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Hadley Wickham and Garrett Grolemund - R for Data Science [2017, PDF, ENG]

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R for Data Science
Год издания: 2017
Автор: Hadley Wickham and Garrett Grolemund
Жанр или тематика: R
Издательство: O’Reilly Media, Inc.
ISBN: 978-1491910399
Язык: Английский
Формат: PDF
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 520
Описание: Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.
Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way.
You’ll learn how to:
Wrangle—transform your datasets into a form convenient for analysis
Program—learn powerful R tools for solving data problems with greater clarity and ease
Explore—examine your data, generate hypotheses, and quickly test them
Model—provide a low-dimensional summary that captures true "signals" in your dataset
Communicate—learn R Markdown for integrating prose, code, and results

Примеры страниц



Оглавление

Part I. Explore
1. Data Visualization with ggplot2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Introduction 3
First Steps 4
Aesthetic Mappings 7
Common Problems 13
Facets 14
Geometric Objects 16
Statistical Transformations 22
Position Adjustments 27
Coordinate Systems 31
The Layered Grammar of Graphics 34
2. Workflow: Basics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Coding Basics 37
What’s in a Name? 38
Calling Functions 39
3. Data Transformation with dplyr. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Introduction 43
Filter Rows with filter() 45
Arrange Rows with arrange() 50
Select Columns with select() 51
Add New Variables with mutate() 54
Grouped Summaries with summarize() 59
Grouped Mutates (and Filters) 73
4. Workflow: Scripts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
Running Code 78
RStudio Diagnostics 79
5. Exploratory Data Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
Introduction 81
Questions 82
Variation 83
Missing Values 91
Covariation 93
Patterns and Models 105
ggplot2 Calls 108
Learning More 108
6. Workflow: Projects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
What Is Real? 111
Where Does Your Analysis Live? 113
Paths and Directories 113
RStudio Projects 114
Summary 116
Part II. Wrangle
7. Tibbles with tibble. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
Introduction 119
Creating Tibbles 119
Tibbles Versus data.frame 121
Interacting with Older Code 123
8. Data Import with readr. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
Introduction 125
Getting Started 125
Parsing a Vector 129
Parsing a File 137
Writing to a File 143
Other Types of Data 145
9. Tidy Data with tidyr. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
Introduction 147
Tidy Data 148
Spreading and Gathering 151
Separating and Pull 157
Missing Values 161
Case Study 163
Nontidy Data 168
10. Relational Data with dplyr. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
Introduction 171
nycflights13 172
Keys 175
Mutating Joins 178
Filtering Joins 188
Join Problems 191
Set Operations 192
11. Strings with stringr. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
Introduction 195
String Basics 195
Matching Patterns with Regular Expressions 200
Tools 207
Other Types of Pattern 218
Other Uses of Regular Expressions 221
stringi 222
12. Factors with forcats. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223
Introduction 223
Creating Factors 224
General Social Survey 225
Modifying Factor Order 227
Modifying Factor Levels 232
13. Dates and Times with lubridate. . . . . . . . . . . . . . . . . . . . . . . . . . . . 237
Introduction 237
Creating Date/Times 238
Date-Time Components 243
Time Spans 249
Time Zones 254
Part III. Program
14. Pipes with magrittr. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261
Introduction 261
Piping Alternatives 261
When Not to Use the Pipe 266
Other Tools from magrittr 266
15. Functions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269
Introduction 269
When Should You Write a Function? 270
Functions Are for Humans and Computers 273
Conditional Execution 276
Function Arguments 280
Return Values 285
Environment 288
16. Vectors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291
Introduction 291
Vector Basics 292
Important Types of Atomic Vector 293
Using Atomic Vectors 296
Recursive Vectors (Lists) 302
Attributes 307
Augmented Vectors 309
17. Iteration with purrr. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313
Introduction 313
For Loops 314
For Loop Variations 317
For Loops Versus Functionals 322
The Map Functions 325
Dealing with Failure 329
Mapping over Multiple Arguments 332
Walk 335
Other Patterns of For Loops 336
Part IV. Model
18. Model Basics with modelr. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345
Introduction 345
A Simple Model 346
Visualizing Models 354
Formulas and Model Families 358
Missing Values 371
Other Model Families 372
19. Model Building. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375
Introduction 375
Why Are Low-Quality Diamonds More Expensive? 376
What Affects the Number of Daily Flights? 384
Learning More About Models 396
20. Many Models with purrr and broom. . . . . . . . . . . . . . . . . . . . . . . . . 397
Introduction 397
gapminder 398
List-Columns 409
Creating List-Columns 411
Simplifying List-Columns 416
Making Tidy Data with broom 419
Part V. Communicate
21. R Markdown. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423
Introduction 423
R Markdown Basics 424
Text Formatting with Markdown 427
Code Chunks 428
Troubleshooting 435
YAML Header 435
Learning More 438
22. Graphics for Communication with ggplot2. . . . . . . . . . . . . . . . . . . 441
Introduction 441
Label 442
Annotations 445
Scales 451
Zooming 461
Themes 462
Saving Your Plots 464
Learning More 467
23. R Markdown Formats. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469
Introduction 469
Output Options 470
Documents 470
Notebooks 471
Presentations 472
Dashboards 473
Interactivity 474
Websites 477
Other Formats 477
Learning More 478
24. R Markdown Workflow. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 479
Доп. информация: https://github.com/hadley/r4ds
http://r4ds.had.co.nz/
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