LEADER 05296nam 2200637 a 450 001 9910789721903321 005 20230912122810.0 010 $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(Au-PeEL)EBL1137514 035 $a(CaPaEBR)ebr10513550 035 $a(Au-PeEL)EBL6094356 035 $a(OCoLC)1156011993 035 $a(CaSebORM)9781593273842 035 $a(MiAaPQ)EBC1137514 035 $a(MiAaPQ)EBC6094356 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 $aR (Computer program language) 615 0$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 $a9910789721903321 996 $aThe art of R programming$93856067 997 $aUNINA LEADER 02146nam 2200493z- 450 001 9910341144503321 005 20210709 035 $a(CKB)5340000000058062 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/71170 035 $a(oapen)doab71170 035 $a(EXLCZ)995340000000058062 100 $a20202107d2018 |y 0 101 0 $afre 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aLe Nebelspalter zurichois (1875-1921)$eAu coeur de l'Europe des revues et des arts 210 $aGene?ve$cLibrairie Droz$d2018 215 $a1 online resource 225 1 $aPresse et caricature 311 08$a9782600059121 311 08$a2600059121 330 $aCette monographie constitue une e?tude sur la pe?riode fondatrice de la revue satirique illustre?e suisse Der Nebelspalter du de?but de sa parution, en 1875, jusqu'en 1921, soit durant l'a?ge d'or europe?en des revues. 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