DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Introduction Data Mining with Rattle Data Exploring Data Transforming Data Cluster Analysis Association Analysis Classification and Regression Models Evaluation and Deployment Issues Moving into R Troubleshooting Beyond Rattle R Data Graphics in R Understanding Data Preparing Data Descriptive and Predictive Analytics Issues Evaluating Models Reporting Cluster Analysis Text Mining Text Mining Algorithms Bagging Bayes Classifier Cluster Analysis Conditional Trees Hierarchical Clustering K-Nearest Neighbours Linear Models Neural Networks Support Vector Machines 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 Installing GTK, R, and Rattle Glossary Bibliography Index

R Dataset

Rattle can use a dataset that is already loaded into R (although it will take a copy of it, with memory implications). Only data frames are currently supported, and Rattle will list for you the names of all of the available data frames.

Figure 3.12: Loading an already defined R data frame

The data frames need to be constructed in the same R session that is running Rattle (i.e., the same R Console in which you lo the Rattle package). This provides much more flexibility in loading data into Rattle, than is provided directly through the actual Rattle interface.

Figure 3.13: Selected region of a spreadsheet copied to the clipboard

This is useful if you have data in a spreadsheet (or any application really) and want to directly paste that data from a clipboard into R. Figure 3.13 illustrates a spreadsheet with three columns of data selected and copied to the clipboard (by the right mouse button menu selecting the Copy option). This data can then be loaded into R quite easily as illustrated in Listing . There, we also convert the date from a string, which is nothing more than a string, into a actual date data type.

[float,caption={Load data from clipboard into \textsf{R}},label={lst:clipboard.into.r}]
> expenses <- read.table(file("clipboard"), header=TRUE)
> expenses$Date <- as.Date(expenses$Date, format="%d-%b-%Y")
> expenses
         Date Expense   Total
1  2005-11-17   19.50   19.50
2  2005-11-23  -15.00    4.50
3  2005-12-10   30.00   34.50
4  2006-01-23 -110.00  -75.50
5  2006-01-28  -20.00  -95.50
6  2006-02-14  -10.00 -105.50
7  2006-02-14  300.00  194.50
8  2006-02-26  220.41  414.91
9  2006-03-03  -20.00  394.91
10 2006-07-14   50.00  444.91
11 2006-07-17   -5.00  439.91
12 2006-09-08 -120.00  319.91
13 2006-09-08 -130.00  189.91
14 2006-10-22   55.00  244.91
15 2006-11-23  135.00  379.91
16 2007-02-11  -90.00  289.91
17 2007-02-22  -20.00  269.91

We can then load this into Rattle directly, as in Figure 3.14.

Figure 3.14: Loading an R data frame originally from the clipboard

As another example, you may want to load data from an SQLite database directly, and have this available in Rattle.

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