# R in Action (2nd ed): Chapter 11
# Intermediate graphs
# requires packages car, scatterplot3d, gclus, hexbin, IDPmisc, Hmisc, 
#                   corrgram, vcd, rlg, Rcmdr to be installed
# install.packages(c("car", "scatterplot3d", "gclus", "hexbin", "IDPmisc", "Hmisc", 
                     "corrgram", "vcd", "rld", "Rcmdr"))
#------------------------------------------------------------------------------------

par(ask=TRUE)
opar <- par(no.readonly=TRUE) # record current settings

# A scatterplot with best fit lines
attach(mtcars)                                                     
plot(wt, mpg, 
     main="Basic Scatterplot of MPG vs. Weight",       
     xlab="Car Weight (lbs/1000)", 
     ylab="Miles Per Gallon ", pch=19)
abline(lm(mpg ~ wt), col="red", lwd=2, lty=1)            
lines(lowess(wt, mpg), col="blue", lwd=2, lty=2)   

library(car) 
scatterplot(mpg ~ wt | cyl, data=mtcars, lwd=2,
            main="Scatter Plot of MPG vs. Weight by # Cylinders", 
            xlab="Weight of Car (lbs/1000)", 
            ylab="Miles Per Gallon", id.method="identify",
            legend.plot=TRUE, labels=row.names(mtcars), 
            boxplots="xy")


# scatterplot matrices
pairs(~ mpg + disp + drat + wt, data=mtcars, 
      main="Basic Scatterplot Matrix")
   
library(car)
scatterplotMatrix(~ mpg + disp + drat + wt, data=mtcars, spread=FALSE,
                 lty.smooth=2, main="Scatterplot Matrix via car package")
scatterplotMatrix(~ mpg + disp + drat + wt | cyl, data=mtcars, spread=FALSE,
                  main="Scatterplot Matrix via car package", diagonal="histogram")

cor(mtcars[c("mpg", "wt", "disp", "drat")])

library(gclus)                                                   
mydata <- mtcars[c(1,3,5,6)]                               
mydata.corr <- abs(cor(mydata))                                                                           
mycolors <- dmat.color(mydata.corr)                              
myorder <- order.single(mydata.corr)                             
cpairs(mydata,                                                   
       myorder, 
       panel.colors=mycolors, 
       gap=.5,
       main="Variables Ordered and Colored by Correlation"
)

# high density scatterplots
set.seed(1234)
n <- 10000
c1 <- matrix(rnorm(n, mean=0, sd=.5), ncol=2)
c2 <- matrix(rnorm(n, mean=3, sd=2), ncol=2)
mydata <- rbind(c1, c2)
mydata <- as.data.frame(mydata)
names(mydata) <- c("x", "y")

with(mydata,
     plot(x, y, pch=19, main="Scatter Plot with 10000 Observations"))

with(mydata,
     smoothScatter(x, y, main="Scatterplot colored by Smoothed Densities"))

library(hexbin)
with(mydata, {
     bin <- hexbin(x, y, xbins=50)
     plot(bin, main="Hexagonal Binning with 10,000 Observations")
     })

library(IDPmisc)
with(mydata,
     iplot(x, y, main="Image Scatter Plot with Color Indicating Density"))
par(opar)

# 3-D Scatterplots
library(scatterplot3d)
attach(mtcars)
scatterplot3d(wt, disp, mpg, 
              main="Basic 3D Scatterplot")
 
scatterplot3d(wt, disp, mpg, 
              pch=16, 
              highlight.3d=TRUE,
              type="h",
              main="3D Scatterplot with Vertical Lines")

s3d <-scatterplot3d(wt, disp, mpg, 
                    pch=16, 
                    highlight.3d=TRUE,
                    type="h", 
                    main="3D Scatter Plot with Verical Lines and Regression Plane")
fit <- lm(mpg ~ wt+disp) 
s3d$plane3d(fit)
detach(mtcars)

# spinning 3D plot
library(rgl)
attach(mtcars)
plot3d(wt, disp, mpg, col="red", size=5)

# alternative
rgl.open()
library(Rcmdr)
attach(mtcars)
scatter3d(wt, disp, mpg)


# bubble plots
attach(mtcars)
r <- sqrt(disp/pi)
symbols(wt, mpg, r, inches=0.30, fg="white", bg="lightblue",
main="Bubble Plot with point size proportional to displacement",
ylab="Miles Per Gallon",
xlab="Weight of Car (lbs/1000)")
text(wt, mpg, rownames(mtcars), cex=0.6)
detach(mtcars)
par(opar)



# Creating side by side scatter and line plots
par(mfrow=c(1,2))
t1 <- subset(Orange, Tree==1)
plot(t1$age, t1$circumference,
     xlab="Age (days)", 
     ylab="Circumference (mm)", 
     main="Orange Tree 1 Growth")
plot(t1$age, t1$circumference,
     xlab="Age (days)",
     ylab="Circumference (mm)",
     main="Orange Tree 1 Growth", 
     type="b")
par(opar)

# Line chart displaying the growth of 5 Orange trees over time
Orange$Tree <- as.numeric(Orange$Tree)      
ntrees <- max(Orange$Tree)

xrange <- range(Orange$age)                 
yrange <- range(Orange$circumference)        

plot(xrange, yrange,                        
    type="n",                               
    xlab="Age (days)",                      
    ylab="Circumference (mm)"               
 )                                          

colors <- rainbow(ntrees)                   
linetype <- c(1:ntrees)                     
plotchar <- seq(18, 18+ntrees, 1)           

for (i in 1:ntrees) {                       
     tree <- subset(Orange, Tree==i)          
     lines(tree$age, tree$circumference,      
           type="b",                           
           lwd=2,                              
           lty=linetype[i],                     
           col=colors[i],                      
           pch=plotchar[i]                     
     )                                       
}                                            

title("Tree Growth", "example of line plot")

legend(xrange[1], yrange[2],                
       1:ntrees,                                
       cex=0.8,                                
       col=colors,                             
       pch=plotchar,                           
       lty=linetype,                           
       title="Tree"                            
)                                           

# Correlograms
options(digits=2)
cor(mtcars)

library(corrgram)
corrgram(mtcars, order=TRUE, lower.panel=panel.shade,
         upper.panel=panel.pie, text.panel=panel.txt,
         main="Correlogram of mtcar intercorrelations")

corrgram(mtcars, order=TRUE, lower.panel=panel.ellipse,
         upper.panel=panel.pts, text.panel=panel.txt,
         diag.panel=panel.minmax, 
         main="Correlogram of mtcar data using scatterplots and ellipses")
    
corrgram(mtcars, lower.panel=panel.shade,
         upper.panel=NULL, text.panel=panel.txt,
         main="Car Mileage Data (unsorted)")

col.corrgram <- function(ncol){   
                  colorRampPalette(c("darkgoldenrod4", "burlywood1",
                                     "darkkhaki", "darkgreen"))(ncol)} 
corrgram(mtcars, order=TRUE, lower.panel=panel.shade, 
         upper.panel=panel.pie, text.panel=panel.txt, 
         main="A Corrgram (or Horse) of a Different Color")


# Mosaic Plots
ftable(Titanic)
library(vcd)
mosaic(Titanic, shade=TRUE, legend=TRUE)


# type= options in the plot() and lines() functions
x <- c(1:5)
y <- c(1:5)
par(mfrow=c(2,4))
types <- c("p", "l", "o", "b", "c", "s", "S", "h")
for (i in types){
     plottitle <- paste("type=", i)
     plot(x,y,type=i, col="red", lwd=2, cex=1, main=plottitle)
}