R Programming by Example

Cover......Page 1
Title Page......Page 2
Copyright......Page 3
Credits......Page 4
About the Author......Page 5
About the Reviewer......Page 6
www.PacktPub.com......Page 7
Customer Feedback......Page 8
Table of Contents......Page 9
Preface......Page 17
What R is and what it isn't......Page 24
R is a high quality statistical computing system......Page 25
R is free, as in freedom and as in free beer......Page 26
Comparing R with other software......Page 27
The interpreter and the console......Page 28
Pick an IDE or a powerful editor......Page 31
The send to console functionality......Page 32
The efficient write-execute loop......Page 33
Executing R code in non-interactive sessions......Page 34
How to use this book......Page 36
Tracking state with symbols and variables......Page 37
Working with data types and data structures......Page 38
Special values......Page 39
Characters......Page 40
Logicals......Page 42
Vectors......Page 44
Factors......Page 48
Matrices......Page 50
Lists......Page 53
Data frames......Page 56
Divide and conquer with functions......Page 58
Functions as arguments......Page 61
Operators are functions......Page 63
Complex logic with control structures......Page 64
If… else conditionals......Page 65
For loops......Page 67
While loops......Page 70
The examples in this book......Page 71
Summary......Page 72
Chapter 2: Understanding Votes with Descriptive Statistics......Page 73
The Brexit votes example......Page 74
Cleaning and setting up the data......Page 76
Summarizing the data into a data frame......Page 79
Visualizing variable distributions......Page 84
Using matrix scatter plots for a quick overview......Page 88
Getting a better look with detailed scatter plots......Page 89
Understanding interactions with correlations......Page 93
Creating a new dataset with what we've learned......Page 94
Building new variables with principal components......Page 96
Planning before programming......Page 100
Understanding the fundamentals of high-quality code......Page 101
Programming by visualizing the big picture......Page 103
Summary......Page 110
Chapter 3: Predicting Votes with Linear Models......Page 112
Setting up the data......Page 113
Training and testing datasets......Page 114
Predicting votes with linear models......Page 115
Checking model assumptions......Page 118
Checking linearity with scatter plots......Page 119
Checking normality with histograms and quantile-quantile plots......Page 120
Checking homoscedasticity with residual plots......Page 123
Checking no collinearity with correlations......Page 125
Measuring accuracy with score functions......Page 127
Generating model combinations......Page 129
Summary......Page 136
Chapter 4: Simulating Sales Data and Working with Databases......Page 137
Designing our data tables......Page 138
Simplifying assumptions......Page 139
The too-much-empty-space problem......Page 140
The too-much-repeated-data problem......Page 141
Simulating the sales data......Page 143
Simulating numeric data according to distribution assumptions......Page 144
Simulating categorical values using factors......Page 146
Simulating numbers under shared restrictions......Page 147
Simulating strings for complex identifiers......Page 149
Putting everything together......Page 151
Simulating the client data......Page 155
Simulating the client messages data......Page 157
Working with relational databases......Page 160
Summary......Page 165
Chapter 5: Communicating Sales with Visualizations......Page 166
Extending our data with profit metrics......Page 167
Building blocks for reusable high-quality graphs......Page 168
Starting with simple applications for bar graphs......Page 170
Adding a third dimension with colors......Page 174
Graphing top performers with bar graphs......Page 177
Graphing disaggregated data with boxplots......Page 179
Pricing and profitability by protein source and continent......Page 181
Client birth dates, gender, and ratings......Page 183
Developing our own graph type – radar graphs......Page 186
Exploring with interactive 3D scatter plots......Page 191
Looking at dynamic data with time-series......Page 193
Looking at geographical data with static maps......Page 196
Maps you can navigate and zoom-in to......Page 198
High-tech-looking interactive globe......Page 201
Summary......Page 205
Chapter 6: Understanding Reviews with Text Analysis......Page 206
This chapter's required packages......Page 207
What is text analysis and how does it work?......Page 208
Preparing, training, and testing data......Page 210
Building the corpus with tokenization and data cleaning......Page 212
Document feature matrices......Page 215
Training our first predictive model......Page 217
Improving speed with parallelization......Page 219
Computing predictive accuracy and confusion matrices......Page 220
Improving our results with TF-IDF......Page 221
Adding flexibility with N-grams......Page 224
Reducing dimensionality with SVD......Page 226
Extending our analysis with cosine similarity......Page 228
Digging deeper with sentiment analysis......Page 229
Testing our predictive model with unseen data......Page 231
Retrieving text data from Twitter......Page 234
Summary......Page 236
Chapter 7: Developing Automatic Presentations......Page 237
Why invest in automation?......Page 238
Literate programming as a content creation methodology......Page 239
Reproducibility as a benefit of literate programming......Page 240
The basic tools for an automation pipeline......Page 241
A gentle introduction to Markdown......Page 242
Headers......Page 243
Lists......Page 244
Tables......Page 245
Images......Page 246
Code......Page 247
Code chunks......Page 248
Tables......Page 249
Graphs......Page 250
Caching......Page 251
Producing the final output with knitr......Page 252
Developing graphs and analysis as we normally would......Page 253
Building our presentation with R Markdown......Page 263
Summary......Page 270
Chapter 8: Object-Oriented System to Track Cryptocurrencies......Page 271
The cryptocurrencies example......Page 272
A brief introduction to object-oriented programming......Page 273
The purpose of object-oriented programming......Page 274
Encapsulation......Page 275
Polymorphism......Page 276
Classes and constructors......Page 277
Public and private methods......Page 278
Interfaces, factories, and patterns in general......Page 279
The first source of confusion – various object models......Page 280
The second source of confusion – generic functions......Page 281
Classes, constructors, and composition......Page 282
Public methods and polymorphism......Page 283
Encapsulation and mutability......Page 286
Inheritance......Page 288
The S4 object model......Page 289
Classes, constructors, and composition......Page 290
Public methods and polymorphism......Page 291
Encapsulation and mutability......Page 293
Inheritance......Page 295
Classes, constructors, and composition......Page 296
Encapsulation and mutability......Page 299
Inheritance......Page 300
Active bindings......Page 301
The architecture behind our cryptocurrencies system......Page 302
Starting simple with timestamps using S3 classes......Page 307
Implementing cryptocurrency assets using S4 classes......Page 310
Implementing our storage layer with R6 classes......Page 311
Communicating available behavior with a database interface......Page 312
Implementing a database-like storage system with CSV files......Page 313
Easily allowing new database integration with a factory......Page 320
Encapsulating multiple databases with a storage layer......Page 321
Creating a very simple requester to isolate API calls......Page 323
Developing our exchanges infrastructure......Page 324
Developing our wallets infrastructure......Page 328
Implementing our wallet requesters......Page 329
Finally introducing users with S3 classes......Page 333
Helping ourselves with a centralized settings file......Page 335
Saving our initial user data into the system......Page 337
Activating our system with two simple functions......Page 338
Some advice when working with object-oriented systems......Page 341
Summary......Page 342
Chapter 9: Implementing an Efficient Simple Moving Average......Page 343
Required packages......Page 344
Starting by using good algorithms......Page 345
Just how much impact can algorithm selection have?......Page 346
Calculating simple moving averages inefficiently......Page 349
Simulating the time-series ......Page 351
Our first (very inefficient) attempt at an SMA......Page 353
Understanding why R can be slow......Page 356
Object immutability......Page 357
Memory-bound processes......Page 358
Single-threaded processes......Page 359
Profiling fundamentals with Rprof()......Page 360
Benchmarking manually with system.time()......Page 362
Benchmarking automatically with microbenchmark()......Page 363
Using the simple data structure for the job......Page 365
Vectorizing as much as possible......Page 368
Removing unnecessary logic......Page 371
Moving checks out of iterative processes......Page 372
If you can, avoid iterating at all......Page 374
Using R's way of iterating efficiently......Page 376
Avoiding sending data structures with overheads......Page 378
Using parallelization to divide and conquer......Page 380
How deep does the parallelization rabbit hole go?......Page 381
Practical parallelization with R......Page 382
Using C++ and Fortran to accelerate calculations......Page 385
Using an old-school approach with Fortran......Page 386
Using a modern approach with C++......Page 390
Looking back at what we have achieved......Page 394
Preallocating memory to avoid duplication......Page 396
Just-in-time (JIT) compilation of R code......Page 397
Improving our data and memory management......Page 398
Using specialized packages for performance......Page 399
Summary......Page 400
Chapter 10: Adding Interactivity with Dashboards......Page 402
Introducing the Shiny application architecture and reactivity......Page 403
What is functional reactive programming and why is it useful?......Page 404
The building blocks for reactivity in Shiny......Page 406
The input, output, and rendering functions......Page 407
Designing our high-level application structure......Page 409
Setting up a two-column distribution......Page 410
Introducing sections with panels......Page 411
Inserting a dynamic data table......Page 412
Setting up static user inputs......Page 414
Setting up dynamic options in a drop-down......Page 417
Setting up dynamic input panels......Page 419
Adding a summary table with shared data......Page 422
Adding a simple moving average graph......Page 424
Adding interactivity with a secondary zoom-in graph......Page 428
Styling our application with themes......Page 431
Adding static images......Page 437
Adding custom CSS styling......Page 438
Sharing your newly created application......Page 439
Summary......Page 440
External requirements – software outside of R......Page 441
macOS High Sierra......Page 443
Setting up user/password in both Linux and macOS......Page 444
Ubuntu 17.10......Page 446
macOS High Sierra......Page 447
Internal requirements – R packages......Page 448
Loading R packages......Page 452
Index......Page 453