#Nutritional Dataset

names(NutritionData)

hist(NutritionData$SatFat, data=NutritionData)

hist(NutritionData$SatFat,breaks=20,freq=T,xlab="Saturated Fat", main="Histogram of Saturated Fat",col='gray')
hist(NutritionDatal$SatFat,breaks=c(0,5, 10,15,20,25, 30),freq=T,xlab="Saturated Fat", main="Histogram of Saturated Fat",col='gray')

hist(NutritionData$SatFat,breaks=20,freq=F,xlab="Saturated Fat", main="Histogram of Saturated Fat",col='gray')
lines(density(NutritionData$SatFat))


hist(NutritionData$SatFat,breaks=40,freq=F,xlab="Saturated Fat", main="Histogram of Saturated Fat", col='gray')
lines(density(NutritionData$SatFat,adjust=0.50),lty=2)


boxplot(NutritionData$SatFat ~ NutritionData$Location)



#Change factor level order
NutritionData$Location<-factor(NutritionData$Location,levels=c("BurgerKing","KFC","McDonalds","TacoBell","Wendys","Dominos","PizzaHut","ErbertGerbert","Subway"))
boxplot(NutritionData$SatFat ~ NutritionData$Location)

#USe to change back to original order if needed
#NutritionData$Location<-factor(NutritionData$Location,levels=c("BurgerKing","Dominos","ErbertGerbert","KFC","McDonalds","PizzaHut","Subway","TacoBell","Wendys"))

aggregate(NutritionData$SatFat,by=list(NutritionData$Location), mean)

#Barcharts
table(NutritionData$Location)
barplot(table(NutritionData$Location))
pie(table(NutritionData$Location))

table(NutritionData$Type, NutritionData$Location)
barplot(table(NutritionData$Type, NutritionData$Location), legend=TRUE)


table.of.counts<-table(NutritionData$Type,NutritionData$Location)
barplot(prop.table(table.of.counts,2),legend=TRUE)
table.of.counts


plot(TotalFat~SatFat,data=NutritionData)
abline(lm(TotalFat~SatFat,data=NutritionData))


#Graphical Parameters
par(mfrow=c(1,2)) #Create a 1 x 2 grid for plotting
par(pty="m") #Force square plotting region, par(pty="m") will change back to default


plot(TotalFat~SatFat,data=NutritionData, xlab="Saturated Fat", ylab="Total Fat", xlim=c(0,60), ylim=c(0,60))


plot(TotalFat~SatFat,data=NutritionData, xlab="Saturated Fat", ylab="Total Fat", col="blue",pch=2)

#Creating the intial plot
plot(TotalFat~SatFat,data=NutritionData)

#Fitting a loess model
fit<-loess(TotalFat~SatFat,data=NutritionData, span=.3)

#Using a generic sequence for x to obtain a prediction line
#lines(seq(0,30,1), predict(fit,newdata=data.frame(SatFat=seq(0,30,1))))

#Using actual SatFat data valus to obtain a prediction
lines(sort(NutritionData$SatFat), predict(fit,newdata=NutritionData[order(NutritionData$SatFat),]))



fit<-loess(TotalFat~SatFat,data=NutritionData, span=.3)
#lines(seq(0,30,1), predict(fit,newdata=data.frame(SatFat=seq(0,30,1))))
lines(sort(NutritionData$SatFat), predict(fit,newdata=NutritionData[order(NutritionData$SatFat),]))





scatter.smooth(NutritionData$SatFat,NutritionData$TotalFat,span=.4)


#Get subset of only beef and chicken

BeefChickenData <- NutritionData[NutritionData$Type == "Beef" | NutritionData$Type == "Chicken", ]
BeefChickenData <- NutritionData[NutritionData$Type %in% c("Beef","Chicken"), ]


plot(TotalFat~SatFat,data=BeefChickenData,xlab="Saturated Fat", ylab="Total Fat", 
     col=c("red","blue")[match(Type,c("Beef","Chicken"))])

abline(lm(TotalFat~SatFat,data=BeefChickenData[BeefChickenData$Type=="Beef",]),col="red")
abline(lm(TotalFat~SatFat,data=BeefChickenData[BeefChickenData$Type=="Chicken",]),col="blue")
abline(0,1,col="gray",lty=2)
title("Red = Beef, Blue= Chicken")

legend("bottomright",legend=c("Beef","Chicken"),fill=c("red","blue"))
legend(25,15,legend=c("Beef","Chicken"),fill=c("red","blue"))



#Plots with Loess smoothers
plot(TotalFat~SatFat,data=BeefChickenData,xlab="Saturated Fat", ylab="Total Fat", 
     col=c("red","blue")[match(Type,c("Beef","Chicken"))], type="n")

fit.beef<-loess(TotalFat~SatFat,data=BeefChickenData[BeefChickenData$Type=="Beef",])
lines(sort(BeefChickenData[BeefChickenData$Type == "Beef",6]), sort(predict(fit.beef,newdata=BeefChickenData[BeefChickenData$Type == "Beef",])), col="red")

fit.chicken<-loess(TotalFat~SatFat,data=BeefChickenData[BeefChickenData$Type=="Chicken",])
lines(sort(BeefChickenData[BeefChickenData$Type == "Chicken",6]), sort(predict(fit.chicken,newdata=BeefChickenData[BeefChickenData$Type == "Chicken",])), col="blue")
abline(0,1,col="gray",lty=2)
legend("bottomright",legend=c("Beef","Chicken"),fill=c("red","blue"))



#Lattice graphics
library(lattice)
histogram(~SatFat,data=NutritionData)
histogram(~SatFat|Location,data=NutritionData, col="gray")

densityplot(~SatFat|Location,data=NutritionData, col="black",plot.points=FALSE)
densityplot(~SatFat|Location,data=NutritionData, col="black")


BeefChickenData$Type<-factor(BeefChickenData$Type,levels=c("Beef","Chicken"))

densityplot(~SatFat, data=BeefChickenData, groups=Type, plot.points=FALSE, auto.key=TRUE)

xyplot(TotalFat~SatFat, data=NutritionData)

xyplot(TotalFat~SatFat|Location, data=NutritionData)


#Loess in xyplot
xyplot(TotalFat~SatFat|Location,data=NutritionData, type="smooth")
xyplot(TotalFat~SatFat|Location,data=NutritionData, type=c("p","smooth"))


panel.smoother <- function(x, y) {
  #panel.xyplot(x, y, pch=2, cex=0.5) # show points 
  panel.loess(x, y)  # show smoothed line 
}

xyplot(TotalFat~SatFat|Location,data=NutritionData, panel = panel.smoother)



xyplot(TotalFat~SatFat,data=NutritionData, groups=Location, type=c("p","smooth"))

pairs(NutritionData[,5:10], panel=panel.smooth)

splom(~BeefChickenData[,5:10], data=BeefChickenData, groups=BeefChickenData$Type, 
      panel=panel.superpose, 
      key=list(title="Scatterplot Matrix",
               columns=2,
               points=list(pch=1:2,
                           col=4:5),
               text=list(c("Beef","Chicken"))))


splom(BeefChickenData[,5:10], groups=BeefChickenData$Type, data=BeefChickenData, panel=panel.superpose)