DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Data Mining Data Mining with Rattle Introduction Data Transform Explore A Model Building Framework Unsupervised Modelling Two Class Models Multi Class Models Regression Models Text Mining Evaluation and Deployment Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Cluster Analysis Conditional Trees Hierarchical Clustering K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

Windows, Icons, Mouse, Pointer--WIMP

Graphical interfaces might be good for a while, but soon become restrictive. Nonetheless, people like to get into new tools graphically, so we will introduce a common option within R, using Rcmdr, whilst noting that rattle (See Chapter 2) is an alternative, specifically targeted for data mining.

Rcmdr is the most feature full R GUI. To start it up, simply load the package:

> library(Rcmdr)

A basic GUI interface with split screens will appear, as in Figure .

Figure: The R Commander GUI provides a simplified interface to using R. The interface consists of the top edit window and the lower execution and results window.

[width=0.8]rcmdr-main

The top window in the GUI records the commands or functions that are to be sent to R. You can edit the commands here or else use the menus to generate the commands for you. The lower window shows the commands as they are evaluated and their output.

The GUI provides an excellent pathway into learning how to use R. Using various menus and resulting pop-up windows to build R functions can be very instructive. For some, the Rcmdr may be all that is needed for using R, allowing commands to be entered directly and then saved to script files, either as a record of processing and modelling, or else for later automatic regeneration of results.

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