R for Everyone
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R for Everyone : Advanced Analytics and Graphics

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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 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, and manipulation; 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.



Coverage Includes:



Exploring R, RStudio, and R packages
Using R for math: variable types, vectors, calling functions, and more
Exploiting data structures, including data.frames, matrices, and lists
Creating attractive, intuitive statistical graphics
Writing user-defined functions
Controlling program flow with if, ifelse, and complex checks
Improving program efficiency with group manipulations
Combining and reshaping multiple datasets
Manipulating strings using R's facilities and regular expressions
Creating normal, binomial, and Poisson probability distributions
Programming basic statistics: mean, standard deviation, and t-tests
Building linear, generalized linear, and nonlinear models
Assessing the quality of models and variable selection
Preventing overfitting, using the Elastic Net and Bayesian methods
Analyzing univariate and multivariate time series data
Grouping data via K-means and hierarchical clustering
Preparing reports, slideshows, and web pages with knitr
Building reusable R packages with devtools and Rcpp
Getting involved with the R global community
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Pearson Programming and Web Development

Product details

  • Paperback | 560 pages
  • 180 x 230 x 20mm | 699g
  • Addison Wesley
  • Boston, United States
  • English
  • 2nd edition
  • 013454692X
  • 9780134546926
  • 95,913

Table of contents

Foreword xv

Preface xvii

Acknowledgments xxi

About the Author xxv






Chapter 1: Getting R 1

1.1 Downloading R 1

1.2 R Version 2

1.3 32-bit vs. 64-bit 2

1.4 Installing 2

1.5 Microsoft R Open 14

1.6 Conclusion 14




Chapter 2: The R Environment 15

2.1 Command Line Interface 16

2.2 RStudio 17

2.3 Microsoft Visual Studio 31

2.4 Conclusion 31




Chapter 3: R Packages 33

3.1 Installing Packages 33

3.2 Loading Packages 36

3.3 Building a Package 37

3.4 Conclusion 37




Chapter 4: Basics of R 39

4.1 Basic Math 39

4.2 Variables 40

4.3 Data Types 42

4.4 Vectors 47

4.5 Calling Functions 52

4.6 Function Documentation 52

4.7 Missing Data 53

4.8 Pipes 54

4.9 Conclusion 55




Chapter 5: Advanced Data Structures 57

5.1 data.frames 57

5.2 Lists 64

5.3 Matrices 70

5.4 Arrays 73

5.5 Conclusion 74




Chapter 6: Reading Data into R 75

6.1 Reading CSVs 75

6.2 Excel Data 79

6.3 Reading from Databases 81

6.4 Data from Other Statistical Tools 84

6.5 R Binary Files 85

6.6 Data Included with R 87

6.7 Extract Data from Web Sites 88

6.8 Reading JSON Data 90

6.9 Conclusion 92




Chapter 7: Statistical Graphics 93

7.1 Base Graphics 93

7.2 ggplot2 96

7.3 Conclusion 110




Chapter 8: Writing R functions 111

8.1 Hello, World! 111

8.2 Function Arguments 112

8.3 Return Values 114

8.4 do.call 115

8.5 Conclusion 116




Chapter 9: Control Statements 117

9.1 if and else 117

9.2 switch 120

9.3 ifelse 121

9.4 Compound Tests 123

9.5 Conclusion 123




Chapter 10: Loops, the Un-R Way to Iterate 125

10.1 for Loops 125

10.2 while Loops 127

10.3 Controlling Loops 127

10.4 Conclusion 128




Chapter 11: Group Manipulation 129

11.1 Apply Family 129

11.2 aggregate 132

11.3 plyr 136

11.4 data.table 140

11.5 Conclusion 150




Chapter 12: Faster Group Manipulation with dplyr 151

12.1 Pipes 151

12.2 tbl 152

12.3 select 153

12.4 filter 161

12.5 slice 167

12.6 mutate 168

12.7 summarize 171

12.8 group_by 172

12.9 arrange 173

12.10 do 174

12.11 dplyr with Databases 176

12.12 Conclusion 178




Chapter 13: Iterating with purrr 179

13.1 map 179

13.2 map with Specified Types 181

13.3 Iterating over a data.frame 186

13.4 map with Multiple Inputs 187

13.5 Conclusion 188




Chapter 14: Data Reshaping 189

14.1 cbind and rbind 189

14.2 Joins 190

14.3 reshape2 197

14.4 Conclusion 200




Chapter 15: Reshaping Data in the Tidyverse 201

15.1 Binding Rows and Columns 201

15.2 Joins with dplyr 202

15.3 Converting Data Formats 207

15.4 Conclusion 210




Chapter 16: Manipulating Strings 211

16.1 paste 211

16.2 sprintf 212

16.3 Extracting Text 213

16.4 Regular Expressions 217

16.5 Conclusion 224




Chapter 17: Probability Distributions 225

17.1 Normal Distribution 225

17.2 Binomial Distribution 230

17.3 Poisson Distribution 235

17.4 Other Distributions 238

17.5 Conclusion 240




Chapter 18: Basic Statistics 241

18.1 Summary Statistics 241

18.2 Correlation and Covariance 244

18.3 T-Tests 252

18.4 ANOVA 260

18.5 Conclusion 263




Chapter 19: Linear Models 265

19.1 Simple Linear Regression 265

19.2 Multiple Regression 270

19.3 Conclusion 287




Chapter 20: Generalized Linear Models 289

20.1 Logistic Regression 289

20.2 Poisson Regression 293

20.3 Other Generalized Linear Models 297

20.4 Survival Analysis 297

20.5 Conclusion 302




Chapter 21: Model Diagnostics 303

21.1 Residuals 303

21.2 Comparing Models 309

21.3 Cross-Validation 313

21.4 Bootstrap 318

21.5 Stepwise Variable Selection 321

21.6 Conclusion 324




Chapter 22: Regularization and Shrinkage 325

22.1 Elastic Net 325

22.2 Bayesian Shrinkage 342

22.3 Conclusion 346




Chapter 23: Nonlinear Models 347

23.1 Nonlinear Least Squares 347

23.2 Splines 350

23.3 Generalized Additive Models 353

23.4 Decision Trees 359

23.5 Boosted Trees 361

23.6 Random Forests 364

23.7 Conclusion 366




Chapter 24: Time Series and Autocorrelation 367

24.1 Autoregressive Moving Average 367

24.2 VAR 374

24.3 GARCH 379

24.4 Conclusion 388




Chapter 25: Clustering 389

25.1 K-means 389

25.2 PAM 397

25.3 Hierarchical Clustering 403

25.4 Conclusion 407




Chapter 26: Model Fitting with Caret 409

26.1 Caret Basics 409

26.2 Caret Options 409

26.3 Tuning a Boosted Tree 411

26.4 Conclusion 415




Chapter 27: Reproducibility and Reports with knitr 417

27.1 Installing a LaTeX Program 417

27.2 LaTeX Primer 418

27.3 Using knitr with LaTeX 420

27.4 Conclusion 426




Chapter 28: Rich Documents with RMarkdown 427

28.1 Document Compilation 427

28.2 Document Header 427

28.3 Markdown Primer 429

28.4 Markdown Code Chunks 430

28.5 htmlwidgets 432

28.6 RMarkdown Slideshows 444

28.7 Conclusion 446




Chapter 29: Interactive Dashboards with Shiny 447

29.1 Shiny in RMarkdown 447

29.2 Reactive Expressions in Shiny 452

29.3 Server and UI 454

29.4 Conclusion 463




Chapter 30: Building R Packages 465

30.1 Folder Structure 465

30.2 Package Files 465

30.3 Package Documentation 472

30.4 Tests 475

30.5 Checking, Building and Installing 477

30.6 Submitting to CRAN 479

30.7 C++ Code 479

30.8 Conclusion 484




Appendix A: Real-Life Resources 485

A.1 Meetups 485

A.2 Stack Overflow 486

A.3 Twitter 487

A.4 Conferences 487

A.5 Web Sites 488

A.6 Documents 488

A.7 Books 488

A.8 Conclusion 489




Appendix B: Glossary 491






List of Figures 507

List of Tables 513

General Index 515

Index of Functions 521

Index of Packages 527

Index of People 529

Data Index 531
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About Jared Lander

Jared P. Lander is the owner of Lander Analytics, a statistical consulting firm based in New York City, the organizer of the New York Open Statistical Programming Meetup and an adjunct professor of statistics at Columbia University. He is also a tour guide for Scott's Pizza Tours and an advisor to Brewla Bars, a gourmet ice pop startup. With an M.A. from Columbia University in statistics and a B.A. from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations spans politics, tech startups, fund raising, music, finance, healthcare, and humanitarian relief efforts. He specializes in data management, multilevel models, machine learning, generalized linear models, visualization, data management, and statistical computing.
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Rating details

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3.6 out of 5 stars
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