R语言实战：数据挖掘算法与案例分析

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"《R与数据挖掘：实例与案例研究》是一本由Yanchang Zhao撰写的关于R语言在数据挖掘领域的实用指南。该书以实际项目为载体，深入介绍了R语言中的各种数据挖掘算法，并提供了丰富的代码示例。作者强调，书中提供的案例研究并未包含在这篇在线版本中，它们专属于即将出版的书籍版本。读者可以在http://www.RDataMining.com获取最新在线版本，同时还能找到R数据挖掘参考卡和其他相关资源。作者鼓励读者访问网站，获取R代码、数据集以及针对文档或书籍版本的常见问题解答（FAQ）。书中已经涵盖了主题建模和流图分析，以及空间数据分析等内容。作者欢迎读者提出对未涵盖的话题建议，或者在遇到问题时，在RDataMining讨论组<http://group.rdatamining.com>或通过电子邮件联系作者进行交流。此外，作者还在Twitter上建立了连接，以便于实时分享与本书相关的信息和讨论。该书不仅是一份技术文档，也是一份实践者的工具，适合那些希望深入了解如何使用R进行数据挖掘的读者，无论是初学者还是经验丰富的专业人士，都能从中获益匪浅。" 这本书的每个章节都详尽地介绍了R语言的数据挖掘功能，包括但不限于聚类、分类、关联规则学习、回归分析等基础方法，以及更高级的主题模型和时空数据分析。通过实例演示，读者能够掌握如何运用R进行数据清洗、预处理、模型构建和结果解释。同时，书中提供的资源和社区支持确保了学习者能够在实践中遇到困难时得到及时的帮助和指导。《R与数据挖掘：实例与案例研究》是一本不可多得的R语言学习和实践资料。

6 CHAPTER 2. DATA IMPORT AND EXPORT

2.3 Import Data from SAS

Package foreign [R-core, 2012] provides function read.ssd() for importing SAS datasets (.sas7bdat

ﬁles) into R. However, the following points are essential to make importing successful.



SAS must be available on your computer, and read.ssd() will call SAS to read SAS datasets

and import them into R.



The ﬁle name of a SAS dataset has to be no longer than eight characters. Otherwise, the

importing would fail. There is no such a limit when importing from a .CSV ﬁle.



During importing, variable names longer than eight characters are truncated to eight char-

acters, which often makes it diﬃcult to know the meanings of variables. One way to get

around this issue is to import variable names separately from a .CSV ﬁle, which keeps full

names of variables.

An empty .CSV ﬁle with variable names can be generated with the following method.

1. Create an empty SAS table dumVariables from dumData as follows.

data work.dumVariables;

set work.dumData(obs=0);

run;

2. Export table dumVariables as a .CSV ﬁle.

The example below demonstrates importing data from a SAS dataset. Assume that there is a

SAS data ﬁle dumData.sas7bdat and a .CSV ﬁle dumVariables.csv in folder “Current working

directory/data”.

> library(foreign) # for importing SAS data

> # the path of SAS on your computer

> sashome <- "C:/Program Files/SAS/SASFoundation/9.2"

> filepath <- "./data"

> # filename should be no more than 8 characters, without extension

> fileName <- "dumData"

> # read data from a SAS dataset

> a <- read.ssd(file.path(filepath), fileName, sascmd=file.path(sashome, "sas.exe"))

> print(a)

VARIABLE VARIABL2 VARIABL3

1 1 0.1 R

2 2 0.2 and

3 3 0.3 Data Mining

4 4 0.4 Examples

5 5 0.5 Case Studies

Note that the variable names above are truncated. The full names can be imported from a

.CSV ﬁle with the following code.

> # read variable names from a .CSV file

> variableFileName <- "dumVariables.csv"

> myNames <- read.csv(paste(filepath, variableFileName, sep="/"))

> names(a) <- names(myNames)

> print(a)

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R语言实战：数据挖掘算法与案例分析

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