# A variety of common graphics options/parameters
library(faraway)

# Basic scatterplot
plot(math ~ read, hsb)

# Titles, axis labels, tickmarks
plot(math ~ read, hsb, main="Reading and Math")
plot(math ~ read, hsb, ylab="Math Score")
plot(math ~ read, hsb, xaxp=c(25, 75, 10))
# Notice that if you ask for tick marks beyond the plotting area
#    they are silently omitted (no error or warning)

# Jitter
plot(jitter(math) ~ jitter(read), data=hsb, main="Jittered Scores", ylab="Math", xlab="Reading")
# not shown here:  you can specify the amount of jitter

# Marker color and symbol - specified numerically or as a character string
plot(math ~ read, hsb, col="red")
plot(math ~ read, hsb, col=1)
plot(math ~ read, hsb, pch=15)
plot(math ~ read, hsb, pch="a")

# these are vector-valued parameters, so they can be 
#    used to identify groups or create patterns
plot(math ~ read, data=hsb, col=prog, pch=19) 
# here, prog is a vector (factor) coerced to numeric values

# You get more control by explicitly building a factor of color names
dot.color <- ifelse(hsb$prog=="academic", "blue", "red")
dot.color[hsb$prog == "vocation"] <- "black"
plot(math ~ read, data=hsb, col=dot.color, pch=19)
# similarly with symbols, you can build your own vector

# You can also control the default sequence of colors with palette()
palette(c("blue","red","black"))
plot(math ~ read, data=hsb, col=prog, pch=19) 

palette("default")

# Overlaying graphical elements
# You'll want to add a legend to the previous plot
legend("bottomright", legend=levels(hsb$prog), fill=c("blue", "red", "black"))

# overlay horizontal or vertical reference lines
abline(h=mean(hsb$math))

#overlay a regression line
plot(math ~ read, data=hsb, col=dot.color, pch=19)
fit <- lm(math ~ read, data=hsb)
abline(coef(fit))
abline(fit)

# an arbitrary line in intercept-slope form
abline(0, 1, col="red")

# Multiple plots in one device
opar <- par(mfrow=c(2,2))
for (i in 1:3) {
    plot(math ~ read, data=hsb, subset=(prog==levels(prog)[i]), 
         main=levels(prog)[i], xlim=c(30,75), ylim=c(30,75))
    abline(h=mean(hsb$math[hsb$prog==levels(hsb$prog)[i]]))
    abline(v=mean(hsb$read[hsb$prog==levels(hsb$prog)[i]]))
}
par(opar)

library(lattice)
xyplot(math ~ read | prog, data=hsb)
xyplot(math ~ read | prog+gender, data=hsb,
       panel=function(x, y){
           panel.xyplot(x, y)
           panel.lmline(x, y)
       })

library(ggplot2)
qplot(read, math, data=hsb, facets=~prog+gender) + geom_smooth(method="lm")

opar <- par(mfrow=c(2,2))
for (i in 1:3) {
    plot(math ~ read, data=hsb, subset=(prog==levels(prog)[i]), 
         main=levels(prog)[i], xlim=c(30,75), ylim=c(30,75), pch=16)
    fit <- lm(math ~ read, data=hsb, subset=(prog==levels(prog)[i]))
    abline(coef(fit))
}
par(opar)