データサイエンスのためのR完全入門<br>The Big R-Book : From Data Science to Learning Machines and Big Data (HAR/PSC)

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データサイエンスのためのR完全入門
The Big R-Book : From Data Science to Learning Machines and Big Data (HAR/PSC)

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  • 製本 Hardcover:ハードカバー版/ページ数 892 p.
  • 言語 ENG
  • 商品コード 9781119632726
  • DDC分類 005.133

Full Description


Introduces professionals and scientists to statistics and machine learning using the programming language RWritten by and for practitioners, this book provides an overall introduction to R, focusing on tools and methods commonly used in data science, and placing emphasis on practice and business use. It covers a wide range of topics in a single volume, including big data, databases, statistical machine learning, data wrangling, data visualization, and the reporting of results. The topics covered are all important for someone with a science/math background that is looking to quickly learn several practical technologies to enter or transition to the growing field of data science. The Big R-Book for Professionals: From Data Science to Learning Machines and Reporting with R includes nine parts, starting with an introduction to the subject and followed by an overview of R and elements of statistics. The third part revolves around data, while the fourth focuses on data wrangling. Part 5 teaches readers about exploring data. In Part 6 we learn to build models, Part 7 introduces the reader to the reality in companies, Part 8 covers reports and interactive applications and finally Part 9 introduces the reader to big data and performance computing. It also includes some helpful appendices.Provides a practical guide for non-experts with a focus on business usersContains a unique combination of topics including an introduction to R, machine learning, mathematical models, data wrangling, and reportingUses a practical tone and integrates multiple topics in a coherent frameworkDemystifies the hype around machine learning and AI by enabling readers to understand the provided models and program them in RShows readers how to visualize results in static and interactive reportsSupplementary materials includes PDF slides based on the book's content, as well as all the extracted R-code and is available to everyone on a Wiley Book Companion SiteThe Big R-Book is an excellent guide for science technology, engineering, or mathematics students who wish to make a successful transition from the academic world to the professional. It will also appeal to all young data scientists, quantitative analysts, and analytics professionals, as well as those who make mathematical models.

Table of Contents

Foreword                                           xxv
About the Author xxvii
Acknowledgements xxix
Preface xxxi
About the Companion Site xxxv
I Introduction 1 (18)
1 The Big Picture with Kondratiev and 3 (4)
Kardashev
2 The Scientific Method and Data 7 (4)
3 Conventions 11 (8)
II Starting with R and Elements of Statistics 19 (194)
4 The Basics of R 21 (60)
4.1 Getting Started with R 23 (3)
4.2 Variables 26 (2)
4.3 Data Types 28 (29)
4.3.1 The Elementary Types 28 (1)
4.3.2 Vectors 29 (1)
4.3.2.1 Creating Vectors 29 (1)
4.3.3 Accessing Data from a Vector 29 (1)
4.3.3.1 Vector Arithmetic 30 (1)
4.3.3.2 Vector Recycling 30 (1)
4.3.3.3 Reordering and Sorting 31 (1)
4.3.4 Matrices 32 (1)
4.3.4.1 Creating Matrices 32 (1)
4.3.4.2 Naming Rows and Columns 33 (1)
4.3.4.3 Access Subsets of a Matrix 33 (1)
4.3.4.4 Matrix Arithmetic 34 (4)
4.3.5 Arrays 38 (1)
4.3.5.1 Creating and Accessing Arrays 38 (1)
4.3.5.2 Naming Elements of Arrays 39 (1)
4.3.5.3 Manipulating Arrays 39 (1)
4.3.5.4 Applying Functions over Arrays 39 (2)
4.3.6 Lists 41 (1)
4.3.6.1 Creating Lists 41 (1)
4.3.6.2 Naming Elements of Lists 41 (1)
4.3.6.3 List Manipulations 42 (3)
4.3.7 Factors 45 (1)
4.3.7.1 Creating Factors 46 (1)
4.3.7.2 Ordering Factors 47 (2)
4.3.8 Data Frames 49 (1)
4.3.8.1 Introduction to Data Frames 49 (1)
4.3.8.2 Accessing Information from a 50 (1)
Data Frame
4.3.8.3 Editing Data in a Data Frame 51 (1)
4.3.8.4 Modifying Data Frames 51 (3)
4.3.9 Strings or the Character-type 54 (3)
4.4 Operators 57 (6)
4.4.1 Arithmetic Operators 57 (1)
4.4.2 Relational Operators 57 (1)
4.4.3 Logical Operators 58 (1)
4.4.4 Assignment Operators 59 (2)
4.4.5 Other Operators 61 (2)
4.5 Flow Control Statements 63 (6)
4.5.1 Choices 63 (1)
4.5.1.1 The if-Statement 63 (1)
4.5.1.2 The Vectorised If-statement 64 (1)
4.5.1.3 The Switch-statement 64 (1)
4.5.2 Loops 65 (1)
4.5.2.1 The For Loop 65 (1)
4.5.2.2 Repeat 66 (1)
4.5.2.3 While 66 (1)
4.5.2.4 Loop Control Statements 67 (2)
4.6 Functions 69 (3)
4.6.1 Built-in Functions 69 (1)
4.6.2 Help with Functions 69 (1)
4.6.3 User-defined Functions 70 (1)
4.6.4 Changing Functions 70 (1)
4.6.5 Creating Function with Default 71 (1)
Arguments
4.7 Packages 72 (3)
4.7.1 Discovering Packages in R 72 (1)
4.7.2 Managing Packages in R 73 (2)
4.8 Selected Data Interfaces 75 (6)
4.8.1 CSV Files 75 (4)
4.8.2 Excel Files 79 (1)
4.8.3 Databases 79 (2)
5 Lexical Scoping and Environments 81 (6)
5.1 Environments in R 81 (2)
5.2 Lexical Scoping in R 83 (4)
6 The Implementation of OO 87 (34)
6.1 Base Types 89 (2)
6.2 S3 Objects 91 (9)
6.2.1 Creating S3 Objects 94 (2)
6.2.2 Creating Generic Methods 96 (1)
6.2.3 Method Dispatch 97 (1)
6.2.4 Group Generic Functions 98 (2)
6.3 S4 Objects 100 (13)
6.3.1 Creating S4 Objects 100 (1)
6.3.2 Using S4 Objects 101 (4)
6.3.3 Validation of Input 105 (2)
6.3.4 Constructor functions 107 (1)
6.3.5 The Data slot 108 (1)
6.3.6 Recognising Objects, Generic 108 (2)
Functions, and Methods
6.3.7 Creating S4 Generics 110 (3)
6.3.8 Method Dispatch Ill
6.4 The Reference Class, refclass, RC or 113 (6)
R5 Model
6.4.1 Creating RC Objects 113 (4)
6.4.2 Important Methods and Attributes 117 (2)
6.5 Conclusions about the OO 119 (2)
Implementation
7 Tidy R with the Tidyverse 121 (18)
7.1 The Philosophy of the Tidyverse 121 (3)
7.2 Packages in the Tidyverse 124 (3)
7.2.1 The Core Tidyverse 124 (1)
7.2.2 The Non-core Tidyverse 125 (2)
7.3 Working with the Tidyverse 127 (12)
7.3.1 Tibbies 127 (5)
7.3.2 Piping with R 132 (1)
7.3.3 Attention Points When Using the 133 (1)
Pipe
7.3.4 Advanced Piping 134 (1)
7.3.4.1 The Dollar Pipe 134 (1)
7.3.4.2 TheT-Pipe 135 (1)
7.3.4.3 The Assignment Pipe 136 (1)
7.3.5 Conclusion 137 (2)
8 Elements of Descriptive Statistics 139 (20)
8.1 Measures of Central Tendency 139 (6)
8.1.1 Mean 139 (1)
8.1.1.1 The Arithmetic Mean 139 (1)
8.1.1.2 Generalised Means 140 (2)
8.1.2 The Median 142 (1)
8.1.3 The Mode 143 (2)
8.2 Measures of Variation or Spread 145 (2)
8.3 Measures of Covariation 147 (3)
8.3.1 The Pearson Correlation 147 (1)
8.3.2 The Spearman Correlation 148 (1)
8.3.3 Chi-square Tests 149 (1)
8.4 Distributions 150 (5)
8.4.1 Normal Distribution 150 (3)
8.4.2 Binomial Distribution 153 (2)
8.5 Creating an Overview of Data 155 (4)
Characteristics
9 Visualisation Methods 159 (38)
9.1 Scatterplots 161 (2)
9.2 Line Graphs 163 (2)
9.3 Pie Charts 165 (2)
9.4 Bar Charts 167 (4)
9.5 Boxplots 171 (2)
9.6 Violin Plots 173 (3)
9.7 Histograms 176 (3)
9.8 Plotting Functions 179 (1)
9.9 Maps and Contour Plots 180 (1)
9.10 Heat-maps 181 (3)
9.11 Text Mining 184 (7)
9.11.1 Word Clouds 184 (4)
9.11.2 Word Associations 188 (3)
9.12 Colours in R 191 (6)
10 Time Series Analysis 197 (14)
10.1 Time Series in R 197 (3)
10.1.1 The Basics of Time Series in R 197 (1)
10.1.1.1 The Function ts() 197 (1)
10.1.1.2 Multiple Time Series in one 198 (2)
Object
10.2 Forecasting 200 (11)
10.2.1 Moving Average 200 (1)
10.2.1.1 The Moving Average in R 200 (2)
10.2.1.2 Testing the Accuracy of the 202 (2)
Forecasts
10.2.1.3 Basic Exponential Smoothing 204 (1)
10.2.1.4 Holt-Winters Exponential 205 (1)
Smoothing
10.2.2 Seasonal Decomposition 206 (5)
11 Further Reading 211 (2)
III Data Import 213 (44)
12 A Short History of Modern Database 215 (4)
Systems
13 RDBMS 219 (4)
14 SQL 223 (30)
14.1 Designing the Database 223 (3)
14.2 Building the Database Structure 226 (9)
14.2.1 Installing a RDBMS 226 (2)
14.2.2 Creating the Database 228 (1)
14.2.3 Creating the Tables and Relations 229 (6)
14.3 Adding Data to the Database 235 (4)
14.4 Querying the Database 239 (5)
14.4.1 The Basic Select Query 239 (1)
14.4.2 More Complex Queries 240 (4)
14.5 Modifying the Database Structure 244 (5)
14.6 Selected Features of SQL 249 (4)
14.6.1 Changing Data 249 (1)
14.6.2 Functions in SQL 249 (4)
15 Connecting R to an SQL Database 253 (4)
IV Data Wrangling 257 (116)
16 Anonymous Data 261 (4)
17 Data Wrangling in the tidyverse 265 (68)
17.1 Importing the Data 266 (9)
17.1.1 Importing from an SQL RDBMS 266 (1)
17.1.2 Importing Flat Files in the 267 (3)
Tidyverse
17.1.2.1 CSV Files 270 (1)
17.1.2.2 Making Sense of Fixed-width 271 (4)
Files
17.2 Tidy Data 275 (2)
17.3 Tidying Up Data with tidyr 277 (11)
17.3.1 Splitting Tables 278 (3)
17.3.2 Convert Headers to Data 281 (3)
17.3.3 Spreading One Column Over Many 284 (1)
17.3.4 Split One Columns into Many 285 (1)
17.3.5 Merge Multiple Columns Into One 286 (1)
17.3.6 Wrong Data 287 (1)
17.4 SQL-like Functionality via dplyr 288 (11)
17.4.1 Selecting Columns 288 (1)
17.4.2 Filtering Rows 289 (1)
17.4.3 Joining 290 (3)
17.4.4 Mutating Data 293 (3)
17.4.5 Set Operations 296 (3)
17.5 String Manipulation in the tidyverse 299 (15)
17.5.1 Basic String Manipulation 300 (2)
17.5.2 Pattern Matching with Regular 302 (1)
Expressions
17.5.2.1 The Syntax of Regular 303 (5)
Expressions
17.5.2.2 Functions Using Regex 308 (6)
17.6 Dates with lubridate 314 (11)
17.6.1 ISO 8601 Format 315 (2)
17.6.2 Time-zones 317 (1)
17.6.3 Extract Date and Time Components 318 (1)
17.6.4 Calculating with Date-times 319 (1)
17.6.4.1 Durations 320 (1)
17.6.4.2 Periods 321 (2)
17.6.4.3 Intervals 323 (1)
17.6.4.4 Rounding 324 (1)
17.7 Factors with Forcats 325 (8)
18 Dealing with Missing Data 333 (10)
18.1 Reasons for Data to be Missing 334 (2)
18.2 Methods to Handle Missing Data 336 (3)
18.2.1 Alternative Solutions to Missing 336 (2)
Data
18.2.2 Predictive Mean Matching (PMM) 338 (1)
18.3 R Packages to Deal with Missing Data 339 (4)
18.3.1 mice 339 (1)
18.3.2 missForest 340 (1)
18.3.3 Hmisc 341 (2)
19 Data Binning 343 (20)
19.1 What is Binning and Why Use It 343 (4)
19.2 Tuning the Binning Procedure 347 (5)
19.3 More Complex Cases: Matrix Binning 352 (7)
19.4 Weight of Evidence and Information 359 (4)
Value
19.4.1 Weight of Evidence (WOE) 359 (1)
19.4.2 Information Value (IV) 359 (1)
19.4.3 WOE and IV in R 359 (4)
20 Factoring Analysis and Principle 363 (10)
Components
20.1 Principle Components Analysis (PCA) 364 (4)
20.2 Factor Analysis 368 (5)
V Modelling 373 (190)
21 Regression Models 375 (12)
21.1 Linear Regression 375 (4)
21.2 Multiple Linear Regression 379 (5)
21.2.1 Poisson Regression 379 (2)
21.2.2 Non-linear Regression 381 (3)
21.3 Performance of Regression Models 384 (3)
21.3.1 Mean Square Error (MSE) 384 (1)
21.3.2 q-Squared 384 (2)
21.3.3 Mean Average Deviation (MAD) 386 (1)
22 Classification Models 387 (18)
22.1 Logistic Regression 388 (2)
22.2 Performance of Binary Classification 390 (15)
Models
22.2.1 The Confusion Matrix and Related 391 (2)
Measures
22.2.2 ROC 393 (3)
22.2.3 The AUC 396 (1)
22.2.4 The Gini Coefficient 397 (1)
22.2.5 Kolmogorov-Smirnov (KS) for 398 (1)
Logistic Regression
22.2.6 Finding an Optimal Cut-off 399 (6)
23 Learning Machines 405 (64)
23.1 Decision Tree 407 (21)
23.1.1 Essential Background 407 (1)
23.1.1.1 The Linear Additive Decision 407 (1)
Tree
23.1.1.2 The CART Method407 (1)
23.1.1.3 Tree Pruning 408 (1)
23.1.1.4 Classification Trees 409 (2)
23.1.1.5 Binary Classification Trees 411 (1)
23.1.2 Important Considerations 412 (1)
23.1.2.1 Broadening the Scope 412 (1)
23.1.2.2 Selected Issues 413 (1)
23.1.3 Growing Trees with the Package 414 (1)
rpart
23.1.3.1 Getting Started with the 414 (1)
Function rpart()
23.1.3.2 Example of a Classification 415 (3)
Tree with rpart
23.1.3.3 Visualising a Decision Tree 418 (1)
with rpart.plot
23.1.3.4 Example of a Regression Tree 419 (5)
with rpart
23.1.4 Evaluating the Performance of a 424 (1)
Decision Tree
23.1.4.1 The Performance of the 424 (1)
Regression Tree
23.1.4.2 The Performance of the 424 (4)
Classification Tree
23.2 Random Forest 428 (6)
23.3 Artificial Neural Networks (ANNs) 434 (13)
23.3.1 The Basics of ANNs in R 434 (2)
23.3.2 Neural Networks in R 436 (2)
23.3.3 The Work-flow to for Fitting a NN 438 (6)
23.3.4 Cross Validate the NN 444 (3)
23.4 Support Vector Machine 447 (3)
23.4.1 Fitting a SVM in R 447 (2)
23.4.2 Optimizing the SVM 449 (1)
23.5 Unsupervised Learning and Clustering 450 (19)
23.5.1 k-Means Clustering 450 (2)
23.5.1.1 k-Means Clustering in R 452 (3)
23.5.1.2 PCA before Clustering 455 (6)
23.5.1.3 On the Relation Between PCA 461 (1)
and k-Means
23.5.2 Visualizing Clusters in Three 462 (2)
Dimensions
23.5.3 Fuzzy Clustering 464 (2)
23.5.4 Hierarchical Clustering 466 (2)
23.5.5 Other Clustering Methods 468 (1)
24 Towards a Tidy Modelling Cycle with 469 (6)
modelr
24.1 Adding Predictions 470 (1)
24.2 Adding Residuals 471 (1)
24.3 Bootstrapping Data 472 (2)
24.4 Other Functions of modelr 474 (1)
25 Model Validation 475 (20)
25.1 Model Quality Measures 476 (1)
25.2 Predictions and Residuals 477 (2)
25.3 Bootstrapping 479 (4)
25.3.1 Bootstrapping in Base R 479 (2)
25.3.2 Bootstrapping in the tidyverse 481 (2)
with modelr
25.4 Cross-Validation 483 (9)
25.4.1 Elementary Cross Validation 483 (3)
25.4.2 Monte Carlo Cross Validation 486 (2)
25.4.3 fc-Fold Cross Validation 488 (1)
25.4.4 Comparing Cross Validation 489 (3)
Methods
25.5 Validation in a Broader Perspective 492 (3)
26 Labs 495 (16)
26.1 Financial Analysis with quantmod 495 (16)
26.1.1 The Basics of quantmod 495 (1)
26.1.2 Types of Data Available in 496 (1)
quantmod
26.1.3 Plotting with quantmod 497 (3)
26.1.4 The quantmod Data Structure 500 (1)
26.1.4.1 Sub-setting by Time and Date 500 (1)
26.1.4.2 Switching Time Scales 501 (1)
26.1.4.3 Apply by Period 501 (1)
26.1.5 Support Functions Supplied by 502 (2)
quantmod
26.1.6 Financial Modelling in quantmod 504 (1)
26.1.6.1 Financial Models in quantmod 504 (1)
26.1.6.2 A Simple Model with quantmod 504 (3)
26.1.6.3 Testing the Model Robustness 507 (4)
27 Multi Criteria Decision Analysis (MCDA) 511 (52)
27.1 What and Why 511 (2)
27.2 General Work-flow 513 (3)
27.3 Identify the Issue at Hand: Steps 1 516 (2)
and 2
27.4 Step 3: the Decision Matrix 518 (3)
27.4.1 Construct a Decision Matrix 518 (2)
27.4.2 Normalize the Decision Matrix 520 (1)
27.5 Step 4: Delete Inefficient and 521 (3)
Unacceptable Alternatives
27.5.1 Unacceptable Alternatives 521 (1)
27.5.2 Dominance - Inefficient 521 (3)
Alternatives
27.6 Plotting Preference Relationships 524 (2)
27.7 Step 5: MCDA Methods 526 (35)
27.7.1 Examples of Non-compensatory 526 (1)
Methods
27.7.1.1 The MaxMin Method 526 (1)
27.7.1.2 The MaxMax Method 526 (1)
27.7.2 The Weighted Sum Method (WSM) 527 (3)
27.7.3 Weighted Product Method (WPM) 530 (1)
27.7.4 Electre 530 (2)
27.7.4.1 ELECTRE I 532 (6)
27.7.4.2 ELECTRE II 538 (1)
27.7.4.3 Conclusions ELECTRE 539 (1)
27.7.5 PROMethEE 540 (3)
27.7.5.1 PROMethEE I 543 (6)
27.7.5.2 PROMethEE II 549 (4)
27.7.6 PCA (Gaia) 553 (4)
27.7.7 Outranking Methods 557 (1)
27.7.8 Goal Programming 558 (3)
27.8 Summary MCDA 561 (2)
VI Introduction to Companies 563 (120)
28 Financial Accounting (FA) 567 (16)
28.1 The Statements of Accounts 568 (3)
28.1.1 Income Statement 568 (1)
28.1.2 Net Income: The P&L statement 568 (1)
28.1.3 Balance Sheet 569 (2)
28.2 The Value Chain 571 (2)
28.3 Further, Terminology 573 (2)
28.4 Selected Financial Ratios 575 (8)
29 Management Accounting 583 (14)
29.1 Introduction 583 (2)
29.1.1 Definition of Management 583 (1)
Accounting (MA)
29.1.2 Management Information Systems 584 (1)
(MIS)
29.2 Selected Methods in MA 585 (5)
29.2.1 Cost Accounting 585 (2)
29.2.2 Selected Cost Types 587 (3)
29.3 Selected Use Cases of MA 590 (7)
29.3.1 Balanced Scorecard 590 (1)
29.3.2 Key Performance Indicators (KPIs) 591 (1)
29.3.2.1 Lagging Indicators 592 (1)
29.3.2.2 Leading Indicators 592 (1)
29.3.2.3 Selected Useful KPIs 593 (4)
30 Asset Valuation Basics 597 (86)
30.1 Time Value of Money 598 (3)
30.1.1 Interest Basics 598 (1)
30.1.2 Specific Interest Rate Concepts 598 (2)
30.1.3 Discounting 600 (1)
30.2 Cash 601 (1)
30.3 Bonds 602 (8)
30.3.1 Features of a Bond 602 (2)
30.3.2 Valuation of Bonds 604 (2)
30.3.3 Duration 606 (1)
30.3.3.1 Macaulay Duration 606 (1)
30.3.3.2 Modified Duration 607 (3)
30.4 The Capital Asset Pricing Model 610 (4)
(CAPM)
30.4.1 The CAPM Framework 610 (2)
30.4.2 The CAPM and Risk 612 (1)
30.4.3 Limitations and Shortcomings of 612 (2)
the CAPM
30.5 Equities 614 (24)
30.5.1 Definition 614 (1)
30.5.2 Short History 614 (1)
30.5.3 Valuation of Equities 615 (1)
30.5.4 Absolute Value Models 616 (1)
30.5.4.1 Dividend Discount Model (DDM) 616 (4)
30.5.4.2 Free Cash Flow (FCF) 620 (2)
30.5.4.3 Discounted Cash Flow Model 622 (1)
30.5.4.4 Discounted Abnormal Operating 623 (1)
Earnings Model
30.5.4.5 Net Asset Value Method or Cost 624 (1)
Method
30.5.4.6 Excess Earnings Method 625 (1)
30.5.5 Relative Value Models 625 (1)
30.5.5.1 The Concept of Relative Value 625 (1)
Models
30.5.5.2 The Price Earnings Ratio (PE) 626 (1)
30.5.5.3 Pitfalls when using PE Analysis 627 (1)
30.5.5.4 Other Company Value Ratios 627 (3)
30.5.6 Selection of Valuation Methods 630 (1)
30.5.7 Pitfalls in Company Valuation 631 (1)
30.5.7.1 Forecasting Performance 631 (1)
30.5.7.2 Results and Sensitivity 631 (7)
30.6 Forwards and Futures 638 (2)
30.7 Options 640 (43)
30.7.1 Definitions 640 (2)
30.7.2 Commercial Aspects 642 (1)
30.7.3 Short History 643 (1)
30.7.4 Valuation of Options at Maturity 644 (1)
30.7.4.1 A Long Call at Maturity 644 (1)
30.7.4.2 A Short Call at Maturity 645 (1)
30.7.4.3 Long and Short Put 646 (2)
30.7.4.4 The Put-Call Parity 648 (1)
30.7.5 The Black and Scholes Model 649 (1)
30.7.5.1 Pricing of Options Before 649 (1)
Maturity
30.7.5.2 Apply the Black and Scholes 650 (3)
Formula
30.7.5.3 The Limits of the Black and 653 (1)
Scholes Model
30.7.6 The Binomial Model 654 (1)
30.7.6.1 Risk Neutral Method 655 (4)
30.7.6.2 The Equivalent Portfolio 659 (1)
Binomial Model
30.7.6.3 Summary Binomial Model 660 (1)
30.7.7 Dependencies of the Option Price 660 (1)
30.7.7.1 Dependencies in a Long Call 661 (1)
Option
30.7.7.2 Dependencies in a Long Put 662 (2)
Option
30.7.7.3 Summary of Findings 664 (1)
30.7.8 The Greeks 664 (1)
30.7.9 Delta Hedging 665 (2)
30.7.10 Linear Option Strategies 667 (1)
30.7.10.1 Plotting a Portfolio of 667 (3)
Options
30.7.10.2 Single Option Strategies 670 (1)
30.7.10.3 Composite Option Strategies 671 (3)
30.7.11 Integrated Option Strategies 674 (1)
30.7.11.1 The Covered Call 675 (1)
30.7.11.2 The Married Put 676 (1)
30.7.11.3 The Collar 677 (1)
30.7.12 Exotic Options 678 (2)
30.7.13 Capital Protected Structures 680 (3)
VII Reporting 683 (58)
31 A Grammar of Graphics with ggplot2 687 (12)
31.1 The Basics of ggplot2 688 (4)
31.2 Over-plotting 692 (4)
31.3 Case Study for ggplot2 696 (3)
32 RMarkdown 699 (4)
33 Knitr and ETeX 703 (4)
34 An Automated Development Cycle 707 (2)
35 Writing and Communication Skills 709 (4)
36 Interactive Apps 713 (28)
36.1 Shiny 715 (4)
36.2 Browser Born Data Visualization 719 (6)
36.2.1 HTML-widgets 719 (1)
36.2.2 Interactive Maps with leaflet 720 (1)
36.2.3 Interactive Data Visualisation 721 (1)
with ggvis
36.2.3.1 Getting Started in R with ggvis 721 (2)
36.2.3.2 Combining the Power of ggvis 723 (1)
and Shiny
36.2.4 googleVis 723 (2)
36.3 Dashboards 725 (16)
36.3.1 The Business Case: a Diversity 726 (5)
Dashboard
36.3.2 A Dashboard with flexdashboard 731 (1)
36.3.2.1 A Static Dashboard 731 (5)
36.3.2.2 Interactive Dashboards with 736 (1)
flexdashboard
36.3.3 A Dashboard with shinydashboard 737 (4)
VIII Bigger and Faster R 741 (78)
37 Parallel Computing 743 (18)
37.1 Combine foreach and doParallel 745 (3)
37.2 Distribute Calculations over LAN 748 (4)
with Snow
37.3 Using the GPU 752 (9)
37.3.1 Getting Started with gpuR 754 (3)
37.3.2 On the Importance of Memory use 757 (2)
37.3.3 Conclusions for GPU Programming 759 (2)
38 Rand Big Data 761 (6)
38.1 Use a Powerful Server 763 (2)
38.1.1 Use R on a Server 763 (1)
38.1.2 Let the Database Server do the 763 (2)
Heavy Lifting
38.2 Using more Memory than we have RAM 765 (2)
39 Parallelism for Big Data 767 (26)
39.1 Apache Hadoop 769 (2)
39.2 Apache Spark 771 (22)
39.2.1 Installing Spark 771 (2)
39.2.2 Running Spark 773 (3)
39.2.3 SparkR 776 (4)
39.2.3.1 A User Defined Function on 780 (4)
Spark
39.2.3.2 Some Other Functions of SparkR 784 (1)
39.2.3.3 Machine learning with SparkR 785 (3)
39.2.4 sparklyr 788 (3)
39.2.5 SparkR or sparklyr 791 (2)
40 The Need for Speed 793 (26)
40.1 Benchmarking 794 (3)
40.2 Optimize Code 797 (15)
40.2.1 Avoid Repeating the Same 797 (1)
40.2.2 Use Vectorisation where 797 (2)
Appropriate
40.2.3 Pre-allocating Memory 799 (1)
40.2.4 Use the Fastest Function 800 (1)
40.2.5 Use the Fastest Package 801 (1)
40.2.6 Be Mindful about Details 802 (2)
40.2.7 Compile Functions 804 (2)
40.2.8 Use C or C++ Code in R 806 (3)
40.2.9 Using a C++ Source File in R 809 (2)
40.2.10 Call Compiled C++ Functions in R 811 (1)
40.3 Profiling Code 812 (5)
40.3.1 The Package profr 813 (1)
40.3.2 The Package proftools 813 (4)
40.4 Optimize Your Computer 817 (2)
IX Appendices 819 (2)
A Create your own R Package 821 (8)
A.1 Creating the Package in the R Console 823 (2)
A.2 Update the Package Description 825 (1)
A.3 Documenting the Functionsxs 826 (1)
A.4 Loading the Package 827 (1)
A.5 Further Steps 828 (1)
B Levels of Measurement 829 (4)
B.1 Nominal Scale 829 (1)
B.2 Ordinal Scale 830 (1)
B.3 Interval Scale 831 (1)
B.4 Ratio Scale 832 (1)
C Trademark Notices 833 (6)
C.1 General Trademark Notices 834 (1)
C.2 R-Related Notices 835 (4)
C.2.1 Crediting Developers of R Packages 835 (1)
C.2.2 The R-packages used in this Book 835 (4)
D Code Not Shown in the Body of the Book 839 (6)
E Answers to Selected Questions 845 (14)
Bibliography 859 (10)
Nomenclature 869 (12)
Index 881