R语言实战：自动化数据采集与文本挖掘指南

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《R语言网络爬虫与文本挖掘实践指南》是一本由Simon Munzert、Christian Rubba、Peter Meißner和Dominic Nyhuis四位专家合作编写的实用教程。该书专注于介绍如何使用R语言进行自动化数据采集，尤其适用于那些希望在Web scraping（网络抓取）和文本挖掘领域深入学习的专业人士或研究人员。作者们分别来自德国的康斯坦茨大学、苏黎世大学和曼海姆大学，他们在政治学和公共行政管理领域拥有深厚背景。本书的核心内容围绕以下几个关键知识点展开： 1. **R语言基础**：首先，读者将了解到R语言的基本语法和环境设置，包括数据结构（如向量、列表、数据框等）、函数使用以及R编程的基本逻辑，这对于后续的爬虫操作至关重要。 2. **网络爬虫技术**：书中详细讲解了如何利用R中的各种包，如`rvest`、`xml2`和`httr`等，设计和实现高效、稳定的网络爬虫程序，包括如何解析HTML、处理cookies、应对反爬虫机制等技巧。 3. **网页结构分析**：针对不同类型的网站，作者会教导读者如何理解和分析网页结构，识别需要抓取的数据源，这涉及XPath和CSS选择器的运用。 4. **文本挖掘**：除了数据抓取，书中还涵盖了文本数据预处理（如清洗、分词、去除停用词等）、特征提取（如TF-IDF、词频统计等）以及基本的文本分析方法，如情感分析和主题建模。 5. **案例实战**：通过实际案例，作者展示了如何将理论知识应用到实践中，帮助读者掌握从零开始创建爬虫项目，直至整理和分析抓取到的数据。 6. **最佳实践与注意事项**：书中还会分享关于数据安全、隐私保护、知识产权合规性等方面的实用建议，确保读者在进行数据采集时遵循道德规范和法律法规。 7. **版本更新与支持**：作为2015年首次出版的作品，作者们确保提供的内容既包含当时的技术趋势，又考虑到随着R语言的不断更新，书中提供了一些维护和升级技巧。《R语言网络爬虫与文本挖掘实践指南》是一本兼具理论深度和实践经验的教程，对于任何希望提升R语言数据采集能力的学习者来说，都是不可或缺的参考资料。无论是科研工作者还是数据分析师，都可以从中获益良多。

Preface

The rapid growth of the World Wide Web over the past two decades tremendously changed

the way we share, collect, and publish data. Firms, public institutions, and private users

provide every imaginable type of information and new channels of communication generate

vast amounts of data on human behavior. What was once a fundamental problem for the

social sciences—the scarcity and inaccessibility of observations—is quickly turning into

an abundance of data. This turn of events does not come without problems. For example,

traditional techniques for collecting and analyzing data may no longer sufce to overcome

the tangled masses of data. One consequence of the need to make sense of such data has

been the inception of “data scientists,” who sift through data and are greatly sought after by

researchers and businesses alike.

Along with the triumphant entry of the World Wide Web, we have witnessed a second

trend, the increasing popularity and power of open-source software like

R.Forquantitative

social scientists,

R is among the most important statistical software. It is growing rapidly

due to an active community that constantly publishes new packages. Yet,

R is more than a

free statistics suite. It also incorporates interfaces to many other programming languages and

software solutions, thus greatly simplifying work with data from various sources.

On a personal note, we can say the following about our work with social scienticdata:

our nancial resources are sparse;

we have little time or desire to collect data by hand;

we are interested in working with up-to-date, high quality, and data-rich sources; and

we want to document our research from the beginning (data collection) to the end

(publication), so that it can be reproduced.

In the past, we frequently found ourselves being inconvenienced by the need to manually

assemble data from various sources, thereby hoping that the inevitable coding and copy-and-

paste errors are unsystematic. Eventually we grew weary of collecting research data in a

non-reproducible manner that is prone to errors, cumbersome, and subject to heightened risks

of death by boredom. Consequently, we have increasingly incorporated the data collection and

publication processes into our familiar software environment that already helps with statistical

analyses—

R.Theprogramoffersagreatinfrastructuretoexpandthedailyworkow to steps

before and after the actual data analysis.

xvi PREFACE

Although R is not about to collect survey data on its own or conduct experiments any

time soon, we do consider the techniques presented in this book as more than the “the poor

man’s substitute” for costly surveys, experiments, and student-assistant coders. We believe

that they are a powerful supplement to the portfolio of modern data analysts. We value the

collection of data from online resources not only as a more cost-sensitive solution compared

to traditional data acquisition methods, but increasingly think of it as the exclusive approach

to assemble datasets from new and developing sources. Moreover, we cherish program-based

solutions because they guarantee reliability, reproducibility, time-efciency, and assembly of

higher quality datasets. Beyond productivity, you might nd that you enjoy writing code and

drafting algorithmic solutions to otherwise tedious manual labor. In short, we are convinced

that if you are willing to make the investment and adopt the techniques proposed in this book,

you will benetfromalastingimprovementintheeaseandqualitywithwhichyouconduct

your data analyses.

If you have identied online data as an appropriate resource for your project, is web

scraping or statistical text processing and therefore an automated or semi-automated data

collection procedure really necessary? While we cannot hope to offer any denitive guidelines,

here are some useful criteria. If you nd yourself answering several of these afrmatively, an

automated approach might be the right choice:

Do you plan to repeat the task from time to time, for example, in order to update your

database?

Do you want others to be able to replicate your data collection process?

Do you deal with online s ources of data frequently?

Is the task non-trivial in terms of scope and complexity?

If the task can also be accomplished manually—do you lack the resources to let others

do the work?

Are you willing to automate processes by means of programming?

Ideally, the techniques presented in this book enable you to create powerful collections of

existing, but unstructured or unsorted data no one has analyzed before at very reasonable cost.

In many cases, you will not get far without rethinking, rening, and combining the proposed

techniques due to your subjects’ specics. In any case, we hope you nd the topics of this

book inspiring and perhaps even eye opening: The streets of the Web are paved with data that

cannot wait to be collected.

What you won’t learn from reading this book

When you browse the table of contents, you get a rst impression of what you can expect to

learn from reading this book. As it is hard to identify parts that you might have hoped for but

that are in fact not covered in this book, we will name some aspects that you will not nd in

this volume.

What you will not get in this book is an introduction to the

R environment. There are

plenty of excellent introductions—both printed and online—and this book won’t be just

another addition to the pile. In case you have not previously worked with

R,thereisnoreason

PREFACE xvii

to set this book aside in disappointment. In the next section we’ll suggest some well-written

R introductions.

You sho u ld als o not ex pe c t th e denitive guide to web scraping or text mining. First, we

focus on a software environment that was not specically tailored to these purposes. There

might be applications where

R is not the ideal solution for your task and other software

solutions might be more suited. We will not bother you with alternative environments such

as PHP, Python, Ruby, or Perl. To nd out if this book is helpful for you, you should ask

yourself whether you are already using or planning to use

R for your daily work. If the answer

to both questions is no, you should probably consider your alternatives. But if you already

use

R or intend to use it, you can spare yourself the effort to learn yet another language and

stay within a familiar environment.

This book is not strictly speaking about data science either. There are excellent intro-

ductions to the topic like the recently published books by O’Neil and Schutt (2013), Torgo

(2010), Zhao (2012), and Zumel and Mount (2014). What is occasionally missing in these

introductions is how data for data science applications are actually acquired. In this sense,

our book serves as a preparatory step for data analyses but also provides guidance on how to

manage available information and keep it up to date.

Finally, what you most certainly will not get is the perfect solution to your specic

problem. It is almost inherent in the data collection process that the elds where the data are

harvested are never exactly alike, and sometimes rapidly change shape. Our goal is to enable

you to adapt the pieces of code provided in the examples and case studies to create new pieces

of code to help you succeed in collecting the data you need.

Why R?

There are many reasons why we think that R is a good solution for the problems that are

covered in this book. To us, the most important points are:

R is freely and easily accessible. You can download, install, and use it wherever and

whenever you want. There are huge benets to not being a specialist in expensive

proprietary programs, as you do not depend on the willingness of employers to pay

licensing fees.

2. For a software environment with a primarily statistical focus,

R has a large community

that continues to ourish.

R is used by various disciplines, such as social scientists,

medical scientists, psychologists, biologists, geographers, linguists, and also in busi-

ness. This range allows you to share code with many developers and protfrom

well-documented applications in diverse settings.

R is open source. This means that you can easily retrace how functions work and mod-

ify them with little effort. It also means that program modications are not controlled

by an exclusive team of programmers that takes care of the product. Even if you are

not interested in contributing to the development of

R,youwillstillreapthebenets

from having access to a wide variety of optional extensions—packages. The num-

ber of packages is continuously growing and many existing packages are frequently

updated. You can nd nice overviews of popular themes in

R usage on http://cran.r-

project.org/web/views/.

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