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

Basic Histogram with Density Curve

R allows plots to be built up--this example shows a density histogram of a set of random numbers extracted from a normal distribution with the density curve of the same normal distribution also displayed. In the R code we build the histogram at first without plotting it, so as to determine the y limits ($range$ selects the minimum and maximum values, while $h\$density$ is the list of density values being plotted and $dnorm(0)$ is the maximum possible value of the density), since otherwise the curve might push up into the title!

ds <- rnorm(200)
pdf("graphics/rplot-hist-density.pdf")
  par(xpd=T)
  h <- hist(ds, plot=F)
  ylim <- range(0, h$density, dnorm(0))
  hist(ds, xlab="normal", ylim=ylim, freq=F,
       main="Histogram of Normal Distribution with Density")
  curve(dnorm, col=2, add=T)
dev.off()

http://rattle.togaware.com/code/rplot-hist-density.R

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