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mms.R
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mms.R
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# A script to provide some insight to M&M distributions.
# Jesse Hamner, 2012
library(foreign)
#setwd("")
fontsize=12
cexval=1.2
# A function to draw a distribution normal curve over the histogram:
# (Thanks to Peter Dalgaard)
addNorm <- function(data,color,linewidth) {
xfit<-seq(min(data),max(data),length=80)
yfit<-dnorm(xfit,mean=mean(data),sd=sd(data))
yfit <- yfit*diff(h$mids[1:2])*length(data)
lines(xfit, yfit, col=color, lwd=linewidth)
return;
}
addText <- function(xcoord,ycoord,avg,sd,med) {
text(xcoord,ycoord,labels=paste("µ = ",avg,sep=""), cex=1.5)
text(xcoord,(ycoord-0.7),labels=paste("σ = ",sd,sep=""),cex=1.5)
text(xcoord,(ycoord-1.4),labels=paste("med. = ",med,sep=""),cex=1.5)
return;
}
flatTop <- function(datavector,topofrange) {
for(i in 1:length(datavector)) {
if (datavector[i]>topofrange) { datavector[i]=topofrange }
}
return(datavector);
}
# get grades for the first exam:
mms<-read.table("MM2012.csv", sep = "\t", header=TRUE, fill=TRUE)
attach(mms)
sumstat=Total
# Create summary statistics for Exam 1 raw values:
minimum=min(sumstat)
maximum=max(sumstat)
avg<-round(mean((sumstat)),digits=1)
sd<-round(sd(sumstat),digits=1)
mediangrade<-median(sumstat)
png(filename="Totalmmsrawhist.png", res=300, bg="white", type="quartz", pointsize=fontsize, width=6, height=6, units="in")
# Histogram
h<-hist(x=sumstat,breaks=8,
col="gray",
main="Histogram of M&Ms",
cex=1.0,
xlab=paste("Total","Count", sep=" "),
ylim=c(0,12),
xlim=c(45,65)
)
# Annotation text:
addText(xcoord=50,ycoord=6,avg=avg,sd=sd,med=mediangrade)
text(70,8.0,labels=paste(paste(paste("Range: [",minimum,sep=""),maximum,sep=","),"]",sep=""),cex=cexval)
#text(50,6.75,labels=paste("Raw Min = ",minimum,sep=""),cex=1.4)
#text(50,6.25,labels=paste("Raw Max = ",maximum,sep=""),cex=1.4)
addNorm(data=sumstat,color="red", linewidth=2)
dev.off()
# Second Histogram
sumstat=Blue
# Create summary statistics for Exam 1 raw values:
minimum=min(sumstat)
maximum=max(sumstat)
avg<-round(mean((sumstat)),digits=1)
sd<-round(sd(sumstat),digits=1)
mediangrade<-median(sumstat)
#png(filename="Bluemmsrawhist.png", res=300, bg="white", type="quartz", pointsize=fontsize, width=6, height=6, units="in")
# Histogram
h<-hist(x=sumstat,breaks=8,col="gray", main="Histogram of Blue M&Ms", cex=1.0, xlab=paste("Blue","Count", sep=" "), ylim=c(0,12), xlim=c(5,20))
# Annotation text:
addText(xcoord=7,ycoord=9,avg=avg,sd=sd,med=mediangrade)
#text(70,8.0,labels=paste(paste(paste("Range: [",minimum,sep=""),maximum,sep=","),"]",sep=""),cex=1.2)
#text(50,6.75,labels=paste("Raw Min = ",minimum,sep=""),cex=1.4)
#text(50,6.25,labels=paste("Raw Max = ",maximum,sep=""),cex=1.4)
addNorm(data=sumstat,color="red", linewidth=2)
#dev.off()
# Third Histogram
sumstat=Green
# Create summary statistics for Exam 1 raw values:
minimum=min(sumstat)
maximum=max(sumstat)
avg<-round(mean((sumstat)),digits=1)
sd<-round(sd(sumstat),digits=1)
mediangrade<-median(sumstat)
png(filename="Greenmmsrawhist.png", res=300, bg="white", type="quartz", pointsize=fontsize, width=6, height=6, units="in")
# Histogram
h<-hist(x=sumstat,breaks=8,col="gray", main="Histogram of Green M&Ms", cex=1.0, xlab=paste("Green","Count", sep=" "), ylim=c(0,7), xlim=c(6,18))
# Annotation text:
addText(xcoord=12,ycoord=6,avg=avg,sd=sd,med=mediangrade)
#text(70,8.0,labels=paste(paste(paste("Range: [",minimum,sep=""),maximum,sep=","),"]",sep=""),cex=1.2)
#text(50,6.75,labels=paste("Raw Min = ",minimum,sep=""),cex=1.4)
#text(50,6.25,labels=paste("Raw Max = ",maximum,sep=""),cex=1.4)
addNorm(data=sumstat,color="red", linewidth=2)
dev.off()
# Fourth Histogram
sumstat=Brown
# Create summary statistics for Exam 1 raw values:
minimum=min(sumstat)
maximum=max(sumstat)
avg<-round(mean((sumstat)),digits=1)
sd<-round(sd(sumstat),digits=1)
mediangrade<-median(sumstat)
png(filename="Brownmmsrawhist.png", res=300, bg="white", type="quartz", pointsize=fontsize, width=6, height=6, units="in")
# Histogram
h<-hist(x=sumstat,breaks=8,col="gray", main="Histogram of Brown M&Ms", cex=1.0, xlab=paste("Brown","Count", sep=" "), ylim=c(0,7), xlim=c(2,14))
# Annotation text:
addText(xcoord=12,ycoord=5,avg=avg,sd=sd,med=mediangrade)
#text(70,8.0,labels=paste(paste(paste("Range: [",minimum,sep=""),maximum,sep=","),"]",sep=""),cex=1.2)
#text(50,6.75,labels=paste("Raw Min = ",minimum,sep=""),cex=1.4)
#text(50,6.25,labels=paste("Raw Max = ",maximum,sep=""),cex=1.4)
addNorm(data=sumstat,color="red", linewidth="2")
dev.off()
# fifth Histogram
sumstat=Weight
# Create summary statistics for Exam 1 raw values:
minimum=min(sumstat)
maximum=max(sumstat)
avg<-round(mean((sumstat)),digits=1)
sd<-round(sd(sumstat),digits=1)
mediangrade<-median(sumstat)
png(filename="Weightmmsrawhist.png", res=300, bg="white", type="quartz", pointsize=fontsize, width=6, height=6, units="in")
# Histogram
h<-hist(x=sumstat,breaks=4,col="gray", main="Histogram of M&M Bag Weights", cex=1.0, xlab=paste("Weight","of each bag", sep=" "), ylim=c(2,14), xlim=c(47,52))
# Annotation text:
addText(xcoord=48,ycoord=12,avg=avg,sd=sd,med=mediangrade)
text(48,9.7,labels=paste(paste(paste("Range: [",minimum,sep=""),maximum,sep=","),"]",sep=""),cex=1.2)
#text(50,6.75,labels=paste("Raw Min = ",minimum,sep=""),cex=1.4)
#text(50,6.25,labels=paste("Raw Max = ",maximum,sep=""),cex=1.4)
addNorm(data=sumstat,color="red", linewidth="2")
dev.off()
detach(mms)
# EOF