The Art of R Programming : A Tour of Statistical Software Design

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 packages for text analysis, image manipulation, and thousands 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. Brief Contents Contents in Detail Acknowledgments Introduction 1: Getting Started 2: Vectors 3: Matrices and Arrays 4: Lists 5: Data Frames 6: Factors and Tables 7: R Programming Structures 8: Doing Math and Simulations in R 9: Object-Oriented Programming 10: Input/Output 11: String Manipulation 12: Graphics 13: Debugging 14: Performance Enhancement: Speed and Memory 15: Interfacing R to Other Languages 16: Parallel R Appendix A: Installing R Appendix B: Installing and Using Packages Index Blank Page A Guide To Software Development Using The R Programming Language Covers Such Topics As Closures, Recursion, Anonymous Functions, And Debugging Techniques. 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. By Norman Matloff. Includes Index. 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. "The Art of 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."--Page 4 de la couverture