DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Preface List of Figures List of Tables Process . Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Conditional Trees Decision Trees Hierarchical Clustering Kernel Methods K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products Borgelt Data Mining Suite R Rattle Weka Closed Products C4.5 Clementine Enterprise Miner Equbits Foresight GhostMiner InductionEngine ODM Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

.

R is a statistical and data mining package consisting of a programming language and a graphics system. It is used throughout this book to illustrate how to do data mining. In the following sections of this chapter we introduce the basics of R. Many examples are provided and can be readily tried by yourself to facilitate learning. You will also find many examples on the R-help mailing list at https://stat.ethz.ch/mailman/listinfo/r-help. As an advocate of learning by example, and motivated by the programming paradigm of ``programming by example'' (Cypher, 1993), my intention is that you will be able to replicate the examples from the book, and then fine tune them to suit your own needs.

R is a language and the basic modus operandi is to write sentences expressed in that language. After a while you will want to do more than to issue single, simple, commands (sentences), but to write sentences and paragraphs and full novels in this language! R script files (often with the R file name extension) are the place to write script files. You can then re-run your scripts to transform, at will and automatically, your source data into information and knowledge.

This chapter begins with an overview of some of the key advantages (and disadvantages) of using R and continues with a guide to interacting with R. The recommended interface is through the powerful Emacs editor, augmented with the ESS package, under either GNU/Linux or MS/Windows. This is a personal preference and you may prefer some of the alternatives we discuss.

There are graphical user interface (GUI) tools available for accessing R, but they are not in general as gadget full as those provided by commercial data mining tools. This is both a disadvantage and an advantage! For R this results in a steeper learning curve, but once into R, performing operations over the same or similar datasets becomes very easy using its programming language interface.

Let's start with some of the advantages with using R:

• R is licensed under the GNU General Public License, with Copyright held by The R Foundation for Statistical Computing. Thus, it is Free Open Source Software, freely available, so that anyone can freely download and install the software and even freely modify the software, or to look at the code behind the software to learn how things can be done. Indeed, anyone is welcome to provide bug fixes, code enhancements, and new packages, and the wealth of quality packages available for R is a testament to this approach to software development.
• R probably has the most complete collection of statistical functions of any statistical or data mining package. New technology and ideas often appear first in R.
• The graphic capabilities of R are outstanding, providing a fully programmable graphics language which surpasses most other statistical and graphical packages.
• A very active email list, with some of the worlds leading statisticians actively responding, is available for anyone to join. Questions are quickly answered and the archive provides a wealth of user solutions and examples. Be sure to read the Posting Guide first.
• Being open source the R source code is peer reviewed, and anyone is welcome to review it and suggest improvements. Bugs are fixed very quickly. Consequently, R is a rock solid product. New packages provided with R do go through a life cycle, often beginning as somewhat less quality tools, but usually quickly evolving into top quality products.
• R plays well with many other tools, importing data, for example, from CSV files, SAS, and SPSS, or directly from MS/Excel, MS/Access, Oracle, and MySQL. It can also produce graphics output in PDF, JPG, PNG, and SVG formats, and table output for L^ATEX and HTML.

Whilst the advantages might flow from the pen with a great deal of enthusiasm, it is useful to note some of the disadvantages or weaknesses of R, even if they are perhaps transitory!

• R is not so easy to use for the novice. There is no simple to use graphical user interface (GUI) that encompasses a nice data processing view with point and click graphics as in commercial and free offerings including Clementine (See Chapter 30), SAS/Enterprise Miner (See Chapter ), and even Weka (See Chapter 28).
• Documentation is sometimes patchy. Whilst there are extensive documents on line and available in books and throughout the Internet, it can sometimes be terse and even impenetrable to the non-statistician. On the other hand, for example, SAS has extensive, self-contained, and often well explained, documentation, readily available to the user. Nonetheless, users do comment that the R documentation is to the point and easy to consult.
• The quality of some packages is less than perfect, although if a package is useful to many people, it will quickly evolve into a very robust product through collaborative effort.
• There is no one to complain to if something doesn't work - at least no one who has a financial interest in keeping you, the user, as a satisfied customer. Organisations are quite happy to pay major premiums for that apparent peace of mind! Nonetheless, problems are usually dealt with quickly on the mailing list, and bugs disappear with lightning speed.

The remaining sections of this chapter can generally be skipped on a reading through the book, but provide a basic reference guide for using R, and in particular, loading in data and manipulating data, as well as some of its programming capabilities. While chapter 2 deals in detail with creating data in R, we introduce some of the basics here. The most basic needs include creating simple datasets, and being familiar with the basic data types and programming concepts, and how to get help.

Brought to you by Togaware.