R编程实战第二版：数据分析与图形

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"R in Action, Second Edition" 是一本由 Robert I. Kabacoff 编写的书籍，专注于使用R语言进行数据分析和图形制作。这本书是第一版的更新版，获得了众多专家的好评，适合初学者和有经验的程序员作为学习和参考的资料。在 "R in Action" 第二版中，作者深入浅出地介绍了R语言的强大功能，包括数据处理、统计分析以及创建高质量的图形。书中的内容涵盖了从基础到高级的各种主题，旨在帮助读者快速提升使用R语言的技能。书中可能包含以下关键知识点： 1. **R语言基础**：讲解R语言的基本语法，如变量定义、数据类型、控制结构（如循环和条件语句）以及函数的使用。 2. **数据操作**：介绍如何在R中读取、写入和处理数据，包括数据框的操作、数据清洗和数据转换。 3. **统计分析**：涵盖常见的统计方法，如描述性统计、假设检验、回归分析、时间序列分析等，以及如何在R中实现这些方法。 4. **可视化**：详细讲解使用R的ggplot2包或其他绘图工具创建专业图表的过程，包括散点图、直方图、箱线图、热力图等多种图形的生成。 5. **包的使用**：介绍如何利用R的生态系统，如dplyr、tidyr、tidyverse等包进行数据操作和分析，以及如何安装和管理R的附加包。 6. **编程技巧**：讨论面向对象编程、调试和优化代码的策略，以及如何编写自定义函数来提高效率。 7. **实战应用**：通过实际案例展示R在生物统计、社会科学、商业分析等领域的应用，帮助读者将理论知识转化为解决实际问题的能力。 8. **进阶主题**：可能包括数据挖掘、机器学习、预测模型等现代数据分析技术，以及如何利用R与其他编程语言（如Python）进行集成。这本书得到了多领域专家的推荐，被誉为快速提升R技能的优秀教程。无论你是初次接触R的新手，还是寻求提升现有技能的专业人士，"R in Action, Second Edition" 都能提供宝贵的指导和深入的洞察。通过这本书的学习，读者可以在短时间内掌握R语言，并能解决各种数据相关的问题，甚至发现和解决未知的问题。

CONTENTS

18.3 Exploring missing-values patterns 418

Tabulating missing values 419

■

Exploring missing data

visually 419

■

Using correlations to explore missing

values 422

18.4 Understanding the sources and impact of missing data 424

18.5 Rational approaches for dealing with incomplete data 425

18.6 Complete-case analysis (listwise deletion) 426

18.7 Multiple imputation 428

18.8 Other approaches to missing data 432

Pairwise deletion 432

■

Simple (nonstochastic)

imputation 433

18.9 Summary 433

PART 5EXPANDING YOUR SKILLS ............................. 435

Advanced graphics with ggplot2 437

19.1 The four graphics systems in R 438

19.2 An introduction to the ggplot2 package 439

19.3 Specifying the plot type with geoms 443

19.4 Grouping 447

19.5 Faceting 450

19.6 Adding smoothed lines 453

19.7 Modifying the appearance of ggplot2 graphs 455

Axes 455

■

Legends 457

■

Scales 458

■

Themes 460

Multiple graphs per page 461

19.8 Saving graphs 462

19.9 Summary 462

Advanced programming 463

20.1 A review of the language 464

Data types 464

■

Control structures 470

■

Creating

functions 473

20.2 Working with environments 475

20.3 Object-oriented programming 477

Generic functions 477

■

Limitations of the S3 model 479

20.4 Writing efficient code 479

Licensed to Mark Watson <nordickan@gmail.com>

PREFACE

xviii

and set off to learn it. I was an experienced statistician and researcher, had 25 years

experience as an SAS and SPSS programmer, and was fluent in a half dozen program-

ming languages. How hard could it be? Famous last words.

As I tried to learn the language (as fast as possible, with an interview looming), I

found either tomes on the underlying structure of the language or dense treatises on

specific advanced statistical methods, written by and for subject-matter experts. The

online help was written in a spartan style that was more reference than tutorial. Every

time I thought I had a handle on the overall organization and capabilities of R, I

found something new that made me feel ignorant and small.

To make sense of it all, I approached R as a data scientist. I thought about what it

takes to successfully process, analyze, and understand data, including

■

Accessing the data (getting the data into the application from multiple sources)

■

Cleaning the data (coding missing data, fixing or deleting miscoded data, trans-

forming variables into more useful formats)

■

Annotating the data (in order to remember what each piece represents)

■

Summarizing the data (getting descriptive statistics to help characterize the

data)

■

Visualizing the data (because a picture really is worth a thousand words)

■

Modeling the data (uncovering relationships and testing hypotheses)

■

Preparing the results (creating publication-quality tables and graphs)

Then I tried to understand how I could use R to accomplish each of these tasks.

Because I learn best by teaching, I eventually created a website (www.statmethods.net)

to document what I had learned.

Then, about a year later, Marjan Bace, Manning’s publisher, called and asked if I

would like to write a book on R. I had already written 50 journal articles, 4 technical

manuals, numerous book chapters, and a book on research methodology, so how

hard could it be? At the risk of sounding repetitive—famous last words.

A year after the first edition came out in 2011, I started working on the second edi-

tion. The R platform is evolving, and I wanted to describe these new developments. I

also wanted to expand the coverage of predictive analytics and data mining—impor-

tant topics in the world of big data. Finally, I wanted to add chapters on advanced data

visualization, software development, and dynamic report writing.

The book you’re holding is the one that I wished I had so many years ago. I have

tried to provide you with a guide to R that will allow you to quickly access the power of

this great open source endeavor, without all the frustration and angst. I hope you

enjoy it.

P.S. I was offered the job but didn’t take it. But learning R has taken my career in

directions that I could never have anticipated. Life can be funny.

Licensed to Mark Watson <nordickan@gmail.com>

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