R语言实战：数据分析与图形绘制(第2版)

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"《数据分析与图形化实战：R语言版》（2015年曼宁出版社第二版）是一本面向商业开发者介绍R语言的实用指南。该书由经验丰富的研究员兼教师Rob Kabacoff博士编写，他以Quick-R网站的维护者而知名。本书注重实践，旨在让读者掌握统计学基础知识，以及如何处理复杂、不完整的数据。通过阅读，你将学会如何使用R进行数据管理、分析和可视化，以及调试程序和创建R包等高级技巧。第二版新增了时间序列分析、聚类分析和分类方法（如决策树、随机森林和支持向量机）等内容，扩展了对预测、数据挖掘和动态报告撰写的教学。书中详细讲解了完整的R语言教程，包括基本和中级统计方法，以及超过160种图形的制作。此外，作者还介绍了如何利用ggplot2和lattice等库进行高级图形设计，以及如何提升编程技能和创建自己的R包。对于初学者而言，这本书提供了全面且易于理解的入门路径，而对于已经在R领域有一定基础的专业人士，它则是一个深入学习和解决问题的强大工具。读者可以快速提升数据分析能力，通过实例驱动的学习方式，解决未知问题，从而在商业应用中发挥R语言的巨大潜力。购买纸质书将附赠PDF、Kindle和ePub格式的电子书，让学习更加便捷。"

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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