## 3. Plotting

demo(graphics)
## 3.1 plot () and allied functions
plot(y ~ x)  # Use a formula to specify the graph
plot(x, y)   # 
plot((0:20)*pi/10, sin((0:20)*pi/10))
plot((1:30)*0.92, sin((1:30)*0.92))
attach(elasticband)   # R now knows where to find distance & stretch 
plot(distance ~ stretch) 
plot(ACT ~ Year, data=austpop, type="l")
plot(ACT ~ Year, data=austpop, type="b")
attach(austpop)
plot(spline(Year, ACT), type="l")  # Fit smooth curve through points
detach(austpop)   # In S-PLUS, specify detach("austpop")
## 3.1.1 Plot methods for other classes of object
plot(hills)  # Has the same effect as pairs(hills)

## 3.2 Fine control - Parameter settings
par(cex=1.25)   # character expansion
oldpar <- par(cex=1.25, mex=1.25)  # mex=1.25 expands the margin by 25%
attach(elasticband)  
oldpar <- par(cex=1.5)
plot(distance ~ stretch) 
par(oldpar)         # Restores the earlier settings
detach(elasticband) 
oldpar <- par(cex=1.25)
on.exit(par(oldpar))
## 3.2.1 Multiple plots on the one page
par(mfrow=c(2,2), pch=16)
attach(Animals)   # This dataset is in the MASS package, which must be attached
plot(body, brain)
plot(sqrt(body), sqrt(brain))
plot((body)^0.1, (brain)^0.1)
plot(log(body),log(brain))
detach(Animals)
par(mfrow=c(1,1), pch=1)      # Restore to 1 figure per page
## 3.2.2 The shape of the graph sheet

## 3.3 Adding points, lines and text
primates
attach(primates)  # Needed if primates is not already attached.
plot(Bodywt, Brainwt)
text(x=Bodywt, y=Brainwt, labels=row.names(primates), pos=4) 
 # pos=4 positions text to the right of the point
plot(x=Bodywt, y=Brainwt, pch=16, 
     xlab="Body weight (kg)", ylab="Brain weight (g)", 
     xlim=c(0,280), ylim=c(0,1350))
# Specify xlim so that there is room for the labels
text(x=Bodywt, y=Brainwt, labels=row.names(primates), pos=4)
detach(primates)
text(x=Bodywt, y=Brainwt, labels=row.names(primates), pos=2)
## 3.3.1 Size, colour and choice of plotting symbol
plot(1, 1, xlim=c(1, 7.5), ylim=c(1.75,5), type="n", axes=F, xlab="",
       ylab="")   # Do not plot points
box()
points(1:7, rep(4.5, 7), cex=1:7, col=1:7, pch=0:6)
text(1:7,rep(3.5, 7), labels=paste(0:6), cex=1:7, col=1:7)
points(1:7,rep(2.5,7), pch=(0:6)+7)		# Plot symbols 7 to 13
text((1:7), rep(2.5,7), paste((0:6)+7), pos=4)  # Label with symbol number
points(1:7,rep(2,7), pch=(0:6)+14)		# Plot symbols 14 to 20
text((1:7), rep(2,7), paste((0:6)+14), pos=4) # Labels with symbol number
view.colours <- function(){
	plot(1, 1, xlim=c(0,14), ylim=c(0,3), type="n", axes=F, 
	     xlab="",ylab="")
	text(1:6, rep(2.5,6), paste(1:6), col=palette()[1:6], cex=2.5)
	text(10, 2.5, "Default palette", adj=0)
	rainchars <- c("R","O","Y","G","B","I","V")
	text(1:7, rep(1.5,7), rainchars, col=rainbow(7), cex=2.5)
	text(10, 1.5, "rainbow(7)", adj=0)
	cmtxt <- substring("cm.colors", 1:9,1:9)  
	   # Split "cm.colors" into its 9 characters
	text(1:9, rep(0.5,9), cmtxt, col=cm.colors(9), cex=3)
	text(10, 0.5, "cm.colors(9)", adj=0)
}
view.colours()
## 3.3.2 Adding Text in the Margin

## 3.4 Identification and Location on the Figure Region
## 3.4.1 identify()
attach(florida)
plot(BUSH, BUCHANAN, xlab="Bush", ylab="Buchanan")
identify(BUSH, BUCHANAN, County)
detach(florida)
## 3.4.2 locator()
attach(florida)  # if not already attached
plot(BUSH, BUCHANAN, xlab="Bush", ylab="Buchanan")
locator()
detach(florida)

## 3.5 Plots that show the distribution of data values
## 3.5.1 Histograms and density plots
attach(possum)
here <- sex == "f"
hist(totlngth[here], breaks = 72.5 + (0:5) * 5, ylim = c(0, 22),
        xlab="Total length", main ="A: Breaks at 72.5, 77.5, ...")
detach(possum)
attach(possum)
plot(density(totlngth[here]),type="l")
detach(possum)
attach(possum)
here <- sex == "f"
dens <- density(totlngth[here])
xlim <- range(dens$x)
ylim <- range(dens$y)
hist(totlngth[here], breaks = 72.5 + (0:5) * 5, probability = TRUE,
   xlim = xlim, ylim = ylim, xlab="Total length", main="")
lines(dens)
detach(possum)
## 3.5.3 Boxplots
attach(possum)
boxplot(totlngth[here], horizontal=TRUE)
rug(totlngth[here], side=1)
detach(possum)
## 3.5.4 Normal probability plots
x11(width=8, height=6)  # This is a better shape for this plot
attach(possum)
here <- sex == "f"
par(mfrow=c(2,4))             # A 2 by 4 layout of plots
y <- totlngth[here]
qqnorm(y,xlab="", ylab="Length", main="Possums")  
for(i in 1:7)qqnorm(rnorm(43),xlab="", ylab="Simulated lengths",
                   main="Simulated")
detach(possum)
# Before continuing, type dev.off() 

## 3.6 Other Useful Plotting Functions
## 3.6.1 Scatterplot smoothing
attach(ais)
here<- sex=="f"
plot(pcBfat[here]~ht[here], xlab = "Height", ylab = "% Body fat")
panel.smooth(ht[here],pcBfat[here])
detach(ais)
## 3.6.2 Adding lines to plots
attach(ais)
here<- sex=="f"
plot(pcBfat[here] ~ ht[here], xlab = "Height", ylab = "% Body fat")
abline(lm(pcBfat[here] ~ ht[here]))
detach(ais)
## 3.6.3 Rugplots
attach(ais)
here <- sex == "f"
oldpar <- par(mar=c(4.1,1.1,1.1, 1.1), mgp=c(2.25, 0.75,0))
boxplot(ht[here], boxwex = 0.35, ylab = "Height", horizontal=TRUE)
rug(ht[here], side = 1)
par(oldpar)
detach(ais)
## 3.6.4 Scatterplot matrices
## 3.6.5 Dotcharts
dotchart(islands)  
# vector of named numeric values, in the datasets base package
dotchart(islands, cex=0.5)

## 3.7 Plotting Mathematical Symbols 
##x <- 1:15
plot(x, y, xlab="Radius", ylab=expression(Area == pi*r^2))
demo(graphics)

## 3.8 Guidelines for Graphs
3.9 Exercises
3.10 References


4. Lattice graphics
## 4.1 Examples that Present Panels of Scatterplots - Using xyplot()

xyplot(csoa ~ it | sex * agegp, data=tinting) # Simple use of xyplot()
xyplot(csoa~it|sex*agegp, data=tinting, 
	 groups=target, auto.key=list(columns=2))
xyplot(csoa~it|sex*agegp, data=tinting, panel=panel.superpose, 
	 groups=target, type=c("p","smooth")
xyplot(csoa~it|sex*agegp, data=tinting, groups=tint,
	 auto.key=list(columns=3))

## 4.2 Some further examples of lattice plots
## 4.2.1 Plotting columns in parallel
library(DAAGxtras)
xyplot(Beer+Spirit+Wine ~ Year | Country, outer=TRUE,  
      data=grog) 
xyplot(Beer+Spirit+Wine ~ Year, groups=Country, outer=TRUE,  
      data=grog) 
xyplot(Beer+Spirit+Wine ~ Year | Country, outer=FALSE,  
      data=grog, auto.key=list(columns=3), 
      par.settings=simpleTheme(pch=16, cex=2) )
## 4.2.2 Fixed, sliced and free scales
library(DAAG) 
## scale="fixed" 
xyplot(BC+Alberta ~ Date, data=jobs, outer=TRUE) 
## scale="sliced" - different slices of same scale 
xyplot(BC+Alberta ~ Date, data=jobs, outer=TRUE, 
     scales=list(y=list(relation="sliced")) ) 
## scale="free" - independent scales 
xyplot(BC+Alberta ~ Date, data=jobs, outer=TRUE, 
     scales=list(y=list(relation="free")) )

## 4.3 An incomplete list of lattice Functions
splom( ~ data.frame)                	# Scatterplot matrix
bwplot(factor ~ numeric , . .)      	# Box and whisker plot
qqnorm(numeric , . .)			# normal probability plots
dotplot(factor ~ numeric , . .)     	# 1-dim. Display
stripplot(factor ~ numeric , . .)   	# 1-dim. Display
barchart(character ~ numeric , . .)
histogram( ~ numeric , . .)
densityplot( ~ numeric , . .)       	# Smoothed version of histogram
qqmath(numeric ~ numeric , . .) 	       	# QQ plot
splom( ~ dataframe, . .)			# Scatterplot matrix  
parallel( ~ dataframe, . .)		# Parallel coordinate plots
cloud(numeric ~ numeric * numeric, . .) 	# 3-D plot
contourplot(numeric ~ numeric * numeric, . .) 	# Contour plot
levelplot(numeric ~ numeric * numeric, . .) 	# Variation on a contour plot
## 4.4 Exercises