LEADER 05330nam 2200649 a 450 001 9910461836203321 005 20200520144314.0 010 $a1-59327-410-6 035 $a(CKB)2670000000120896 035 $a(EBL)1137514 035 $a(OCoLC)830164515 035 $a(SSID)ssj0000571686 035 $a(PQKBManifestationID)12230734 035 $a(PQKBTitleCode)TC0000571686 035 $a(PQKBWorkID)10630305 035 $a(PQKB)11639495 035 $a(MiAaPQ)EBC1137514 035 $a(CaSebORM)9781593273842 035 $a(MiAaPQ)EBC6094356 035 $a(Au-PeEL)EBL1137514 035 $a(CaPaEBR)ebr10513550 035 $a(Au-PeEL)EBL6094356 035 $a(OCoLC)1156011993 035 $a(EXLCZ)992670000000120896 100 $a20110719d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 14$aThe art of R programming$b[electronic resource] $ea tour of statistical software design /$fby Norman Matloff 205 $a1st edition 210 $aSan Francisco $cNo Starch Press$d2011 215 $a1 online resource (404 p.) 300 $aIncludes index. 311 $a1-59327-384-3 327 $aBrief Contents; Contents in Detail; Acknowledgments; Introduction; Why Use R for Your Statistical Work?; Whom Is This Book For?; My Own Background; 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; 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 327 $a2.8 Filtering2.9 A Vectorized if-then-else: The ifelse() Function; 2.10 Testing Vector Equality; 2.11 Vector Element Names; 2.12 More on c(); 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; 4: Lists; 4.1 Creating Lists; 4.2 General List Operations; 4.3 Accessing List Components and Values 327 $a4.4 Applying Functions to Lists4.5 Recursive Lists; 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; 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; 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 327 $a7.8 Writing Upstairs7.9 Recursion; 7.10 Replacement Functions; 7.11 Tools for Composing Function Code; 7.12 Writing Your Own Binary Operations; 7.13 Anonymous Functions; 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; 9: Object-Oriented Programming; 9.1 S3 Classes; 9.2 S4 Classes; 9.3 S3 Versus S4; 9.4 Managing Your Objects; 10: Input/Output; 10.1 Accessing the Keyboard and Monitor; 10.2 Reading and Writing Files 327 $a10.3 Accessing the Internet11: 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; 12: Graphics; 12.1 Creating Graphs; 12.2 Customizing Graphs; 12.3 Saving Graphs to Files; 12.4 Creating Three-Dimensional Plots; 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 DebuggingTools; 13.5 Ensuring Consistency in Debugging Simulation Code; 13.6 Syntax and Runtime Errors 327 $a13.7 Running GDB on R Itself 330 $aR is the world's most popular programming language for statistical computing. Drug developers use it to evaluate clinical trials and determine which medications are safe and effective; archaeologists use it to sift through mounds of artifacts and track the spread of ancient civilizations; and actuaries use it to assess financial risks and keep economies running smoothly. In The Art of R Programming , veteran author Norman Matloff takes readers on a guided tour of this powerful language, from basic object types and data structures to graphing, parallel processing, and much more. Along the way, 606 $aStatistics$xData processing 606 $aR (Computer program language) 608 $aElectronic books. 615 0$aStatistics$xData processing. 615 0$aR (Computer program language) 676 $a519.50285/5133 700 $aMatloff$b Norman S$0517467 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910461836203321 996 $aThe art of R programming$92464038 997 $aUNINA