R for Everyone: Advanced Analytics and Graphics (2nd Edition)

Description

Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone, Second Edition, is the solution.

Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks.

Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import, manipulation, and visualization; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques.

By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most.

Table of Contents

1. Foreword xv
2. Preface xvii
3. Acknowledgments xxi
4. About the Author xxv
5. Chapter 1: Getting R 1
   1. 1.1 Downloading R 1
   2. 1.2 R Version 2
   3. 1.3 32-bit vs. 64-bit 2
   4. 1.4 Installing 2
   5. 1.5 Microsoft R Open 14
   6. 1.6 Conclusion 14
6. Chapter 2: The R Environment 15
   1. 2.1 Command Line Interface 16
   2. 2.2 RStudio 17
   3. 2.3 Microsoft Visual Studio 31
   4. 2.4 Conclusion 31
7. Chapter 3: R Packages 33
   1. 3.1 Installing Packages 33
   2. 3.2 Loading Packages 36
   3. 3.3 Building a Package 37
   4. 3.4 Conclusion 37
8. Chapter 4: Basics of R 39
   1. 4.1 Basic Math 39
   2. 4.2 Variables 40
   3. 4.3 Data Types 42
   4. 4.4 Vectors 47
   5. 4.5 Calling Functions 52
   6. 4.6 Function Documentation 52
   7. 4.7 Missing Data 53
   8. 4.8 Pipes 54
   9. 4.9 Conclusion 55
9. Chapter 5: Advanced Data Structures 57
   1. 5.1 data.frames 57
   2. 5.2 Lists 64
   3. 5.3 Matrices 70
   4. 5.4 Arrays 73
   5. 5.5 Conclusion 74
10. Chapter 6: Reading Data into R 75
    1. 6.1 Reading CSVs 75
    2. 6.2 Excel Data 79
    3. 6.3 Reading from Databases 81
    4. 6.4 Data from Other Statistical Tools 84
    5. 6.5 R Binary Files 85
    6. 6.6 Data Included with R 87
    7. 6.7 Extract Data from Web Sites 88
    8. 6.8 Reading JSON Data 90
    9. 6.9 Conclusion 92
11. Chapter 7: Statistical Graphics 93
    1. 7.1 Base Graphics 93
    2. 7.2 ggplot2 96
    3. 7.3 Conclusion 110
12. Chapter 8: Writing R functions 111
    1. 8.1 Hello, World! 111
    2. 8.2 Function Arguments 112
    3. 8.3 Return Values 114
    4. 8.4 do.call 115
    5. 8.5 Conclusion 116
13. Chapter 9: Control Statements 117
    1. 9.1 if and else 117
    2. 9.2 switch 120
    3. 9.3 ifelse 121
    4. 9.4 Compound Tests 123
    5. 9.5 Conclusion 123
14. Chapter 10: Loops, the Un-R Way to Iterate 125
    1. 10.1 for Loops 125
    2. 10.2 while Loops 127
    3. 10.3 Controlling Loops 127
    4. 10.4 Conclusion 128
15. Chapter 11: Group Manipulation 129
    1. 11.1 Apply Family 129
    2. 11.2 aggregate 132
    3. 11.3 plyr 136
    4. 11.4 data.table 140
    5. 11.5 Conclusion 150
16. Chapter 12: Faster Group Manipulation with dplyr 151
    1. 12.1 Pipes 151
    2. 12.2 tbl 152
    3. 12.3 select 153
    4. 12.4 filter 161
    5. 12.5 slice 167
    6. 12.6 mutate 168
    7. 12.7 summarize 171
    8. 12.8 group_by 172
    9. 12.9 arrange 173
    10. 12.10 do 174
    11. 12.11 dplyr with Databases 176
    12. 12.12 Conclusion 178
17. Chapter 13: Iterating with purrr 179
    1. 13.1 map 179
    2. 13.2 map with Specified Types 181
    3. 13.3 Iterating over a data.frame 186
    4. 13.4 map with Multiple Inputs 187
    5. 13.5 Conclusion 188
18. Chapter 14: Data Reshaping 189
    1. 14.1 cbind and rbind 189
    2. 14.2 Joins 190
    3. 14.3 reshape2 197
    4. 14.4 Conclusion 200
19. Chapter 15: Reshaping Data in the Tidyverse 201
    1. 15.1 Binding Rows and Columns 201
    2. 15.2 Joins with dplyr 202
    3. 15.3 Converting Data Formats 207
    4. 15.4 Conclusion 210
20. Chapter 16: Manipulating Strings 211
    1. 16.1 paste 211
    2. 16.2 sprintf 212
    3. 16.3 Extracting Text 213
    4. 16.4 Regular Expressions 217
    5. 16.5 Conclusion 224
21. Chapter 17: Probability Distributions 225
    1. 17.1 Normal Distribution 225
    2. 17.2 Binomial Distribution 230
    3. 17.3 Poisson Distribution 235
    4. 17.4 Other Distributions 238
    5. 17.5 Conclusion 240
22. Chapter 18: Basic Statistics 241
    1. 18.1 Summary Statistics 241
    2. 18.2 Correlation and Covariance 244
    3. 18.3 T-Tests 252
    4. 18.4 ANOVA 260
    5. 18.5 Conclusion 263
23. Chapter 19: Linear Models 265
    1. 19.1 Simple Linear Regression 265
    2. 19.2 Multiple Regression 270
    3. 19.3 Conclusion 287
24. Chapter 20: Generalized Linear Models 289
    1. 20.1 Logistic Regression 289
    2. 20.2 Poisson Regression 293
    3. 20.3 Other Generalized Linear Models 297
    4. 20.4 Survival Analysis 297
    5. 20.5 Conclusion 302
25. Chapter 21: Model Diagnostics 303
    1. 21.1 Residuals 303
    2. 21.2 Comparing Models 309
    3. 21.3 Cross-Validation 313
    4. 21.4 Bootstrap 318
    5. 21.5 Stepwise Variable Selection 321
    6. 21.6 Conclusion 324
26. Chapter 22: Regularization and Shrinkage 325
    1. 22.1 Elastic Net 325
    2. 22.2 Bayesian Shrinkage 342
    3. 22.3 Conclusion 346
27. Chapter 23: Nonlinear Models 347
    1. 23.1 Nonlinear Least Squares 347
    2. 23.2 Splines 350
    3. 23.3 Generalized Additive Models 353
    4. 23.4 Decision Trees 359
    5. 23.5 Boosted Trees 361
    6. 23.6 Random Forests 364
    7. 23.7 Conclusion 366
28. Chapter 24: Time Series and Autocorrelation 367
    1. 24.1 Autoregressive Moving Average 367
    2. 24.2 VAR 374
    3. 24.3 GARCH 379
    4. 24.4 Conclusion 388
29. Chapter 25: Clustering 389
    1. 25.1 K-means 389
    2. 25.2 PAM 397
    3. 25.3 Hierarchical Clustering 403
    4. 25.4 Conclusion 407
30. Chapter 26: Model Fitting with Caret 409
    1. 26.1 Caret Basics 409
    2. 26.2 Caret Options 409
    3. 26.3 Tuning a Boosted Tree 411
    4. 26.4 Conclusion 415
31. Chapter 27: Reproducibility and Reports with knitr 417
    1. 27.1 Installing a LaTeX Program 417
    2. 27.2 LaTeX Primer 418
    3. 27.3 Using knitr with LaTeX 420
    4. 27.4 Conclusion 426
32. Chapter 28: Rich Documents with RMarkdown 427
    1. 28.1 Document Compilation 427
    2. 28.2 Document Header 427
    3. 28.3 Markdown Primer 429
    4. 28.4 Markdown Code Chunks 430
    5. 28.5 htmlwidgets 432
    6. 28.6 RMarkdown Slideshows 444
    7. 28.7 Conclusion 446
33. Chapter 29: Interactive Dashboards with Shiny 447
    1. 29.1 Shiny in RMarkdown 447
    2. 29.2 Reactive Expressions in Shiny 452
    3. 29.3 Server and UI 454
    4. 29.4 Conclusion 463
34. Chapter 30: Building R Packages 465
    1. 30.1 Folder Structure 465
    2. 30.2 Package Files 465
    3. 30.3 Package Documentation 472
    4. 30.4 Tests 475
    5. 30.5 Checking, Building and Installing 477
    6. 30.6 Submitting to CRAN 479
    7. 30.7 C++ Code 479
    8. 30.8 Conclusion 484
35. Appendix A: Real-Life Resources 485
    1. A.1 Meetups 485
    2. A.2 Stack Overflow 486
    3. A.3 Twitter 487
    4. A.4 Conferences 487
    5. A.5 Web Sites 488
    6. A.6 Documents 488
    7. A.7 Books 488
    8. A.8 Conclusion 489
36. Appendix B: Glossary 491
    1. List of Figures 507
    2. List of Tables 513
    3. General Index 515
    4. Index of Functions 521
    5. Index of Packages 527
    6. Index of People 529
    7. Data Index 531

Author Biography

Jared Lander is the Founder and CEO of Lander Analytics a data science consultancy based in New York City, the Organizer of the New York Open Statistical Programming Meetup and the New York R Conference and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and a bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations ranges from music and fund raising to finance and humanitarian relief efforts.

He specializes in data management, multilevel models, machine learning, generalized linear models, data management and statistical computing. He is the author of R for Everyone: Advanced Analytics and Graphics, a book about R Programming geared toward Data Scientists and Non-Statisticians alike.