## Iris data further analyses

#### part 1
data(iris) 
par(mfrow=c(2,2))
PCA1<-prcomp(iris[,1:4],center=TRUE,scale.=TRUE) 

irisPCAs <- predict(PCA1, newdata = iris)
#head(irisPCAs)
#class(irisPCAs)

irisPCAs<-as.data.frame(irisPCAs)
colours1<-as.numeric(iris[,5])+9
par(mfrow=c(2,2))
plot(irisPCAs$PC1,irisPCAs$PC2,col=colours1)
plot(irisPCAs$PC1,irisPCAs$PC3,col=colours1)
plot(irisPCAs$PC1,irisPCAs$PC4,col=colours1)
plot(irisPCAs$PC2,irisPCAs$PC3,col=colours1)

legend(-5.15,1.9,xpd=NA,c("setosa","versicolor","virginica"), pch=1, col=c("red","green","blue"))

#### part 2 using scatterplot in car

scatterplot(iris$Sepal.Length,iris$Sepal.Width,groups=iris$Species)

#### part 2 using scatterplot in car to plot PCAs

scatterplot(PCA1$x[,1],PCA1$x[,2])