The big R-book : from data science to learning machines for the professional /: from data science to learning machines for the professional. (2020)

• Record Type: Book
• Further Information: Note: Philippe J.S. De Brouwer.
• Authors: DeBrouwer, Philippe J. S
• Contents: Foreword v About the Author vii Acknowledgements ix Preface / Why this book? xi Contents xv I Introduction 1 1 The Big Picture with Kondratiev and Kardashev 3 2 The Scientific Method and Data 7 3 Conventions 13 II Starting with R and Elements of Statistics 19 4 The Basics of R 21 4.1 Variables 27 4.2 Data Types 29 4.2.1 Elementary Data Types 29 4.2.2 Vectors 30 4.2.3 Lists 33 4.2.4 Matrices 39 4.2.5 Arrays 42 4.2.6 Factors 44 4.2.7 Data Frames 48 4.3 Operators 56 4.3.1 Arithmetic Operators 56 4.3.2 Relational Operators 57 4.3.3 Logical Operators 57 4.3.4 Assignment Operators 59 4.3.5 Other Operators 60 4.3.6 Loops 62 4.3.7 Functions 66 4.3.8 Packages 70 4.3.9 Strings 73 4.4 Selected Data Interfaces 76 4.4.1 CSV Files 76 4.4.2 Excel Files 80 4.4.3 Databases 80 4.5 Distributions 83 4.5.1 Normal Distribution 83 4.5.2 Binomial Distribution 85 5 Lexical Scoping and environments 91 5.1 Environments in R 92 5.2 Lexical Scoping in R 94 6 The Implementation of OO 99 6.1 Base Types 102 6.2 S3 Objects 104 6.2.1 Creating S3 objects 107 6.2.2 Creating generic methods 109 6.2.3 Method dispatch 110 6.2.4 Group generic functions 111 6.3 S4 Objects 114 6.3.1 Creating S4 Objects 114 6.3.2 Recognising objects, generic functions, and methods 122 6.3.3 Creating S4 Generics 124 6.3.4 Method dispatch 125 6.4 The reference class, refclass, RC or R5 model 127 6.4.1 Creating R5 objects 127 6.5 OO Conclusion 134 7 Tidy R with the Tidyverse 137 7.1 The Philosophy of the Tidyverse 138 7.2 Packages in the tidyverse 141 7.3 Working with the tidyverse 144 7.3.1 tibbles 144 7.3.2 Piping with R 150 7.3.3 Attention points when using the pipe command 151 7.3.3.1 Advanced piping 153 7.3.3.2 Conclusion 155 8 Elements of Descriptive Statistics 157 8.1 Measures of Central Tendency 158 8.1.1 Mean 158 8.1.2 The Median 161 8.1.3 The Mode 162 8.2 Measures of Variation or Spread 164 8.3 Measures of Covariation 166 8.4 Chi Square Tests 169 9 Further Reading 171 III Data Import 173 10 A short history of modern database systems 175 11 RDBMS 179 12 SQL 183 12.1 Designing the database 184 12.2 Building the database 187 12.3 Adding data to the database 196 12.4 Querying the database 200 12.5 Modifying an existing database 206 12.6 Advanced features of SQL 211 13 Connecting R to an SQL database 215 IV Data Wrangling 221 14 Anonymising Data 225 15 DataWrangling in the tidyverse 229 15.1 Tidy data 230 15.2 Importing the data 232 15.2.1 Importing from an SQL RDBMS 232 15.2.2 Importing flat files in the tidyverse 234 15.2.2.1 CSV Files 236 15.2.2.2 Making sense of fixed width files 238 15.3 Tidying up data with tidyr 243 15.3.1 Splitting tables 244 15.3.2 headers to data 249 15.3.3 Spreading one column over many 250 15.3.4 separate 252 15.3.5 Unite 254 15.3.6 Wrong Data 255 15.4 Playing with tipples: SQL-like functionality 256 15.4.1 Selecting 256 15.4.2 Filtering 256 15.4.3 Joining 258 15.4.4 Mutating 262 15.4.5 Set Operations 265 15.5 String Manipulation in the tidyverse 268 15.5.1 Basic string manipulation 269 15.5.2 Pattern matching with regular expressions 272 15.5.2.1 Regular Expressions 273 15.5.2.2 Functions using Regex 279 15.6 Dates with lubridate 287 15.6.0.1 ISO 8601 Format 288 15.6.0.2 Timezones 290 15.6.0.3 Extract and set date and time components 291 15.6.0.4 Calculating with date-times 293 15.7 Factors with forcats 298 16 Dealing with missing data 307 17 Data Binning 319 17.1 Tuning the binning procedure 323 17.2 More complex cases: matrix binning 329 17.3 Weight of evidence and information value 336 18 Factoring analysis and principle components 339 18.1 Principle components analysis 340 18.2 Factor Analysis 345 V Explore Data 349 19 Using Descriptive Statistics 353 20 Standard Charts & Graphs 357 20.1 Pie Charts 358 20.2 Bar Charts 359 20.3 Boxplots 361 20.4 Violin plots 363 20.5 Histograms 366 20.6 Scatterplots 368 20.7 Line Graphs 371 20.8 Plotting Functions 373 20.9 Maps and contour plots 374 21 Elected Visualization Methods 377 21.1 Heat-maps 377 21.2 Text Mining 379 21.2.1 Word Clouds 379 21.2.2 Word Associations 383 21.3 Colours in R 386 22 Time Series Analysis 393 22.1 Time Series in R 394 22.2 Forecasting 397 22.2.1 Moving Average 397 22.2.2 Seasonal Decomposition 403 VI Modelling 409 23 Regression Models 411 23.1 Linear Regression 411 23.2 Multiple Linear Regression 415 23.2.1 Poisson Regression 416 23.2.2 Non-Linear Regression 418 23.3 Performance of regression models 421 23.3.1 Mean Square Error (MSE) 421 23.3.2 R-Squared 421 23.3.3 Mean Average Deviation (MAD) 423 24 Classification Models 425 24.1 Logistic Regression 425 24.2 The performance of binary classification models 427 24.2.1 The Confusion Matrix and related measures 428 24.2.2 ROC 431 24.2.3 AUC 433 24.2.4 AUC Gini for logistic regression 435 24.2.5 Kolmogorov-Smirnov (KS) for logistic regression 436 24.2.6 Finding an Optimal Cut-off 439 25 Learning Machines 445 25.1 Decision Tree 447 25.1.1 Essential Background 447 25.1.2 Important considerations 452 25.1.3 Growing trees with R 455 25.1.4 Evaluating the performance of a decision tree 463 25.1.4.1 The performance of the regression tree 464 25.1.4.2 The performance of the classification tree 464 25.2 Random Forest 467 25.3 Artificial Neural Networks (ANN) 472 25.3.1 The basics of ANNs in R 472 25.3.2 An example of a work-flow to develop an ANN 475 25.4 Support Vector Machine 483 25.5 Unsupervised learning and clustering 487 25.5.1 k-means clustering 488 25.5.2 Fuzzy clustering 501 25.5.3 Hierarchical clustering 504 25.5.4 Other clustering methods 506 26 Towards a tidy modelling cycle with modelr 507 27 Model Validation 513 27.1 Model quality measures 515 27.2 Predictions and residuals 516 27.3 Bootstrapping 517 27.4 Cross-Validation 520</
• Edition: 1st
• Publisher Details: Hoboken, New Jersey : John Wiley & Sons, Inc
• Publication Date: 2020
• Extent: 1 online resource
• Subjects: 005.7; Big data; Machine learning; R (Computer program language)
• Languages: English
• ISBNs: 9781119632771
• Related ISBNs: 9781119632764
• Notes: Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
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