# Various aspects of 1-D histogram

#read in data..

test=matrix(scan("Leesferry-mon-data.txt"),ncol=13,byrow=T)
test=matrix(scan("http://civil.colorado.edu/~balajir/r-session-files/Leesferry-mon-data.txt"),ncol=13,byrow=T)

flows=test[,2:13] * 0.0004690502    #get it into CMS
flows=test[,2:13]/10^6     #get it into MAF

#look at the May PDF

X = flows[,5]

library(Rlab)

par(mfrow=c(2,2))

N=length(X)

# of Bins based on Sturge's formula
ncls = round(log(N,2) + 1)

library(sm)

#Default histogram over a wider range
hplot(X,xlab="Flow cms",main="Default Histogram")
points(X,rep(0,length(X)))
sm.density(X, add=T, col="red")

#ncls bins based on Struge's formula - divided between max and min of the data
hplot(X, nclass = ncls, xlab="Flow cms", main="Histogram - Sturge's #of Bins")
points(X,rep(0,length(X)))
sm.density(X, add=T, col="red")