RinA CH11 Code.txt

#-------------------------------------------------------------------------#
# R in Action: Chapter 11                                                 #
# requires that the car, scatterplot3d, gclus, hexbin, IDPmisc, Hmisc,    #
#     corrgram, vcd, rgl, and Rcmdr packages have been installed          #
# install.packages(c("car", "scatterplot3d", "gclus", "hexbin",           #  
#      "IDPmisc", "Hmisc", "corrgram", "vcd", "rgl", "Rcmdr"))            #
#-------------------------------------------------------------------------#

# pause on each graph
par(ask=TRUE)

# save original graphical parameter settings
opar <- par(no.readonly=TRUE) # record current settings

# Listing 11.1 - 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)   

# enhanced scatterplot
# identify points interactively with the mouse

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")])

# Listing 11-2 Scatter plot matrix produced with the gclus package

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)

# Listing 11.3 - 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)

# Listing 11.4 -  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)

# Figure 11.18
# 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)
}