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

Histogram

A histogram provides a quick and useful graphical view of the spread of the data. A histogram plot in Rattle includes three components. The first of these is obviously the coloured vertical bars. The continuous data in the example here (Distribution of Income) has been partitioned into ranges, and the frequency of each range is displayed as the bar. R is automatically choosing both the partitioning and how the x-axis is labelled here, showing x-axis points at 0, 10,000 (using scientific notation of $1e+05$ which means $1x10^5$, or 10,000), and so on. Thus, we can see that the most frequent range of values is in the $0-5,000$ partition. However, each partition spans quite a large range (a range of $5,000).

The plot also includes a line plot showing the so called density estimate and is a more accurate display of the actual (at least estimated true) distribution of the data (the values of XnullXXnullXRattle!VariablesR functions (R function)Rattle!VariablesR libraries (R library)Rattle!VariablesR option (R option)Rattle!VariablesR packages (R package)Rattle!VariablesDatasets (Dataset)XnullXRattle!VariablesRattle!VariablesIncome). It allows us to see that rather than values in the range $0-5,000$ occurring frequently, in fact there is a much smaller range (perhaps $3,000-5,000$) that occurs very frequently.

The third element of the plot is the so called rug along the bottom of the plot. The rug is a single dimension plot of the data along the number line. It is useful in seeing exactly where data points actually lay. For large collections of data with a relatively even spread of values the rug ends up being quite black, as is the case here, up to about $25,000. Above about $35,000 we can see that there is only a splattering of entities with such values. In fact, from the Summary option, using the Describe check box, we can see that the highest values are actually $36,1092.60, $38,0018.10, $39,1436.70, $40,4420.70, and $42,1362.70.

This second plot, showing the distribution for the variable XnullXXnullXRattle!VariablesR functions (R function)Rattle!VariablesR libraries (R library)Rattle!VariablesR option (R option)Rattle!VariablesR packages (R package)Rattle!VariablesDatasets (Dataset)XnullXRattle!VariablesRattle!VariablesHours, illustrates a more normal distribution. It is, roughly speaking, a distribution with a peak in the middle and diminishing on both sides, with regards the frequency. The density plot shows that it is not a very strong normal distribution, and the rug plot indicates that the data take on very distinct values (i.e., one would suggest that they are integer values, as is confirmed through viewing the textual summaries in the Summary option).

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