#Setup
setwd("C:/Users/Mark.Williamson.2/Desktop/Willliamson Presentations/Statistical Modules") #you'll have to set your own
library(ggplot2)
library(dplyr)
library(rgl)
#############################################################################################


#Bubble Plots

#iris
head(iris)

ggplot(data=iris, aes(x=Sepal.Length, y=Sepal.Width, size=Petal.Length))+
  geom_point(alpha=0.7)
ggplot(data=iris, aes(x=Sepal.Length, y=Sepal.Width, size=Petal.Length, color=Species))+
  geom_point(alpha=0.7)

#insular
insular<-read.csv("insular.csv")
attach(insular)
print(insular)

ggplot(data=insular, aes(x=Area ,y=elevation, size=SpeciesRichness)) +
  geom_point(alpha=0.7)
#############################################################################################


#3D Scatterplots

#iris
par(mar=c(0,0,0,0))
plot3d( 
  x=iris$Sepal.Length, y=iris$Sepal.Width, z=iris$Petal.Length)

# Add a new column with color
mycolors <- c('royalblue1', 'darkcyan', 'oldlace')
iris$color <- mycolors[ as.numeric(iris$Species)] 
                         
plot3d( 
  x=iris$Sepal.Length, y=iris$Sepal.Width, z=iris$Petal.Length,
  col = iris$color,
  type = 's', 
  radius = .1,
  xlab="Sepal Length", ylab="Sepal Width", zlab="Petal Length")


#insular
plot3d(
  x=Area, y=elevation, z=SpeciesRichness,
  col=c("red"),
  type='s',
  size=2)

par(mar=c(5.1, 4.1, 4.1, 2.1))
#############################################################################################


#PCA

#iris
# log transform 
log.ir <- log(iris[, 1:4])
ir.species <- iris[, 5]
# apply PCA - scale. = TRUE is highly 
# advisable, but default is FALSE. 
ir.pca <- prcomp(log.ir,
                 center = TRUE,
                 scale. = TRUE) 

print(ir.pca)
plot(ir.pca, type = "l")
summary(ir.pca)

library(ggfortify)
autoplot(ir.pca)
autoplot(ir.pca, data = iris, colour = 'Species', 
         loadings=TRUE, loadings.label=TRUE, loadings.label.size=3)


#insular
head(insular)

in.islands<-insular[, 1]
in.pca <- prcomp(insular[,2:12],
                 center = TRUE,
                 scale. = TRUE) 

print(in.pca)
plot(in.pca, type = "l")
summary(in.pca)
autoplot(in.pca)
autoplot(in.pca, data=insular, colour='Islands',
         loadings=TRUE, loadings.label=TRUE, loadings.label.size=3)
#############################################################################################


#Scatterplot matrices:

# Basic Scatterplot Matrix
pairs(~Sepal.Length+Sepal.Width+Petal.Length+Petal.Width, data=iris)
pairs(~SpeciesRichness+endemic+Area+altitude+elevation+distanceParamo, data=insular)

#Multiplots:
Scars <-read.csv("SAS cars.csv")
head(Scars)
attach(Scars)

boxplot(MSRP)
boxplot(MSRP~DriveTrain)
boxplot(MSRP~DriveTrain*Origin,
  col=c('blue','red','green'))
boxplot(MSRP~DriveTrain*Origin*Type,
  col=c('blue','red','green'), cex.axis=0.5)

par(mfrow=c(3,2))
boxplot(MSRP[Type=='Hybrid']~DriveTrain[Type=='Hybrid']*Origin[Type=='Hybrid'], main='Hybrid',cex.axis=0.75)
boxplot(MSRP[Type=='SUV']~DriveTrain[Type=='SUV']*Origin[Type=='SUV'], main='SUV',cex.axis=0.75)
boxplot(MSRP[Type=='Sedan']~DriveTrain[Type=='Sedan']*Origin[Type=='Sedan'], main='Sedan',cex.axis=0.75)
boxplot(MSRP[Type=='Sports']~DriveTrain[Type=='Sports']*Origin[Type=='Sports'], main='Sports',cex.axis=0.75)
boxplot(MSRP[Type=='Truck']~DriveTrain[Type=='Truck']*Origin[Type=='Truck'], main='Truck',cex.axis=0.75)
boxplot(MSRP[Type=='Wagon']~DriveTrain[Type=='Wagon']*Origin[Type=='Wagon'], main='Wagon',cex.axis=0.75)
par(mfrow=c(1,1))

#Monstrosity
boxplot(MSRP~DriveTrain*Origin*Type*as.factor(Cylinders))

#Scatter Plot Colored by categories 
lnMSRP <-log(MSRP)
plot(MPG_Highway, lnMSRP, pch=16, cex=0.70)
plot(MPG_Highway, lnMSRP, pch=16, cex=0.70, col=Origin)
  legend("topright", legend=c("Asia","Europe","US"), fill=c('black','green','red'))