R in 24 Hours, Sams Teach Yourself

Andy Nicholls has a Master of Mathematics degree from the University of Bath and Master of Science in Statistics with Applications in Medicine from the University of Southampton. Andy worked as a Senior Statistician in the pharmaceutical industry for a number of years before joining Mango Solutions as an R consultant in 2011. Since joining Mango, Andy has taught more than 50 on-site R training courses and has been involved in the development of more than 30 R packages. Today, he manages Mango Solution’s R consultancy team and continues to be a regular contributor to the quarterly LondonR events, by far the largest R user group in the UK, with over 1,000 meet-up members. Andy lives near the historical city of Bath, UK with his wonderful, tolerant wife and son.   Richard Pugh has a first-class Mathematics degree from the University of Bath. Richard worked as a statistician in the pharmaceutical industry before joining Insightful, the developers of S-PLUS, joining the pre-sales consulting team. Richard’s role at Insightful included a variety of activities, providing a range of training and consulting services to blue-chip customers across many sectors. In 2002, Richard co-founded Mango Solutions, developing the company and leading technical efforts around R and other analytic software. Richard is now Mango’s Chief Data Scientist and speaks regularly at data science and R events. Richard lives in Bradford on Avon, UK with his wife and two kids, and spends most of his “spare” (ha!) time renovating his house.   Aimee Gott has a PhD in Statistics from Lancaster University where she also completed her undergraduate and master’s degrees. As Training Lead, Aimee has delivered over 200 days of training for Mango. She has delivered on-site training courses in Europe and the U.S. in all aspects of R, as well as shorter workshops and online webinars. Aimee oversees Mango’s training course development across the data science pipeline, and regularly attends R user groups and meet-ups. In her spare time, Aimee enjoys learning European languages and documenting her travels through photography.

Preface   xii
HOUR 1: The R Community   1
A Concise History of R   1
The R Community   3
R Development   7
Summary   8
Q&A   8
Workshop   9
Activities   9
HOUR 2: The R Environment   11
Integrated Development Environments   11
R Syntax   14
R Objects   16
Using R Packages   23
Internal Help   28
Summary   29
Q&A   30
Workshop   30
Activities   32
HOUR 3: Single-Mode Data Structures   33
The R Data Types   33
Vectors, Matrices, and Arrays   34
Vectors   35
Matrices   49
Arrays   58
Relationship Between Single-Mode Data Objects   60
Summary   62
Q&A   62
Workshop   63
Activities   64
HOUR 4: Multi-Mode Data Structures   67
Multi-Mode Structures   67
Lists   68
Data Frames   86
Exploring Your Data   93
Summary   98
Q&A   98
Workshop   100
Activities   101
HOUR 5: Dates, Times, and Factors   103
Working with Dates and Times   103
The lubridate Package   107
Working with Categorical Data   108
Summary   112
Q&A   112
Workshop   113
Activities   114
HOUR 6: Common R Utility Functions   115
Using R Functions   115
Functions for Numeric Data   117
Logical Data   121
Missing Data   122
Character Data   123
Summary   125
Q&A   126
Workshop   126
Activities   127
HOUR 7: Writing Functions: Part I   129
The Motivation for Functions   129
Creating a Simple Function   130
The If/Else Structure   136
Summary   146
Q&A   147
Workshop   148
Activities   149
HOUR 8: Writing Functions: Part II   151
Errors and Warnings   151
Checking Inputs   155
The Ellipsis   157
Checking Multivalue Inputs   162
Using Input Definition   164
Summary   168
Q&A   168
Workshop   170
Activities   171
HOUR 9: Loops and Summaries   173
Repetitive Tasks   173
The “apply” Family of Functions   181
The apply Function   183
The lapply Function   195
The sapply Function   204
The tapply Function   208
Summary   213
Q&A   213
Workshop   214
Activities   216
HOUR 10: Importing and Exporting   217
Working with Text Files   217
Relational Databases   223
Working with Microsoft Excel   226
Summary   231
Q&A   232
Workshop   232
Activities   233
HOUR 11: Data Manipulation and Transformation   235
Sorting   236
Appending   237
Merging   238
Duplicate Values   241
Restructuring   242
Data Aggregation   249
Summary   258
Q&A   258
Workshop   259
Activities   259
HOUR 12: Efficient Data Handling in R   261
dplyr: A New Way of Handling Data   261
Efficient Data Handling with data   table   273
Summary   282
Q&A   283
Workshop   283
Activities   284
HOUR 13: Graphics   287
Graphics Devices and Colors   287
High-Level Graphics Functions   289
Low-Level Graphics Functions   298
Graphical Parameters   304
Controlling the Layout   305
Summary   308
Q&A   309
Workshop   309
Activities   311
HOUR 14: The ggplot2 Package for Graphics   313
The Philosophy of ggplot2   313
Quick Plots and Basic Control   314
Changing Plot Types   317
Aesthetics   320
Paneling (a   k   a Faceting)   328
Custom Plots   333
Themes and Layout   338
The ggvis Evolution   342
Summary   342
Q&A   343
Workshop   343
Activities   344
HOUR 15: Lattice Graphics   345
The History of Trellis Graphics   345
The Lattice Package   346
Creating a Simple Lattice Graph   346
Graph Options   356
Multiple Variables   358
Groups of Data   360
Using Panels   362
Controlling Styles   372
Summary   376
Q&A   377
Workshop   378
Activities   378
HOUR 16: Introduction to R Models and Object Orientation   379
Statistical Models in R   379
Simple Linear Models   380
Assessing a Model in R   382
Multiple Linear Regression   391
Interaction Terms   396
Factor Independent Variables   398
Variable Transformations   402
R and Object Orientation   405
Summary   407
Q&A   408
Workshop   408
Activities   409
HOUR 17: Common R Models   411
Generalized Linear Models   411
Nonlinear Models   423
Survival Analysis   430
Time Series Analysis   441
Summary   452
Q&A   452
Workshop   452
Activities   453
HOUR 18: Code Efficiency   455
Determining Efficiency   455
Initialization   458
Vectorization   459
Using Alternative Functions   462
Managing Memory Usage   463
Integrating with C++   464
Summary   468
Q&A   469
Workshop   469
Activities   470
HOUR 19: Package Building   471
Why Build an R Package?   471
The Structure of an R Package   472
Code Quality   476
Automated Documentation with roxygen2   477
Building a Package with devtools   482
Summary   485
Q&A   485
Workshop   486
Activities   487
HOUR 20: Advanced Package Building   489
Extending R Packages   489
Developing a Test Framework   490
Including Data in Packages   494
Including a User Guide   496
Code Using Rcpp   501
Summary   502
Q&A   502
Workshop   503
Activities   504
HOUR 21: Writing R Classes   505
What Is a Class?   505
Creating a New S3 Class   509
Generic Functions and Methods   511
Inheritance in S3   516
Documenting S3   518
Limitations of S3   518
Summary   519
Q&A   519
Workshop   520
Activities   520
HOUR 22: Formal Class Systems   523
S4   523
Reference Classes   535
R6 Classes   542
Other Class Systems   544
Summary   544
Q&A   545
Workshop   545
Activities   546
HOUR 23: Dynamic Reporting   547
What Is Dynamic Reporting?   547
An Introduction to knitr   548
Simple Reports with RMarkdown   548
Reporting with LaTeX   553
Summary   557
Q&A   558
Workshop   558
Activities   559
HOUR 24: Building Web Applications with Shiny   561
A Simple Shiny Application   561
Reactive Functions   566
Interactive Documents   569
Sharing Shiny Applications   570
Summary   571
Q&A   571
Workshop   571
Activities   572
APPENDIX: Installation   573
Installing R   573
Installing Rtools for Windows   575
Installing the RStudio IDE   577
Index   579