setwd("C:\\Users\\Owner\\Desktop\\SchoolStuff\\Spring2013\\Presentations\\Graphs") #set working directory
data()#display all available data sets
names(mtcars)#variable names in mtcar
help(mtcars) #description ov variables
attach(mtcars)
#windows() #open new graph window; avoid overwritting the old graph;


 #scatterplot

plot(wt, mpg)  #scatterplot
abline(lm(mpg~wt))#OLS line fit
title("Regression of MPG on Weight")   #title




 #scatterplot with multiple y values
plot(wt, hp, pch="hp", col="red", ylab="", xlab="weight")
points(wt, disp, pch="d", col="green")
title("Regression of Weight on Horsepower and Displacement")   #title


 #barplots
 
 # Simple Bar Plot
counts <- table(mtcars$gear)
barplot(counts, main="Car Distribution",
   xlab="Number of Gears")
   
# Stacked Bar Plot with Colors and Legend
counts <- table(mtcars$vs, mtcars$gear)
barplot(counts, main="Car Distribution by Gears and VS",
  xlab="Number of Gears", col=c("darkblue","red"),
  legend = rownames(counts))
 # Grouped Bar Plot
counts <- table(mtcars$vs, mtcars$gear)
barplot(counts, main="Car Distribution by Gears and VS",
  xlab="Number of Gears", col=c("darkblue","red"),
  legend = c("vs = 0", "vs = 1" ), beside=TRUE)
  
# Fitting Labels
#las=2 # make label text perpendicular to axis
#cex.names changes the font size for labels
par(mar=c(5,8,4,2)) # increase y-axis margin.
counts <- table(mtcars$gear)
barplot(counts, main="Car Distribution", horiz=TRUE, names.arg=c("3 Gears", "4 Gears", "5   Gears"), cex.names=0.8, las=2)

 #histogram
hist(mpg, breaks=12, col="red", xlab="Miles/gallon", main="Histogram of mpg") #colored histogram with different number of bins
xvals<-seq(from=min(mpg), to=max(mpg) , length=40)
lines(pnorm(xvals, mean(mpg), sd(mpg))
 
#add a normal density curve to a histogram
x<-mpg
h<-hist(x, breaks=12, col="turquoise2", xlab="Miles Per Gallon",
   main="Histogram with Normal Curve")
xfit<-seq(min(x),max(x),length=40)   #a sequence of numbers from min x to max x of length 40
yfit<-dnorm(xfit,mean=mean(x),sd=sd(x)) #density plot , returns prob
yfit <- yfit*diff(h$mids[1:2])*length(x)  #diff(h$mids[1:2]) is the difference between 2 midpoints , depends on numebre of bins spec by breaks
lines(xfit, yfit, col="blue", lwd=2)


 #stem-and-leaf plot
 stem(carb)

 
 #side by side boxplots
 
 # Boxplot of MPG by Car Cylinders
bx=boxplot(mpg~cyl,data=mtcars, main="Car Milage Data",
xlab="Number of Cylinders", ylab="Miles Per Gallon", col="skyblue")
# Violin Plots
library(vioplot)
x1 <- mtcars$mpg[mtcars$cyl==4]
x2 <- mtcars$mpg[mtcars$cyl==6]
x3 <- mtcars$mpg[mtcars$cyl==8]
vioplot(x1, x2, x3, names=c("4 cyl", "6 cyl", "8 cyl"),
   col="gold")
title("Violin Plots of Miles Per Gallon")

 
 #QQ plot
 qqnorm(mpg)
 qqline(mpg)
 #Interaction plot
data(ToothGrowth)
attach(ToothGrowth)
png("Scatterplot.png") #redirect graph as a PNG image
interaction.plot(dose, supp, len, fixed = TRUE, type = c("o"), col=c("red","blue"), font.main=4, col.main="darkgreen",legend=FALSE , main="Interaction Plot between Dose and Supplement Type", ylab="Mean of Tooth Length", xlab="Dose", font.lab=3)
legend("bottomright", legend = c ("VC", "OJ"),lty=c(3,1), col=c("red","blue"))

dev.off() #close graphical device

 #Density plot
 #plot univariate density curve;
d <-density(mpg)
plot(d, main="Kernel Density")
polygon(d, col="wheat4", border="red")  #fill the density plot

 #3-D plots
 # 3D Scatterplot with Coloring and Vertical Lines and Regression Plane
library(scatterplot3d)
s3d <-scatterplot3d(wt,disp,mpg, pch=16, highlight.3d=TRUE,
  type="h", main="3D Scatterplot")
fit <- lm(mpg ~ wt+disp)
s3d$plane3d(fit)

#add different types of lines to a graph; ref Quick - R
x <- c(1:5); y <- x # create some data
par(pch=22, col="red") # plotting symbol and color
par(mfrow=c(2,4)) # all plots on one page
opts = c("p","l","o","b","c","s","S","h")
for(i in 1:length(opts)){
  heading = paste("type=",opts[i])
  plot(x, y, type="n", main=heading)
  lines(x, y, type=opts[i])
}

#InClassActivity
#plot the within/between corelation and rho(ICC);
sdb<-seq(1, 30, 1)
sdw<-c(5,10,15,20,25)

icc<-matrix(ncol=30, nrow=5, NA)

for(i in 1:length(sdw)){
  for(j in 1:length(sdb))   {
  icc[i,j]<-sdb[j]^2/(sdb[j]^2+sdw[i]^2)     }}

one<-expression(paste(sigma[b]^2, sep=" ", "(Betwen Subject Variation)"))  #subscript b and squared;
plot(sdb^2, icc[1,],  pch=19, col="blue", type="l", xlab=c(one), ylab ="ICC", cex=3, lwd=3, font=12, main = "Intraclass Correlation Coefficint (ICC) \n under Compound Symmetry (CSS)")

for (i in 1:length(sdw))
  lines(sdb^2, icc[i,], lwd=3, col = c(i*13))

lTitle= expression(paste(sigma^2, "(Within Subject Variation)"))

legend("bottomright", c("25", "100", "225", "400", "625"), title=lTitle, col=c(13,26 ,3*13, 4*13, 5*13), pch=19, bg='gray90', bty="n", cex=.7)