Synopses & Reviews
Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals
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
Review
With the increasing popularity of R--an open source language forstatistical analysis--more books on the subject are published all the time. This one, by a professional statistician and consultant inbusiness analytics, is perhaps the best basic introduction to the language. Oriented at beginners, the goal of the book is to describethe "20 percent of R functionality you'll need to accomplish 80 percent of modern data tasks"--and it succeeds. Beginning with thefundamental tasks of installation, a description of R environment and a primer on RStudio--a powerful IDE available for all majoroperating systems, it dives straight into the subjects of data input and output, manipulation and analysis, covering R's powerful graphiccapabilities, introducing the reader to the concepts of function creation, data reshaping and string manipulation. Further chaptersdiscuss fundamental concepts of statistical methods themselves in the context of R environment, describing relevant R packages thatimplement these functions. Inexpensive and comprehensive, this well-written work is one of the best introductions to R, and statistics in general, and is ideal for self-study.Annotation ©2014 Ringgold, Inc., Portland, OR (protoview.com)
Review
With the increasing popularity of R--an open source language forstatistical analysis--more books on the subject are published all the time. This one, by a professional statistician and consultant inbusiness analytics, is perhaps the best basic introduction to the language. Oriented at beginners, the goal of the book is to describethe "20 percent of R functionality you'll need to accomplish 80 percent of modern data tasks"--and it succeeds. Beginning with thefundamental tasks of installation, a description of R environment and a primer on RStudio--a powerful IDE available for all majoroperating systems, it dives straight into the subjects of data input and output, manipulation and analysis, covering R's powerful graphiccapabilities, introducing the reader to the concepts of function creation, data reshaping and string manipulation. Further chaptersdiscuss fundamental concepts of statistical methods themselves in the context of R environment, describing relevant R packages thatimplement these functions. Inexpensive and comprehensive, this well-written work is one of the best introductions to R, and statistics in general, and is ideal for self-study.Annotation ©2014 Ringgold, Inc., Portland, OR (protoview.com)
Review
With the increasing popularity of R--an open source language forstatistical analysis--more books on the subject are published all the time. This one, by a professional statistician and consultant inbusiness analytics, is perhaps the best basic introduction to the language. Oriented at beginners, the goal of the book is to describethe "20 percent of R functionality you'll need to accomplish 80 percent of modern data tasks"--and it succeeds. Beginning with thefundamental tasks of installation, a description of R environment and a primer on RStudio--a powerful IDE available for all majoroperating systems, it dives straight into the subjects of data input and output, manipulation and analysis, covering R's powerful graphiccapabilities, introducing the reader to the concepts of function creation, data reshaping and string manipulation. Further chaptersdiscuss fundamental concepts of statistical methods themselves in the context of R environment, describing relevant R packages thatimplement these functions. Inexpensive and comprehensive, this well-written work is one of the best introductions to R, and statistics in general, and is ideal for self-study.Annotation ©2014 Ringgold, Inc., Portland, OR (protoview.com)
Review
With the increasing popularity of R--an open source language forstatistical analysis--more books on the subject are published all the time. This one, by a professional statistician and consultant inbusiness analytics, is perhaps the best basic introduction to the language. Oriented at beginners, the goal of the book is to describethe "20 percent of R functionality you'll need to accomplish 80 percent of modern data tasks"--and it succeeds. Beginning with thefundamental tasks of installation, a description of R environment and a primer on RStudio--a powerful IDE available for all majoroperating systems, it dives straight into the subjects of data input and output, manipulation and analysis, covering R's powerful graphiccapabilities, introducing the reader to the concepts of function creation, data reshaping and string manipulation. Further chaptersdiscuss fundamental concepts of statistical methods themselves in the context of R environment, describing relevant R packages thatimplement these functions. Inexpensive and comprehensive, this well-written work is one of the best introductions to R, and statistics in general, and is ideal for self-study.Annotation ©2014 Ringgold, Inc., Portland, OR (protoview.com)
Synopsis
Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals
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
Synopsis
Statistical computation for non-statisticians like computer programmers, social scientists, biologists, physicists, and quants.
Using the free, open source R language, scientists, financial analysts, public policy professionals, and programmers can build powerful statistical models capable of answering many of their most challenging questions. But, for non-statisticians, R can be difficult to learn—and most books on the subject assume far too much knowledge to help the non-statistician.
R for Everyone is the solution. Drawing on his extensive experience teaching new users through the New York City R User Group, professional statistician Jared Lander has written the perfect R tutorial for everyone who’s new to statistical programming and modeling. Offering extensive hands-on practice and sample code, Lander covers all this and more:
- Downloading, installing, and getting started with R
- Navigating and mastering the R environment
- Learning basic techniques, from control statements to data manipulation
- Importing data from SAS, SPSS, Stata, web sites, or elsewhere
- Performing essential statistical tests
- Building, comparing, and diagnosing models
- Developing your own R packages
- Connecting with and learning from the global R user community
By the time you’re done, you won’t just understand how to write R programs: you’ll be ready to use R to tackle the statistical problems you care about most.
Synopsis
Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals
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 nonstatisticians 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 all newcomers 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, hierarchical clustering, and other techniques
• Preparing reports, slideshows, and web pages
• Building reusable R packages with devtools and Rcpp
• Getting involved with the R global community
About the Author
Jared P. Lander is the owner of Lander Analytics, a statistical consultanting 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
Table of Contents
Foreword xiii
Preface xv
Acknowledgments xix
About the Author xxi
Chapter 1: Getting R 11.1 Downloading R 1
1.2 R Version 2
1.3 32-bit vs. 64-bit 2
1.4 Installing 2
1.5 Revolution R Community Edition 10
1.6 Conclusion 11
Chapter 2: The R Environment 13
2.1 Command Line Interface 14
2.2 RStudio 15
2.3 Revolution Analytics RPE 26
2.4 Conclusion 27
Chapter 3: R Packages 29
3.1 Installing Packages 29
3.2 Loading Packages 32
3.3 Building a Package 33
3.4 Conclusion 33
Chapter 4: Basics of R 35
4.1 Basic Math 35
4.2 Variables 36
4.3 Data Types 38
4.4 Vectors 43
4.5 Calling Functions 49
4.6 Function Documentation 49
4.7 Missing Data 50
4.8 Conclusion 51
Chapter 5: Advanced Data Structures 53
5.1 data.frames 53
5.2 Lists 61
5.3 Matrices 68
5.4 Arrays 71
5.5 Conclusion 72
Chapter 6: Reading Data into R 73
6.1 Reading CSVs 73
6.2 Excel Data 74
6.3 Reading from Databases 75
6.4 Data from Other Statistical Tools 77
6.5 R Binary Files 77
6.6 Data Included with R 79
6.7 Extract Data from Web Sites 80
6.8 Conclusion 81
Chapter 7: Statistical Graphics 83
7.1 Base Graphics 83
7.2 ggplot2 86
7.3 Conclusion 98
Chapter 8: Writing R Functions 99
8.1 Hello, World! 99
8.2 Function Arguments 100
8.3 Return Values 103
8.4 do.call 104
8.5 Conclusion 104
Chapter 9: Control Statements 105
9.1 if and else 105
9.2 switch 108
9.3 ifelse 109
9.4 Compound Tests 111
9.5 Conclusion 112
Chapter 10: Loops, the Un-R Way to Iterate 113
10.1 for Loops 113
10.2 while Loops 115
10.3 Controlling Loops 115
10.4 Conclusion 116
Chapter 11: Group Manipulation 117
11.1 Apply Family 117
11.2 aggregate 120
11.3 plyr 124
11.4 data.table 129
11.5 Conclusion 139
Chapter 12: Data Reshaping 141
12.1 cbind and rbind 141
12.2 Joins 142
12.3 reshape2 149
12.4 Conclusion 153
Chapter 13: Manipulating Strings 155
13.1 paste 155
13.2 sprintf 156
13.3 Extracting Text 157
13.4 Regular Expressions 161
13.5 Conclusion 169
Chapter 14: Probability Distributions 171
14.1 Normal Distribution 171
14.2 Binomial Distribution 176
14.3 Poisson Distribution 182
14.4 Other Distributions 185
14.5 Conclusion 186
Chapter 15: Basic Statistics 187
15.1 Summary Statistics 187
15.2 Correlation and Covariance 191
15.3 T-Tests 200
15.4 ANOVA 207
15.5 Conclusion 210
Chapter 16: Linear Models 211
16.1 Simple Linear Regression 211
16.2 Multiple Regression 216
16.3 Conclusion 232
Chapter 17: Generalized Linear Models 233
17.1 Logistic Regression 233
17.2 Poisson Regression 237
17.3 Other Generalized Linear Models 240
17.4 Survival Analysis 240
17.5 Conclusion 245
Chapter 18: Model Diagnostics 247
18.1 Residuals 247
18.2 Comparing Models 253
18.3 Cross-Validation 257
18.4 Bootstrap 262
18.5 Stepwise Variable Selection 265
18.6 Conclusion 269
Chapter 19: Regularization and Shrinkage 271
19.1 Elastic Net 271
19.2 Bayesian Shrinkage 290
19.3 Conclusion 295
Chapter 20: Nonlinear Models 297
20.1 Nonlinear Least Squares 297
20.2 Splines 300
20.3 Generalized Additive Models 304
20.4 Decision Trees 310
20.5 Random Forests 312
20.6 Conclusion 313
Chapter 21: Time Series and Autocorrelation 315
21.1 Autoregressive Moving Average 315
21.2 VAR 322
21.3 GARCH 327
21.4 Conclusion 336
Chapter 22: Clustering 337
22.1 K-means 337
22.2 PAM 345
22.3 Hierarchical Clustering 352
22.4 Conclusion 357
Chapter 23: Reproducibility, Reports and Slide Shows with knitr 359
23.1 Installing a LATEX Program 359
23.2 LATEX Primer 360
23.3 Using knitr with LATEX 362
23.4 Markdown Tips 367
23.5 Using knitr and Markdown 368
23.6 pandoc 369
23.7 Conclusion 371
Chapter 24: Building R Packages 373
24.1 Folder Structure 373
24.2 Package Files 373
24.3 Package Documentation 380
24.4 Checking, Building and Installing 383
24.5 Submitting to CRAN 384
24.6 C++ Code 384
24.7 Conclusion 390
Appendix A: Real-Life Resources 391
A.1 Meetups 391
A.2 Stackoverflow 392
A.3 Twitter 393
A.4 Conferences 393
A.5 Web Sites 393
A.6 Documents 394
A.7 Books 394
A.8 Conclusion 394
Appendix B: Glossary 395
List of Figures 409
List of Tables 417
General Index 419
Index of Functions 429
Index of Packages 433
Index of People 435
Data Index 437