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Auteur
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.
Texte du rabat
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.
Résumé
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:
Contenu
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
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