R软件：SAS与SPSS用户的过渡指南

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本文将深入探讨R语言在统计分析领域中的重要角色，特别关注它与业界常用的统计软件SAS和SPSS之间的关系。R, 作为一种开源的编程语言和环境，近年来在数据科学领域日益受到青睐，其强大的统计分析功能、图形绘制能力以及丰富的扩展库使其成为许多数据分析师的首选工具。首先，R与SAS和SPSS的主要区别在于其开放源代码的特性。SAS是商业软件，以其用户友好界面和高度集成的功能著称，而SPSS也以直观操作和广泛的统计分析功能闻名。相比之下，R提供了更多的灵活性和自定义性，用户可以通过编写R代码实现高度个性化的数据分析，这对于需要深入挖掘数据的高级用户尤其有价值。文章中提到的一些参考书籍，如"Software for Data Analysis: Programming with R" by John Chambers，强调了R在数据处理和分析中的编程基础。"Introductory Statistics with R, 2nd ed." by Peter Dalgaard则适合初学者，通过实例教学，帮助读者快速上手R进行统计学习。而像"Lemmon/Schafer: Developing Statistical Software in Fortran95"这样的著作，可能涵盖了如何在R之外构建统计应用软件。对于那些已经熟悉SAS或SPSS的用户，"R for SAS and SPSS Users" by Muenchen是一本实用指南，它着重于如何利用R来替代或者补充SAS和SPSS的功能，特别是在处理大量数据和复杂模型方面。此外，"Mixed-Effects Models in S and S-PLUS" by Pinheiro and Bates展示了R在混合效应模型等高级统计技术上的优势。文中还提到了一些特定领域的应用，如"Numerical Bayesian Methods Applied to Signal Processing" by Ó Ruanaidh和Fitzgerald，这表明R在处理贝叶斯方法和信号处理等领域具有广泛的应用潜力。同时，"VARIOWIN: Software for Spatial Data Analysis in 2D" by Pannatier展示了R在空间数据分析中的实用性。虽然R最初可能对SAS和SPSS用户来说是一个学习曲线，但其强大的统计分析能力、社区支持和不断增长的生态系统使得越来越多的数据分析专家选择R作为他们的主要工具。因此，理解并掌握R与SAS和SPSS的关系，以及如何在实际工作中有效利用R，对于提升数据分析技能至关重要。

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22.27 An Example Specifying All Defaults . . . . . . . . . . . . . . . . . . 385

22.28 Summary of Graphic Elements and Parameters . . . . . . . . . 386

23 Statistics .............................................. 403

23.1 Scientific Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403

23.2 Descriptive Statistics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404

23.3 Cross-Tabulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408

23.4 Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413

23.5 Linear Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417

23.5.1 Plotting Diagnostics . . . . . . . . . . . . . . . . . . . . . . . . 420

23.5.2 Comparing Models . . . . . . . . . . . . . . . . . . . . . . . . . 421

23.5.3 Making Predictions with New Data . . . . . . . . . . . . 422

23.6 t-Test – Independent Groups . . . . . . . . . . . . . . . . . . . . . . . . 422

23.7 Equality of Variance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424

23.8 t-Test – Paired or Repeated Measures . . . . . . . . . . . . . . . . . 424

23.9 Wilcoxon Mann–Whitney Rank Sum Test – Independent

Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425

23.10 Wilcoxon Signed-Rank Test – Paired Groups . . . . . . . . . . . 426

23.11 Analysis of Variance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427

23.12 Sums of Squares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431

23.13 Kruskal–Wallis Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432

24 Conclusion ............................................. 441

Appendix A A Glossary of R Jargon . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443

Appendix B A Comparis on of SAS and SPSS Products with R Packages

and Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449

Appendix C Automating Your Settings . . . . . . . . . . . . . . . . . . . . . . . . . . 453

Appendix D A comparison of the major attributes of

SAS and SPSS to R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457

Bibliography ............................................... 459

Index ..................................................... 463

Contents xvii

Chapter 1

Introduction

The availability of R [5] has dramatically ch anged the landscape of research

software. It provides a powerful common language for data analysis and

graphics that is freely available to all. SPSS users can now call R functions

within their programs, dramatically expanding the capability of SPSS.

For each aspect of R w e discuss, we will compare and contrast it with SAS

and SPSS. Many of the topics end with example programs that do almost

identical things in all three. The R programs often display more variations on

each theme than do the SAS or SPSS examples, making the R programs

longer.

SAS and SPSS are so similar to each other that moving from one to the other

is straightforward. R, however, is very different, making the transition confus-

ing at first. I hope to ease that confusion by focusing on the similarities and

differences in this book. When we examine a particular analysis, say comparing

two groups with a t-test, someone who knows SAS or SPSS will have very little

trouble figuring out what R is doing. However, the basics of the R language are

very different, so that is where we will spend most of our time.

I introduce topics in a carefully chosen order so it is best to read from

beginning to end the first time through, even if you think you do not need to

know a particular topic. Later you can skip directly to the section you need.

I include a fair amount of redundancy on ke y topics to help teach those topics

and to make it easier to read just one section as a future reference. The glossary

in Appendix A defines R concepts in terms that SAS or SPSS users will understand

and provides parallel definitions using R terminology.

1.1 Why Learn R?

If you already know SAS or SPSS, why should you bother to learn R? Both SAS

and SPSS are excellent statistics packages. I use them both almost daily. If they

meet your needs, and you do not mind paying for them, there is little point in

learning another package. However, R offers a lot:

R.A. Muenchen, R for SAS and SPSS Users, DOI: 10.1007/978-0-387-09418-2_1,

Ó Springer ScienceþBusiness Media, LLC 2009



R offers more analytical methods. There are now well over 1000 add-on

packages available for R, and R can download and install them directly from

the Internet. It takes most statistics packages at least 5 years to add a major

new analytic method. Statisticians who develop new methods often work in

R, so R users often get to use new methods immediately.



You can use R while knowing very little about it. You can do all your data

management with any software you prefer, and learn just enough R to import

a file and run the procedure you need. If you are an SPSS user, you can run

R programs from within SPSS programs, allowing you to do much of your

work in a familiar environment while avoiding the cost of the various add-on

modules for SPSS.



R is far more flexible in the type of data it can analyze. While SAS and SPSS

require you to store your data in rectangular datasets, R offers a rich variety

of data structures that are much more flexible. You can perform analyses

that include variables from different data structures easil y without having to

merge them.



R’s language is more powerful than SAS or SPSS. R developers write most of

their analytic methods using the R language; SAS and SPSS developers do

not use their own languages to develop their procedures.



R’s procedures, which it calls functions, are open for you to see and modify.



Functions that you write in R are automatically on an equal footing with

those that come with the software. The ability to write your own completely

integrated procedures in SAS or SPSS requires using a different language

such as C or Python, and in the case of SAS, a developer’s kit.



R’s graphics are extremely flexible and are of publication qua lity. They

are flexible enough to overlay data from different datasets, even at different

levels of aggregation.



R runs on almost any computer, including Windows, Macintosh, Linux, and

UNIX.



R has full matrix capabilities that are quite similar to MATLAB, and it

even offers a MATLAB emulation package [6]. For a comparison of R

and MATLAB, see http://wiki.r-project.org/rwiki/doku.php? id=getting-

started:translation s:octave2r.



R is free.

1.2 Is R Accurate?

When peop le first learn of R, one of their first questions is ‘‘Can a package

written by volunteers be as accurate as one written by a large corporation?’’

People envision a lone programmer competing against a large corporate team.

Having worked closely with several software companies over the ye ars, I can

assure you that this is not the case. A particular procedure is usually written by

one programmer even at SAS Institute and SPSS, Inc. The testing process is

2 1 Introduction

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