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

Arrow Axes

The par function in R can be used to fine tune characteristics of a plot. Here we add arrows to the axes. For many more arrow options see the p.arrows function in the sfsmisc package.

In the example here, par("usr") is used to identify the bounds of the plot, and these are then employed to draw axes with arrows. The arrows function takes a starting point and an ending point for the line to be drawn. The code=2 selects the type of arrow head to draw and the xpd=TRUE ensures the arrows are not cropped to the default size of the plot (the plot region).

[width=0.7]rplot-iris-arrows

attach(iris)
plot(Petal.Length, Petal.Width, axes=FALSE)
u <- par("usr")
arrows(u[1], u[3], u[2], u[3], code=2, xpd=TRUE)
arrows(u[1], u[3], u[1], u[4], code=2, xpd=TRUE)

http://rattle.togaware.com/code/rplot-iris-arrows.R

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