R语言编程入门与统计计算资源指南

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"编程与R语言：一本由Springer出版社出版的权威入门教程，展示了R语言在数据挖掘和统计分析领域的卓越性能。R语言因其强大的统计功能、灵活性以及丰富的图形处理能力而深受广大数据科学家和研究人员的喜爱。本教程集涵盖了多个方面的主题，旨在帮助读者逐步掌握这一工具。首先，作者J.Chambers的作品《软件for Data Analysis: Programming with R》介绍了如何使用R进行高效的数据分析和编程实践，为初学者提供了实用的编程技巧。这包括了数据清洗、整理、可视化和模型构建的基本步骤。 Dalgaard的《Introductory Statistics with R》则着重于介绍统计学基础知识，并演示如何通过R进行实际操作，让统计理论与实践紧密结合。对于那些希望学习统计方法并应用于实际问题的人来说，这是不可或缺的资源。 Gentle教授的几本书，如《Elementsof Computational Statistics》、《Numerical Linear Algebra for Applications in Statistics》和《Random Number Generation and Monte Carlo Methods》分别探讨了计算统计学的核心概念、数值线性代数在统计中的应用以及随机数生成和模拟技术，这些都是现代数据分析不可或缺的技术支持。 Härdle等人的《XploRe: An Interactive Statistical Computing Environment》则提供了一个交互式平台，让用户能在探索数据的同时进行统计计算，增强了用户体验。此外，还有一些专业书籍如《Mixed-Effects Models in S-PLUS》、《Numerical Bayesian Methods Applied to Signal Processing》和《Spatial Data Analysis in 2D》深入研究特定领域的统计建模和处理，展示了R语言在复杂问题解决上的广泛适应性。值得一提的是，《Modern Applied Statistics with S, 4th ed》（Venables和Ripley合著）是R语言的经典之作，它概述了R语言在现代统计分析中的广泛应用，包括数据处理、模型开发和结果解读，对R的高级用户和教育者来说是一本宝典。 'Programming with R'系列书籍覆盖了从基础到进阶的全方位内容，无论你是R语言的新手还是经验丰富的开发者，都能从中找到适合自己的学习路径，提升数据分析和编程能力。这些教材不仅提供了理论知识，更通过实例和实战演示，使学习者能够在实践中熟练掌握R语言，进而推动数据科学领域的发展。"

Chapter 1

Introduction: Principles and

Concepts

This chapter presents some of the concepts and principles that

recur throughout the book. We begin with the two guiding prin-

ciples: the mission to explore and the responsibility to be trust-

worthy (Sections 1.1 and 1.2). With these as guidelines, we then

introduce some concepts for programming with

R (Section 1.3,

page 4) and add some justiﬁcation for our emphasis on that sys-

tem (Section 1.4, page 9).

1.1 Exploration: The Mission

The ﬁrst principle I propose is that our Mission, as users and creators of

software for data analysis, is to enable the best and most thorough explo-

ration of data possible. That means that users of the software must be ale to

ask the meaningful questions about their applications, quickly and ﬂexibly.

Notice that speed here is human speed, measured in clock time. It’s

the time that the actual computations take, but usually more importantly,

it’s also the time required to formulate the question and to organize the

data in a way to answer it. This is the exploration, and software for data

analysis makes it possible. A wide range of techniques is needed to access

and transform data, to make predictions or summaries, to communicate

results to others, and to deal with ongoing processes.

Whenever we consider techniques for these and other requirements in the

chapters that follow, the ﬁrst principle we will try to apply is the Mission:

2 CHAPTER 1. INTRODUCTION: PRINCIPLES AND CONCEPTS

How can these techniques help people to carry out this speciﬁc kind of

exploration?

Ensuring that software for data analysis exists for such purposes is an

important, exciting, and challenging activity. Later chapters examine how

we can select and develop software using

R and other systems.

The importance, excitement, and challenge all come from the central

role that data and computing have come to play in modern society. Science,

business and many other areas of soc iety continually rely on understanding

data, and that understanding frequently involves large and complicated data

processes.

A few examples current as the book is written can suggest the ﬂavor:

• Many ambitious projects are underway or propose d to deploy sensor

networks, that is, coordinated networks of devices to record a variety

of meas urements in an ongoing program. The data resulting is essen-

tial to understand environmental quality, the mechanisms of weather

and climate, and the future of biodiversity in the earth’s ecosystems.

In both scale and diversity, the challenge is unprecedented, and will

require merging techniques from many disciplines.

• Astronomy and cosmology are undergoing profound changes as a result

of large-scale digital mappings enabled by both sate llite and ground

recording of huge quantities of data. The scale of data collected allows

questions to be addressed in an overall sense that before could only be

examined in a few, local regions.

• Much business activity is now carried out largely through distributed,

computerized processes that both generate large and complex streams

of data and also oﬀer through such data an unprecedented opportu-

nity to understand one’s business quantitatively. Telecommunications

in North America, for example, generates databases with conceptually

billions of records. To explore and understand such data has great

attraction for the business (and for society), but is enormously chal-

lenging.

These and many other possible examples illustrate the importance of what

John Tukey long ago characterized as “the peaceful collision of computing

and data analysis”. Progress on any of these examples will require the ability

to explore the data, ﬂexibly and in a reasonable time frame.

1.2. TRUSTWORTHY SOFTWARE: THE PRIME DIRECTIVE 3

1.2 Trustworthy Software: The Prime Directive

Exploration is our mission; we and those who use our software want to

ﬁnd new paths to understand the data and the underlying processes. The

mission is, indeed, to boldly go where no one has gone before. But, we need

boldness to be balanced by our responsibility. We have a responsibility for

the results of data analysis that provides a key compensating principle.

The complexity of the data processes and of the computations applied

to them mean that those who receive the results of modern data analysis

have limited opportunity to verify the results by direct observation. Users of

the analysis have no option but to trust the analysis, and by extension the

software that produced it. Both the data analyst and the software provider

therefore have a strong responsibility to produce a result that is trustworthy,

and, if possible, one that can be shown to be trustworthy.

This is the second principle: the computations and the software for data

analysis should be trustworthy: they should do what they claim, and be seen

to do so. Neither those who view the results of data analysis nor, in many

cases, the statisticians performing the analysis can directly validate exten-

sive computations on large and complicated data processes. Ironically, the

steadily increasing computer power applied to data analysis often distances

the results further from direct checking by the recipient. The many com-

putational steps between original data source and displayed results must all

be truthful, or the eﬀect of the analysis may be worthless, if not pernicious.

This places an obligation on all creators of software to program in such a

way that the computations can be understood and trusted. This obligation

I label the Prime Directive.

Note that the directive in no sense discourages exploratory or approx-

imate methods. As John Tukey often remarked, better an approximate

answer to the right question than an exact answer to the wrong question.

We should seek answers boldly, but always explaining the nature of the

method applied, in an open and understandable format, supported by as

much e vidence of its quality as can be produced. As we will see, a number

of more technically speciﬁc choices can help us satisfy this obligation.

Readers who have seen the Star Trek



television series

may rec ognize

the term “prime directive”. Captains Kirk, Picard, and Janeway and their

crews were bound by a directive which (slightly paraphrased) was: Do noth-

ing to interfere with the natural course of a new civilization. Do not distort

Actually, at least ﬁve series, from “The Original” in 1966 through “Enterprise”, not

counting the animated version, plus many ﬁlms. See startrek.com and the many reruns

if this is a gap in your cultural background.

4 CHAPTER 1. INTRODUCTION: PRINCIPLES AND CONCEPTS

the development. Our directive is not to distort the message of the data,

and to provide computations whose content can be trusted and understood.

The prime directive of the space explorers, notice, was not their mission

but rather an important safeguard to apply in pursuing that mission. Their

mission was to explore, to “boldly go where no one has gone before”, and

all that. That’s really our mission too: to explore how software can add

new abilities for data analysis. And our own prime directive, likewise, is an

important caution and guiding principle as we create the software to support

our mission.

Here, then, are two motivating principles: the mission, which is bold

exploration; and the prime directive, trustworthy software. We will examine

in the rest of the book how to selec t and program software for data analysis,

with these principles as guides. A few aspects of

R will prove to be es pecially

relevant; let’s examine those next.

1.3 Concepts for Programming with R

The software and the programming techniques to be discussed in later chap-

ters tend to share some concepts that make them helpful for data analysis.

Exploiting these concepts will often beneﬁt both the eﬀectiveness of pro-

gramming and the quality of the results. Each of the concepts arises nat-

urally in later chapters, but it’s worth outlining them together here for an

overall picture of our strategy in programming for data analysis.

Functional Programming

Software in R is written in a functional style that helps both to understand

the intent and to ensure that the implementation corresponds to that intent.

Computations are organized around functions, which can encapsulate spe-

ciﬁc, meaningful computational results, with implementations that can be

examined for their correctness. The style derives from a more formal theory

of functional programming that restricts the computations to obtain well-

deﬁned or eve n formally veriﬁable results. Clearly, programming in a fully

functional manner would contribute to trustworthy software. The

S lan-

guage does not enforce a strict functional programming approach, but does

carry over some of the ﬂavor, particularly when you make some eﬀort to

emphasize simple functional deﬁnitions with minimal use of non-functional

computations.

As the scope of the software expands, much of the beneﬁt from functional

style can be retained by using functional methods to deal with varied types

1.3. CONCEPTS FOR PROGRAMMING WITH R 5

of data, within the general goal deﬁned by the generic function.

Classes and Methods

The natural complement to functional style in programming is the deﬁnition

of classes of objects. Where functions should clearly encapsulate the actions

in our analysis, classes should encapsulate the nature of the objects used

and returned by calls to functions. The duality between function calls and

objects is a recurrent theme of programming with

R. In the design of new

classes, we seek to capture an underlying concept of what the objects mean.

The relevant techniques combine directly specifying the contents (the slots),

relating the new class to existing classes (the inheritance), and expressing

how objects should be created and validated (methods for initializing and

validating).

Method deﬁnitions knit together functions and classes. Well-designed

methods extend the generic deﬁnition of what a function does to provide a

speciﬁc computational method when the argument or arguments come from

speciﬁed classes, or inherit from those classes. In contrast to methods that

are s olely class-base d, as in common object-oriented programming languages

such as

C++ or Java, methods in R are part of a rich but complex network

of functional and object-based computation.

The ability to deﬁne classes and methods in fact is itself a major advan-

tage in adhering to the Prime Directive. It gives us a way to isolate and

deﬁne formally what information certain objects should contain and how

those objects should behave when functions are applied to them.

Data Frames

Trustworthy data analysis depends ﬁrst on trust in the data being analyzed.

Not so much that the data must be perfect, which is impossible in nearly

any application and in any case beyond our control, but rather that trust

in the analysis depends on trust in the relation between the data as we use

it and the data as it has entered the process and then has been recorded,

organized and transformed.

In serious modern applications, the data usually comes from a process

external to the analysis, whether generated by scientiﬁc observations, com-

mercial transactions or any of many other human activities. To access the

data for analysis by well-deﬁned and trustworthy computations, we will ben-

eﬁt from having a description, or model, for the data that corresponds to

its natural home (often in DBMS or spreadsheet software), but can also be

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