Original Title: The art of R programming: a tour of statistical software design Author: (US) Norman matloff Translator: chen Yan Ping Qiu Yi Xuan Pan LAN Feng Xiong Xi series name: Chapter Hua programmer library Press: Machinery Industry Press ISBN: 9787111423140 mounting time: May 2013 Published Date: Opening: 16 open pages: version 1: Classification of version 1-1: more about computers, r programming art, computer books, r programming art, is a classic book recognized in the r language field, written by a famous computer scientist and statistician, Amazon's five-star best-selling book. It is a pure programming book for R language developers and does not require readers to have a statistical foundation, the data structure, programming structure, syntax, TCP/IP network programming, parallel computing, code debugging, program performance optimization, programming skills and all r programming-related knowledge, such as interfaces of the r language and other languages, almost everything. This book is also very practical. 44 Examples of expansion show the strong capabilities of the r language in data processing and statistical analysis. The book consists of 16 chapters: Chapter 1st describes the preparatory knowledge required to learn the r language and some important data structures of the book; 2nd ~ Chapter 6 describes the main data structures of the r language, including vectors, matrices, arrays, lists, data boxes, and factors ~ Chapter 13 describes the r language syntax, including programming structure, object-oriented features, mathematical operations and simulations, input and output, string processing, plotting, and r language debugging methods. 14th ~ Chapter 16 describes the Advanced content of R programming, such as the improvement of execution speed and performance, and the mixed programming of R and C/C ++ or Python, and parallel computing in the r language. Directory R language programming art. preface by the translator: Chapter 2 Quick Start 1st how to run r11.1.1 interactive mode 11.1.2 batch processing mode 11.1 first r session 21.2 function entry 51.3.1 variable scope 71.3.2 default parameter 81.4 important data structures in the r language 81.4.1 vector, in R Language, The 81.4.2 string 91.4.3 matrix 91.4.4 list 101.4.5 data frame 121.4.6 class 121.5 expansion case: Regression Analysis of Test scores 13.1.6 start and close r161.7 get help 171.7.1 help () function 181.7.2 example () function 181.7.3 if you are not quite sure what to look for other theme help 2nd batch processing mode help 211.7.6 Internet resources 21 Chapter 21 vector 222.1 scalar, vector, array and matrix 222.1.1 add or delete Vectors element 222.1.2 obtains the vector length 232.1.3 as the vector matrix and array 242.2 declaration 242.3 loop completion 252.4 common vector operations 262.4.1 vector operations and logical operations 262.4.2 vector index 272.4.3 use: operator creation vector 282.4.4 use seq () to create vector 282.4.5 use rep () to repeat vector constant 292.5 use all () and any () 302.5.1 extension case: search for a continuous 1 run 302.5.2 extended case: prediction of discrete value time series 312.6 vectoring operator 342.6.1 vector input, vector output 342.6.2 vector input, matrix output 362.7 Na and null value 372.7.1 na use 372.7.2 null use 372.8 filter 382.8.1 generate filter index 382.8.2 use subset () function filter 402.8.3 select function which () 402.9 vectorized ifelse () function 412.9.1 extension case: measurement correlation 422.9.2 extension case: recode the abalone dataset 442.10 test vector equality 462.11 vector element name 472.12 about C () more content 48 Chapter 3rd matrix and array 493.1 create matrix 493.2 general matrix operation 503.2.1 linear algebra operation 503.2.2 matrix index 513.2.3 expansion case: image operation 523.2.4 matrix element filtering 553.2.5 expansion case: generate a covariance matrix 573.3 call function 583.3.1 using the apply () function 583.3.2 extension for the rows and columns of the matrix: look for an example of changing the size of a matrix 613.4.2 by adding or deleting a matrix row or column 613.4.1 with an abnormal value 603.4: find the difference between the closest 633.5 vector and the matrix of an endpoint in the graph. 653.6 avoid unexpected dimensionality reduction. 663.7 naming of rows and columns of the 683.8 matrix. 4th high-dimensional array. 68 chapter 714.1 list. 714.2 create list general Operation 724.2.1 List Index 724.2.2 add or delete list elements 734.2.3 get list length 754.2.4 extended case: text vocabulary index 754.3 access list elements and value 784.4 use the apply series functions 794.4.1 lapply () and sapply () on the list using 794.4.2 extended case: Text vocabulary index (continued) 804.4.3 extended case: abalone data 824.5 recursive list 83 chapter 5th Data box 855.1 create data box 855.1.1 access data box 855.1.2 extended case: Regression Analysis of exam scores (continued) 865.2 use other matrix operations 875.2.1 to extract the missing values from the child data frame 875.2.2 885.2.3 use rbind () and cbind () functions 895.2.4 use apply () 905.2.5 extension case: salary study 905.3 combine data frame 925.4 apply the functions 955.4.1 of the data frame apply the lapply () and sapply () functions 955.4.2 extension cases on the data frame: Apply the Logistic model 955.4.3 extension case: auxiliary Tools for learning Chinese dialects Chapter 96 common functions of factor and table 6th and level 1026.1 factors 1036.2.1 tapply function 1036.2.2 split () function 1056.2.3 () function 1066.3 table Operations Section 6.3.1 operations on Matrices and similar arrays Section 6.3.2 extension case: extraction of child tables section 1116.3.3 extension case: find the cells with the largest frequency in the Table. 1136.4 other functions related to the factor and table: 1146.4.1 aggregate () function 1156.4.2 cut () function 115 Chapter 1 R language programming structure 7th control statement 7.1.1 loop 7.1.2 loop 1197.1.3 if-else structure 1167.1 arithmetic and logical operators and default value of the value 1207.2 parameter 1217.3 return value 1237.4.1 whether to explicitly call return () 1247.4.2 returning complex objects 1247.5 functions are all objects 1247.6 environment and variable scope problems 1277.6.1 top-level environment 1277.6.2 variable scope levels 1287.6.3 further discussion of LS () 1317.6.4 function (almost) no side effects 1317.6.5 extension case: show the call box function 1327.7 in the r language no pointer 1347.8 write to the upper-level 1367.8.1 write non-local variables using the superassign operator 1367.8.2 use assign () function write operation 1377.8.3 extended case: Use the r language to simulate discrete events 1387.8.4 when to use the global variable 1457.8.5 closure 1477.9 recursion 1487.9.1 specific implementation of quicksort 1497.9.2 extended example: binary Search Tree 1507.10 replacement function 1557.10.1 what is the replacement function 1557.10.2 extension case: Vector class that records the number of element modifications 1567.11 Function Code Writing Tool 1587.11.1 text editor and integrated development environment 1587.11.2 edit () function 1587.12 create your own binary operators 1597.13 anonymous functions 159 chapter 8th mathematical operations and simulation 1618.1 mathematical functions 1618.1.1 extension example: calculate the probability 1618.1.2 cumulative and cumulative product 1628.1.3 minimum and maximum 1628.1.4 calculus 1638.2 Statistical Distribution Function 1648.3 sort 1658.4 linear algebra of vectors and matrices 1668.4.1 extension example: vector Cross Product 1698.4.2 extension example: determine the stable distribution of Markov Chains 1708.5 set operation 1718.6 use R for simulation 1738.6.1 built-in random variable generator 1738.6.2 get the same random number stream 1758.6.3 expansion case: combined simulation Chapter 1 Object-Oriented Programming 175 S3 class 1779.1.1 S3 generic function 1779.1.2 instance: linear model function LM () in oop1789.1.3 find the implementation method of generic functions 1799.1.4 write S3 class 1819.1.5 use inheritance 1829.1.6 extension example: used
