Learning Modular Java Programming

571 Pages·2016·8.721 MB·english

by  Kun Ren

Learning R Programming
Become an efficient data scientist with R
Kun Ren
Learning R Programming
Copyright © 2016 Packt Publishing

First published: October 2016
Production reference: 1181016

Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK.
ISBN 978-1-78588-977-6
www.packtpub.com
Contents
Preface
1
Chapter 1: Quick Start
7
Introducing R 7
R as a programming language 8
R as a computing environment 8
R as a community 9
R as an ecosystem 9
The need for R 10
Installing R 12
RStudio 15
RStudio's user interface 16
The console 16
The editor 17
The Environment pane 18
The History pane 19
The File pane 19
The Plots pane 20
The Packages pane 20
The Help pane 21
The Viewer pane 22
RStudio Server 23
A quick example 23
Summary 25
Chapter 2: Basic Objects
26
Vector 27
Numeric vector 27
Logical vector 29
Character vector 30
Subsetting vectors 32
Named vectors 34
Extracting an element 36
Telling the class of vectors 37
Converting vectors 37
Arithmetic operators for numeric vectors 39
Matrix 40
Creating a matrix 40
Naming rows and columns 41
Subsetting a matrix 41
Using matrix operators 43
Array 45
Creating an array 45
Subsetting an array 46
Lists 47
Creating a list 47
Extracting an element from a list 48
Subsetting a list 49
Named lists 50
Setting values 50
Other functions 52
Data frames 53
Creating a data frame 53
Naming rows and columns 54
Subsetting a data frame 55
Subsetting a data frame as a list 55
Subsetting a data frame as a matrix 56
Filtering data 58
Setting values 59
Setting values as a list 59
Setting values as a matrix 60
Factors 60
Useful functions for data frames 61
Loading and writing data on disk 63
Functions 64
Creating a function 65
Calling a function 65
Dynamic typing 65
Generalizing a function 66
Default value for function arguments 69
Summary 69
Chapter 3: Managing Your Workspace
70
R's working directory 70
Creating an R project in RStudio 71
Comparing absolute and relative paths 72
Managing project files 73
Inspecting the environment 74
[ ii ]
Inspecting existing symbols 75
Viewing the structure of an object 76
Removing symbols 79
Modifying global options 81
Modifying the number of digits to print 81
Modifying the warning level 82
Managing the library of packages 84
Getting to know a package 85
Installing packages from CRAN 86
Updating packages from CRAN 87
Installing packages from online repositories 88
Using package functions 89
Masking and name conflicts 92
Checking whether a package is installed 94
Summary 95
Chapter 4: Basic Expressions
96
Assignment expressions 96
Alternative assignment operators 97
Using backticks with non-standard names 100
Conditional expressions 103
Using if as a statement 104
Using if as an expression 108
Using if with vectors 110
Using vectorized if: ifelse 112
Using switch to branch values 113
Loop expressions 115
Using the for loop 115
Managing the flow of a for loop 118
Creating nested for loops 120
Using the while loop 121
Summary 122
Chapter 5: Working with Basic Objects
124
Using object functions 124
Testing object types 125
Accessing object classes and types 128
Accessing data dimensions 129
Getting data dimensions 130
Reshaping data structures 131
Iterating over one dimension 133
[ iii ]
Using logical functions 133
Logical operators 134
Logical functions 135
Aggregating logical vectors 136
Asking which elements are TRUE 137
Dealing with missing values 138
Logical coercion 140
Using math functions 141
Basic functions 141
Number rounding functions 142
Trigonometric functions 143
Hyperbolic functions 144
Extreme functions 145
Applying numeric methods 147
Root finding 147
Calculus 152
Derivatives 152
Integration 153
Using statistical functions 153
Sampling from a vector 154
Working with random distributions 155
Computing summary statistics 157
Computing covariance and correlation matrix 159
Using apply-family functions 161
lapply 162
sapply 163
vapply 164
mapply 165
apply 166
Summary 167
Chapter 6: Working with Strings
168
Getting started with strings 168
Printing texts 169
Concatenating strings 172
Transforming texts 173
Changing cases 173
Counting characters 174
Trimming leading and trailing whitespaces 175
Substring 176
Splitting texts 177
Formatting texts 178
[ iv ]
Using Python string functions in R 180
Formatting date/time 181
Parsing text as date/time 181
Formatting date/time to strings 184
Using regular expressions 186
Finding a string pattern 188
Using groups to extract the data 189
Reading data in customizable ways 191
Summary 193
Chapter 7: Working with Data
194
Reading and writing data 194
Reading and writing text-format data in a file 195
Importing data via RStudio IDE 195
Importing data using built-in functions 196
Importing data using the readr package 197
Writing a data frame to a file 198
Reading and writing Excel worksheets 200
Reading and writing native data files 201
Reading and writing a single object in native format 202
Saving and restoring the working environment 204
Loading built-in datasets 205
Visualizing data 208
Creating scatter plots 208
Customizing chart elements 210
Customizing point styles 210
Customizing point colors 214
Creating line plots 216
Customizing line type and width 216
Plotting lines in multiple periods 218
Plotting lines with points 219
Plotting a multi-series chart with a legend 220
Creating bar charts 222
Creating pie charts 223
Creating histogram and density plots 224
Creating box plots 228
Analyzing data 231
Fitting a linear model 231
Fitting a regression tree 237
Summary 239
Chapter 8: Inside R
241
Understanding lazy evaluation 242
[ v ]
Understanding the copy-on-modify mechanism 247
Modifying objects outside a function 251
Understanding lexical scoping 253
Understanding how an environment works 258
Knowing the environment object 258
Creating and chaining environments 259
Accessing an environment 260
Chaining environments 261
Using environments for reference semantics 263
Knowing the built-in environments 264
Understanding environments associated with a function 267
Summary 270
Chapter 9: Metaprogramming
271
Understanding functional programming 271
Creating and using closures 272
Creating a simple closure 272
Making specialized functions 273
Fitting normal distribution with maximal likelihood estimation 274
Using higher-order functions 277
Creating aliases for functions 277
Using functions as variables 278
Passing functions as arguments 279
Computing on language 283
Capturing and modifying expressions 285
Capturing expressions as language objects 285
Modifying expressions 289
Capturing expressions of function arguments 290
Constructing function calls 291
Evaluating expressions 292
Understanding non-standard evaluation 295
Implementing quick subsetting using non-standard evaluation 295
Understanding dynamic scoping 297
Using formulas to capture expression and environment 298
Implementing subset with metaprogramming 301
Summary 303
Chapter 10: Object-Oriented Programming
304
Introducing object-oriented programming 305
Understanding classes and methods 305
Understanding inheritance 305
Working with the S3 object system 306
Understanding generic functions and method dispatch 307
[ vi ]
Working with built-in classes and methods 309
Defining generic functions for existing classes 318
Creating objects of new classes 319
Using list as the underlying data structure 319
Using an atomic vector as the underlying data structure 324
Understanding S3 inheritance 327
Working with S4 331
Defining S4 classes 332
Understanding S4 inheritance 336
Defining S4 generic functions 338
Understanding multiple dispatch 341
Working with the reference class 343
Working with R6 345
Summary 348
Chapter 11: Working with Databases
350
Working with relational databases 350
Creating a SQLite database 351
Writing multiple tables to a database 353
Appending data to a table 354
Accessing tables and table fields 354
Learning SQL to query relational databases 357
Fetching query results chunk by chunk 366
Using transactions for consistency 367
Storing data in files to a database 373
Working with NoSQL databases 375
Working with MongoDB 376
Querying data from MongoDB 377
Creating and removing indexes 384
Using Redis 391
Accessing Redis from R 392
Setting and getting values from the Redis server 392
Summary 396
Chapter 12: Data Manipulation
398
Using built-in functions to manipulate data frames 399
Using built-in functions to manipulate data frames 400
Reshaping data frames using reshape2 407
Using SQL to query data frames via the sqldf package 412
Using data.table to manipulate data 417
Using key to access rows 423
Summarizing data by groups 426
[ vii ]
Reshaping data.table 428
Using in-place set functions 431
Understanding dynamic scoping of data.table 432
Using dplyr pipelines to manipulate data frames 437
Using rlist to work with nested data structures 446
Summary 451
Chapter 13: High-Performance Computing
452
Understanding code performance issues 453
Measuring code performance 455
Profiling code 462
Profiling code with Rprof 463
Profiling code with profvis 466
Understanding why code can be slow 468
Boosting code performance 470
Using built-in functions 470
Using vectorization 473
Using byte-code compiler 475
Using Intel MKL-powered R distribution 477
Using parallel computing 477
Using parallel computing on Windows 480
Using parallel computing on Linux and MacOS 485
Using Rcpp 486
OpenMP 491
RcppParallel 493
Summary 495
Chapter 14: Web Scraping
496
Looking inside web pages 496
Extracting data from web pages using CSS selectors 503
Learning XPath selectors 506
Analysing HTML code and extracting data 511
Summary 522
Chapter 15: Boosting Productivity
523
Writing R Markdown documents 523
Getting to know markdown 523
Integrating R into Markdown 530
Embedding tables and charts 532
Embedding tables 533
Embedding charts and diagrams 536
Embedding interactive plots 537
[ viii ]