R for Everyone: Advanced Analytics and Graphics

Autorentext
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.

Klappentext

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.

Zusammenfassung

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

Inhalt

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 2…