Use of string utilities in the edtdbg debugging tool -- Graphics -- Creating graphs -- Customizing graphs -- Saving graphs to files -- Creating three-dimensional plots -- Debugging -- Fundamental principles of debugging -- Why use a debugging tool? -- Using R debugging facilities -- Moving up in the world: more convenient debugging tools -- Ensuring consistency in debugging simulation code -- Syntax and runtime errors -- Running GDB on R itself -- Performance enhancement: speed and memory -- Writing fast R code -- The dreaded for loop -- Functional programming and memory issues -- Using Rprof() to find slow spots in your code -- Byte code compilation -- Oh no, the data doesn't fit into memory! -- Interfacing R to other languages -- Writing C/C++ functions to be called from R -- Using R from Python -- Parallel R -- The mutual outlinks problem -- Introducing the snow package -- Resorting to C -- General performance considerations -- 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.
Summary: A guide to software development using the R programming language covers such topics as closures, recursion, anonymous functions, and debugging techniques.
Item type Location Call number Copy Status Date due
BOOK BOOK Mesa Lab QA276.4 .M2925 2011 (Browse shelf) 1 Checked out 02/07/2014

Includes index.

Introduction -- Why use R for your statistical work? -- Whom is this book for? -- My own background -- Getting started -- How to run R -- A first R session -- Introduction to functions -- Preview of some important R data structures -- Extended example: regression analysis of exam grades -- Startup and shutdown -- Getting help -- Vectors -- Scalars, vectors, arrays, and matrices -- Declarations -- Recycling -- Common vector operations -- Using all() and any() -- Vectorized operations -- NA and NULL values -- Filtering -- A vectorized if-then-else: the ifelse() function -- Testing vector equality -- Vector element names -- More on c() -- Matrices and arrays -- Creating matrices -- General matrix operations -- Applying functions to matrix rows and columns -- Adding and deleting matrix rows and columns -- More on the vector/matrix distinction -- Avoiding unintended dimension reduction -- Naming matrix rows and columns -- Higher-dimensional arrays -- Lists -- Creating lists -- General list operations -- Accessing list components and values -- Applying functions to lists -- Recursive lists -- Data frames -- Creating data frames -- Other matrix-like operations -- Merging data frames -- Applying functions to data frames -- Factors and tables -- Factors and levels -- Common functions used with factors -- Working with tables -- Other factor and table-related functions -- R programming structures -- Control statements -- Arithmetic and Boolean operators and values -- Default values for arguments -- Return values -- Functions are objects -- Environment and scope issues -- No pointers in R -- Writing upstairs -- Recursion -- Replacement functions -- Tools for composing function code -- Writing your own binary operations -- Anonymous functions -- Doing math and simulations in R -- Math functions -- Functions for statistical distributions -- Sorting -- Linear algebra operations on vectors and matrices -- Set operations -- Simulation programming in R --

Object-oriented programming -- S3 classes -- S4 classes -- S3 versus S4 -- Managing your objects -- Input/output -- Accessing the keyboard and monitor -- Reading and writing files -- Accessing the Internet -- String manipulation -- An overview of string-manipulation functions -- Regular expressions -- Use of string utilities in the edtdbg debugging tool -- Graphics -- Creating graphs -- Customizing graphs -- Saving graphs to files -- Creating three-dimensional plots -- Debugging -- Fundamental principles of debugging -- Why use a debugging tool? -- Using R debugging facilities -- Moving up in the world: more convenient debugging tools -- Ensuring consistency in debugging simulation code -- Syntax and runtime errors -- Running GDB on R itself -- Performance enhancement: speed and memory -- Writing fast R code -- The dreaded for loop -- Functional programming and memory issues -- Using Rprof() to find slow spots in your code -- Byte code compilation -- Oh no, the data doesn't fit into memory! -- Interfacing R to other languages -- Writing C/C++ functions to be called from R -- Using R from Python -- Parallel R -- The mutual outlinks problem -- Introducing the snow package -- Resorting to C -- General performance considerations -- 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.

A guide to software development using the R programming language covers such topics as closures, recursion, anonymous functions, and debugging techniques.

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