作者:
Hadley Wickham
/
Garrett Grolemund
出版社: 东南大学出版社
副标题: R语言实现
原作名: R for Data Science
出版年: 2017-10-1
页数: 492
定价: 108.00
装帧: 平装
ISBN: 9787564173531
出版社: 东南大学出版社
副标题: R语言实现
原作名: R for Data Science
出版年: 2017-10-1
页数: 492
定价: 108.00
装帧: 平装
ISBN: 9787564173531
豆瓣评分
内容简介 · · · · · ·
学习如何利用R语言洞察、知晓、理解原始数据。
《数据科学:R语言实现(影印版 英文版)》介绍了R、RStudio以及tidyverse,后者是一组相互配合工作的R包,能够使数据科学快速、流畅、富有乐趣。
《数据科学:R语言实现(影印版 英文版)》旨在帮助你尽快地上手数据科学相关的工作,并不要求读者具备编程经验。
《数据科学:R语言实现(影印版 英文版)》Hadley Wickham和Garrett Grolernund将一步步指导你对数据进行导入、提炼、探索以及建模并发布成果。除了处理数据所需的基本工具,你还将会对数据科学的周期拥有一个完整的、宏观的理解。
作者简介 · · · · · ·
Hadley Wickham 是 RStudio 的首席科学家以及R基金会成员。他构建了一套使数据科学变得更加快捷、富有乐趣的工具。可以通过其个人网站 http://hadley.nz 了解更多的信息。
Garrett Grolemund 是一名统计学家、教师以及 RStudio 的硕士生导师。他还是 Hands-On Programming with R 的作者。Garrett 的很多授课视频可以在 oreilly.com/safari 上找到。
目录 · · · · · ·
Preface
Part I. Explore
1. Data Visualization with ggplot2
Introduction
First Steps
Aesthetic Mappings
· · · · · · (更多)
Part I. Explore
1. Data Visualization with ggplot2
Introduction
First Steps
Aesthetic Mappings
· · · · · · (更多)
Preface
Part I. Explore
1. Data Visualization with ggplot2
Introduction
First Steps
Aesthetic Mappings
Common Problems
Facets
Geometric Objects
Statistical Transformations
Position Adjustments
Coordinate Systems
The Layered Grammar of Graphics
2. Workflow: Basics
Coding Basics
What's in a Name?
Calling Functions
3. Data Transformation with dplyr
Introduction
Filter Rows with filter()
Arrange Rows with arrange()
Select Columns with select()
Add New Variables with mutate()
Grouped Summaries with summarize()
Grouped Mutates (and Filters)
4. W0rkfl0w: Scripts
Running Code
RStudio Diagnostics
5. Exploratory Data Analysis
Introduction
Questions
Variation
Missing Values
Covariation
Patterns and Models
ggplot2 Calls
Learning More
6. Workflow: Projects
What Is Real?
Where Does Your Analysis Live?
Paths and Directories
RStudio Projects
Summary
Part II. Wrangle
7. Tibbles with tibble
Introduction
Creating Tibbles
Tibbles Versus data.frame
Interacting with Older Code
8. Data Import with readr
Introduction
Getting Started
Parsing a Vector
Parsing a File
Writing to a File
Other Types of Data
9. Tidy Data with tidyr
Introduction
Tidy Data
Spreading and Gathering
Separating and Pull
Missing Values
Case Study
Nontidy Data
10. Relational Data with dplyr
Introduction
nycflightsl3
Keys
Mutating loins
Filtering loins
loin Problems
Set Operations
11. Strings with stringr
Introduction
String Basics
Matching Patterns with Regular Expressions
Tools
Other Types of Pattern
Other Uses of Regular Expressions
stringi
12. Factors with forcats
Introduction
Creating Factors
General Social Survey
Modifying Factor Order
Modifying Factor Levels
13. Dates and Times with lubridate
Introduction
Creating Date/Times
Date-Time Components
Time Spans
Time Zones
Part III. Program
14. Pipeswith magrittr
Introduction
Piping Alternatives
When Not to Use the Pipe
Other Tools from magrittr
15. Functions
Introduction
When Should You Write a Function?
Functions Are for Humans and Computers
Conditional Execution
Function Arguments
Return Values
Environment
16. Vectors
Introduction
Vector Basics
Important Types of Atomic Vector
Using Atomic Vectors
Recursive Vectors (Lists)
Attributes
Augmented Vectors
17. Iteration with purrr
Introduction
For Loops
For Loop Variations
For Loops Versus Functionals
The Map Functions
Dealing with Failure
Mapping over Multiple Arguments
Walk
Other Patterns of For Loops
Part IV. Model
18. Model Basics with modelr
Introduction
A Simple Model
Visualizing Models
Formulas and Model Families
Missing Values
Other Model Families
19. Model Building
Introduction
Why Are Low-Quality Diamonds More Expensive?
What Affects the Number of Daily Flights?
Learning More About Models
20. Many Models with purrr and broom
Introduction
gapminder
List-Columns
Creating List-Columns
Simplifying List-Columns
Making Tidy Data with broom
Part V. Communicate
21. R Markdown
Introduction
R Markdown Basics
Text Formatting with Markdown
Code Chunks
Troubleshooting
YAML Header
Learning More
22. Graphics for Communication with ggplot2
Introduction
Label
Annotations
Scales
Zooming
Themes
Saving Your Plots
Learning More
23. R Markdown Formats
Introduction
Output Options
Documents
Notebooks
Presentations
Dashboards
Interactivity
Websites
Other Formats
Learning More
24. R Markdown Workflow
Index
· · · · · · (收起)
Part I. Explore
1. Data Visualization with ggplot2
Introduction
First Steps
Aesthetic Mappings
Common Problems
Facets
Geometric Objects
Statistical Transformations
Position Adjustments
Coordinate Systems
The Layered Grammar of Graphics
2. Workflow: Basics
Coding Basics
What's in a Name?
Calling Functions
3. Data Transformation with dplyr
Introduction
Filter Rows with filter()
Arrange Rows with arrange()
Select Columns with select()
Add New Variables with mutate()
Grouped Summaries with summarize()
Grouped Mutates (and Filters)
4. W0rkfl0w: Scripts
Running Code
RStudio Diagnostics
5. Exploratory Data Analysis
Introduction
Questions
Variation
Missing Values
Covariation
Patterns and Models
ggplot2 Calls
Learning More
6. Workflow: Projects
What Is Real?
Where Does Your Analysis Live?
Paths and Directories
RStudio Projects
Summary
Part II. Wrangle
7. Tibbles with tibble
Introduction
Creating Tibbles
Tibbles Versus data.frame
Interacting with Older Code
8. Data Import with readr
Introduction
Getting Started
Parsing a Vector
Parsing a File
Writing to a File
Other Types of Data
9. Tidy Data with tidyr
Introduction
Tidy Data
Spreading and Gathering
Separating and Pull
Missing Values
Case Study
Nontidy Data
10. Relational Data with dplyr
Introduction
nycflightsl3
Keys
Mutating loins
Filtering loins
loin Problems
Set Operations
11. Strings with stringr
Introduction
String Basics
Matching Patterns with Regular Expressions
Tools
Other Types of Pattern
Other Uses of Regular Expressions
stringi
12. Factors with forcats
Introduction
Creating Factors
General Social Survey
Modifying Factor Order
Modifying Factor Levels
13. Dates and Times with lubridate
Introduction
Creating Date/Times
Date-Time Components
Time Spans
Time Zones
Part III. Program
14. Pipeswith magrittr
Introduction
Piping Alternatives
When Not to Use the Pipe
Other Tools from magrittr
15. Functions
Introduction
When Should You Write a Function?
Functions Are for Humans and Computers
Conditional Execution
Function Arguments
Return Values
Environment
16. Vectors
Introduction
Vector Basics
Important Types of Atomic Vector
Using Atomic Vectors
Recursive Vectors (Lists)
Attributes
Augmented Vectors
17. Iteration with purrr
Introduction
For Loops
For Loop Variations
For Loops Versus Functionals
The Map Functions
Dealing with Failure
Mapping over Multiple Arguments
Walk
Other Patterns of For Loops
Part IV. Model
18. Model Basics with modelr
Introduction
A Simple Model
Visualizing Models
Formulas and Model Families
Missing Values
Other Model Families
19. Model Building
Introduction
Why Are Low-Quality Diamonds More Expensive?
What Affects the Number of Daily Flights?
Learning More About Models
20. Many Models with purrr and broom
Introduction
gapminder
List-Columns
Creating List-Columns
Simplifying List-Columns
Making Tidy Data with broom
Part V. Communicate
21. R Markdown
Introduction
R Markdown Basics
Text Formatting with Markdown
Code Chunks
Troubleshooting
YAML Header
Learning More
22. Graphics for Communication with ggplot2
Introduction
Label
Annotations
Scales
Zooming
Themes
Saving Your Plots
Learning More
23. R Markdown Formats
Introduction
Output Options
Documents
Notebooks
Presentations
Dashboards
Interactivity
Websites
Other Formats
Learning More
24. R Markdown Workflow
Index
· · · · · · (收起)
数据科学(影印版)的书评 · · · · · · ( 全部 6 条 )
Tidyverse: R 的现代范式
这本书的定位是 data science 入门书,特点是使用了 tidyverse 的一套哲学。整体思路可借用书中的一张图来说明: 首先明确几点原则: 1. 工具不是重点,创造价值才是目的。具体到数据科学,表现形式往往是提供解决方案或者做出某种决策。至于使用什么语言,采用什么工具,不本...
(展开)
不如叫Tidyverse for Data Science
有人说这本书就是Hadley安利他的Tidyverse各种包,书名不如就叫《Tidyverse for Data Science》,没办法,谁让Tidyverse就是优雅好用呢。举个例子,我看完以后就记得一堆动词性函数(Verbs),比如用filter()筛选符合条件的行(observations),用select()筛选需要的列(variab...
(展开)
第二章:用图形进行探索性分析
这篇书评可能有关键情节透露
library(tidyverse) 一、大引擎汽车比小引擎汽车更耗油吗? 先看一看displ与hwy之间的散点关系: ggplot(data=mpg) + geom_point(mapping=aes(x=displ,y=hwy)) 那么,class会不会影响散点的分布呢? 尝试映射为颜色: ggplot(data=mpg)+geom_point(mapping=aes(x=displ,y=hwy,c... (展开)> 更多书评 6篇
论坛 · · · · · ·
在这本书的论坛里发言这本书的其他版本 · · · · · · ( 全部6 )
-
O'Reilly Media (2016)9.4分 426人读过
-
O'Reilly Media (2023)暂无评分 11人读过
-
人民邮电出版社 (2018)9.4分 177人读过
-
オライリージャパン (2017)暂无评分
以下书单推荐 · · · · · · ( 全部 )
谁读这本书? · · · · · ·
二手市场
· · · · · ·
- 在豆瓣转让 有15人想读,手里有一本闲着?
订阅关于数据科学(影印版)的评论:
feed: rss 2.0
0 有用 达瓦里西_卡方 2022-03-11 15:07:41
太棒了,看完此书,对于R的操作有了质的飞跃。大神不愧是大神。
0 有用 ! 2022-12-11 15:14:26 天津
补。最好的R语言入门书。刷过两遍Online Book,实体书是一个师妹抽奖抽到送我的。读研时花了好多时间学这个,可惜最后也没用上。上一次打开R,还是去年给年度读书报告作统计图呢。哎。
0 有用 ! 2022-12-11 15:14:26 天津
补。最好的R语言入门书。刷过两遍Online Book,实体书是一个师妹抽奖抽到送我的。读研时花了好多时间学这个,可惜最后也没用上。上一次打开R,还是去年给年度读书报告作统计图呢。哎。
0 有用 达瓦里西_卡方 2022-03-11 15:07:41
太棒了,看完此书,对于R的操作有了质的飞跃。大神不愧是大神。