R语言统计分析软件设计指南PDF下载

需积分: 10 106 浏览量更新于2024-07-26 收藏 4.51MB PDF 举报

"R统计分析PDF文档下载" 这篇文档似乎是一部关于统计软件设计的书籍，由Norman Matloff撰写，书名是《The Art of Statistical Software Design》。书中涵盖了R语言在统计分析中的应用和编程艺术。Norman Matloff是一位在统计学和计算机科学领域有深厚造诣的专家，他的著作可能深入浅出地介绍了如何使用R进行有效的统计分析。在R统计分析中，学习者可以期待了解到以下关键知识点： 1. **R语言基础**：R是一种专为统计计算和图形制作设计的开源编程语言。它提供了丰富的统计和图形包，使得数据分析工作变得更为便捷。学习者将掌握R的基本语法、数据类型、数据操作以及控制流程等基础知识。 2. **统计概念**：书中可能会深入讲解统计学的基本原理，包括概率论、假设检验、回归分析、方差分析、聚类分析、时间序列分析等。这些概念是进行统计分析的基础。 3. **R包的使用**：R有许多用于特定统计任务的包，如ggplot2用于高级数据可视化，dplyr用于数据清洗和操作，tidyr用于数据整理，lme4和glmnet用于混合效应模型和正则化方法等。学习者将学会如何选择和使用这些包。 4. **编程实践**：书中的“编程艺术”部分可能涵盖如何编写高效、可读的R代码，以及如何组织和管理项目。这包括函数定义、错误处理、调试技巧和版本控制等。 5. **软件设计**：在统计软件设计方面，作者可能会讨论如何构建可复用的代码库，模块化编程，以及如何利用R的面向对象特性来设计复杂的数据分析流程。 6. **数据分析流程**：从数据预处理（数据清洗、缺失值处理、异常值检测）到模型选择与评估，再到结果解释，学习者将学习到一个完整的数据分析项目的实施步骤。 7. **技术审阅者 Hadley Wickham**：提到的技术审阅者Hadley Wickham是R社区的重要人物，他开发了许多R的流行包，如前面提到的ggplot2和dplyr。他的参与意味着书中的内容可能与现代R生态系统紧密相关。 8. **出版信息**：这本书由No Starch Press出版，该出版社专注于计算机科学和技术领域的图书，意味着这本书的内容质量应该是专业且权威的。《The Art of Statistical Software Design》提供了一个全面的学习平台，帮助读者提升R语言在统计分析中的应用能力，同时也强调了编程实践和软件设计的重要性。通过阅读这本书，无论是初学者还是经验丰富的统计学家，都能从中受益并提升自己的数据分析技能。

13.5 Ensuring Consistency in Debugging Simulation Code...........................302

13.6 Syntax and Runtime Errors

...................................................303

13.7 Running GDB on R Itself

.....................................................303

PERFORMANCE ENHANCEMENT: SPEED AND MEMORY 305

14.1 Writing Fast R Code ........................................................ 306

14.2 The Dreaded for Loop

.......................................................306

14.2.1 Vectorization for Speedup

.......................................306

14.2.2 Extended Example: Achieving Better Speed in a Monte Carlo

Simulation

.....................................................308

14.2.3 Extended Example: Generating a Powers Matrix

...................312

14.3 Functional Programming and Memory Issues

..................................314

14.3.1 Vector Assignment Issues

........................................314

14.3.2 Copy-on-Change Issues

..........................................314

14.3.3 Extended Example: Avoiding Memory Copy

......................315

14.4 Using Rprof() to Find Slow Spots in Your Code

................................316

14.4.1 Monitoring with Rprof()

..........................................316

14.4.2 How Rprof() Works

.............................................318

14.5 Byte Code Compilation

.....................................................320

14.6 Oh No, the Data Doesn’t Fit into Memory!

....................................320

14.6.1 Chunking

......................................................320

14.6.2 Using R Packages for Memory Management

.......................321

INTERFACING R TO OTHER LANGUAGES 323

15.1 Writing C/C++ Functions to Be Called from R ................................. 323

15.1.1 Some R-to-C/C++ Preliminaries

..................................324

15.1.2 Example: Extracting Subdiagonals from a Square Matrix

...........324

15.1.3 Compiling and Running Code

....................................325

15.1.4 Debugging R/C Code

...........................................326

15.1.5 Extended Example: Prediction of Discrete-Valued Time Series

.......327

15.2 Using R from Python

........................................................330

15.2.1 Installing RPy

...................................................330

15.2.2 RPy Syntax

.....................................................330

PARALLEL R 333

16.1 The Mutual Outlinks Problem.................................................333

16.2 Introducing the snow Package

...............................................334

16.2.1 Running snow Code

.............................................335

16.2.2 Analyzing the snow Code

.......................................336

16.2.3 How Much Speedup Can Be Attained?

...........................337

16.2.4 Extended Example: K-Means Clustering

...........................338

xiv

Contents in Detail

BARUG has also beneﬁted from the ﬁnancial support of Revolution Analytics

and countless hours, energy, and ideas from David Smith and Joe Rickert of

that ﬁrm.

Jay Emerson and Mike Kane, authors of the award-winning

bigmemory

package in CRAN, read through an early draft of Chapter 16 on parallel R

programming and made valuable comments.

John Chambers (founder of S, the “ancestor” of R) and Martin Morgan

provided advice concerning R internals, which was very helpful to me for the

discussion of R’s performance issues in Chapter 14.

Section 7.8.4 covers a controversial topic in programming communities—

the use of global variables. In order to be able to get a wide range of perspec-

tives, I bounced my ideas off several people, notably R core group member

Thomas Lumley and my UC Davis computer science colleague, Sean Davis.

Needless to say, there is no implication that they endorse my views in that

section of the book, but their comments were quite helpful.

Early in the project, I made a very rough (and very partial) draft of the

book available for public comment and received helpful feedback from

Ramon Diaz-Uriarte, Barbara F. La Scala, Jason Liao, and my old friend

Mike Hannon. My daughter Laura, an engineering student, read parts of

the early chapters and made some good suggestions that improved the book.

My own CRAN projects and other R-related research (parts of which

serve as examples in the book) have beneﬁted from the advice, feedback,

and/or encouragement of many people, especially Mark Bravington,

Stephen Eglen, Dirk Eddelbuett, Jay Emerson, Mike Kane, Gary King,

Duncan Murdoch, and Joe Rickert.

R core group member Duncan Temple Lang is at my institution, the

University of California, Davis. Though we are in different departments and

thus haven’t interacted much, this book owes something to his presence on

campus. He has helped to create a very R-aware culture at UCD, which has

made it easy for me to justify to my department the large amount of time

I’ve spent writing this book.

This is my second project with No Starch Press. As soon as I decided

to write this book, I naturally turned to No Starch Press because I like the

informal style, high usability, and affordability of their products. Thanks go

to Bill Pollock for approving the project, to editorial staff Keith Fancher and

Alison Law, and to the freelance copyeditor Marilyn Smith.

Last but deﬁnitely not least, I thank two beautiful, brilliant, and funny

women—my wife Gamis and the aforementioned Laura, both of whom

cheerfully accepted my statement “I’m working on the R book,” whenever

they asked why I was so buried in work.

xviii Acknowledgments

剩余403页未读，继续阅读

testyuesisoft

粉丝: 0
资源: 1

R语言统计分析软件设计指南PDF下载

王斌会的《多元统计分析及R语言建模》一书数据

R语言实战中文完整版PDF+对应代码_R语言实战_

R语言与统计分析_Rlanguage_statistics_

多元统计分析及R语言建模考试试卷.pdf

多元统计分析案例分析.pdf

Matlab与统计分析.pdf

R语言教程 pdf文档

实验三 空间统计分析.pdf

R语言笔记--常用函数、统计分析、数据类型、数据操作、帮助、安装程序包、R绘图.pdf

质量目标达成统计分析表.pdf

最新资源

实验三空间统计分析.pdf