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Ch23 Bonus - Advanced graphics with the lattice package.R
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Ch23 Bonus - Advanced graphics with the lattice package.R
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#-----------------------------------------------------------#
# R in Action (2nd ed): Chapter 23 - Bonus online chapter #
# Advanced graphics with the lattice package #
#-----------------------------------------------------------#
par(ask=TRUE)
## Lattice Package
library(lattice)
# Histogram of heights conditioned on voice pitch
histogram(~ height | voice.part, data = singer,
main="Distribution of Heights by Voice Pitch",
xlab="Height (inches)")
# Listing 23.1 - Lattice plot examples
data(mtcars)
attach(mtcars)
# Create factors with value labels
gear <- factor(gear, levels=c(3, 4, 5),
labels=c("3 gears", "4 gears", "5 gears"))
cyl <- factor(cyl, levels=c(4, 6, 8),
labels=c("4 cylinders", "6 cylinders", "8 cylinders"))
# Generate plots
densityplot(~ mpg,
main="Density Plot",
xlab="Miles per Gallon")
densityplot(~ mpg | cyl,
main="Density Plot by Number of Cylinders",
xlab="Miles per Gallon")
bwplot(cyl ~ mpg | gear,
main="Box Plots by Cylinders and Gears",
xlab="Miles per Gallon", ylab="Cylinders")
xyplot(mpg ~ wt | cyl * gear,
main="Scatter Plots by Cylinders and Gears",
xlab="Car Weight", ylab="Miles per Gallon")
cloud(mpg ~ wt * qsec | cyl,
main="3D Scatter Plots by Cylinders")
dotplot(cyl ~ mpg | gear,
main="Dot Plots by Number of Gears and Cylinders",
xlab="Miles Per Gallon")
splom(mtcars[c(1, 3, 4, 5, 6)],
main="Scatter Plot Matrix for mtcars Data")
detach(mtcars)
# Manipulating a graph
mygraph <- densityplot(~height|voice.part, data=singer)
plot(mygraph)
update(mygraph, col="red", pch=16, cex=.8, jitter=.05, lwd=2)
plot(mygraph)
# Conditioning on a continuous variable
displacement <- equal.count(mtcars$disp, number=3, overlap=0)
xyplot(mpg ~ wt | displacement, data=mtcars,
main = "Miles per Gallon vs. Weight by Engine Displacement",
xlab = "Weight", ylab = "Mile per Gallon",
layout=c(3,1), aspect=1.5)
# Listing 23.2 - xyplot with custom panel functions
displacement <- equal.count(mtcars$disp, number=3, overlap=0)
mypanel <- function(x, y) {
panel.xyplot(x, y, pch=19)
panel.rug(x, y)
panel.grid(h=-1, v=-1)
panel.lmline(x, y, col="red", lwd=1, lty=2)
}
xyplot(mpg ~ wt|displacement, data=mtcars,
layout=c(3, 1),
aspect=1.5,
main = "Miles per Gallon vs. Weight by Engine Displacement",
xlab = "Weight",
ylab = "Mile per Gallon",
panel = mypanel)
# Listing 23.3 - xyplot with custom panel functions and additional options
mtcars$transmission <- factor(mtcars$am, levels=c(0, 1),
labels=c("Automatic", "Manual"))
panel.smoother <- function(x, y) {
panel.grid(h=-1, v=-1)
panel.xyplot(x, y)
panel.loess(x, y)
panel.abline(h=mean(y), lwd=2, lty=2, col="green")
}
xyplot(mpg ~ disp|transmission, data=mtcars,
scales=list(cex=.8, col="red"),
panel=panel.smoother,
xlab="Displacement", ylab="Miles per Gallon",
main="MGP vs Displacement by Transmission Type",
sub = "Dotted lines are Group Means", aspect=1)
# Grouping variables
mtcars$transmission <- factor(mtcars$am, levels=c(0, 1),
labels=c("Automatic", "Manual"))
densityplot(~ mpg, data=mtcars,
group=transmission,
main="MPG Distribution by Transmission Type",
xlab="Miles per Gallon",
auto.key=TRUE)
# Listing 23.4 - Kernel density plot with a group variable and customized legend
mtcars$transmission <- factor(mtcars$am, levels=c(0,1),
labels=c("Automatic", "Manual"))
colors = c("red", "blue")
lines = c(1, 2)
points = c(16, 17)
key.trans <- list(title="Trasmission",
space="bottom", columns=2,
text=list(levels(mtcars$transmission)),
points=list(pch=points, col=colors),
lines=list(col=colors, lty=lines),
cex.title=1, cex=.9)
densityplot(~ mpg, data=mtcars,
group=transmission,
main="MPG Distribution by Transmission Type",
xlab="Miles per Gallon",
pch=points, lty=lines, col=colors,
lwd=2, jitter=.005,
key=key.trans)
# Listing 23.5 - xyplot with group and conditioning variables and customized legend
colors <- "darkgreen"
symbols <- c(1:12)
linetype <- c(1:3)
key.species <- list(title="Plant",
space="right",
text=list(levels(CO2$Plant)),
points=list(pch=symbols, col=colors))
xyplot(uptake ~ conc | Type*Treatment, data=CO2,
group=Plant,
type="o",
pch=symbols, col=colors, lty=linetype,
main="Carbon Dioxide Uptake\nin Grass Plants",
ylab=expression(paste("Uptake ",
bgroup("(", italic(frac("umol","m"^2)), ")"))),
xlab=expression(paste("Concentration ",
bgroup("(", italic(frac(mL,L)), ")"))),
sub = "Grass Species: Echinochloa crus-galli",
key=key.species)
# Graphical parameters
show.settings()
mysettings <- trellis.par.get()
mysettings$superpose.symbol
mysettings$superpose.symbol$pch <- c(1:10)
trellis.par.set(mysettings)
show.settings()
# Customizing plot strips
histogram(~height | voice.part, data = singer,
strip = strip.custom(bg="lightgrey",
par.strip.text=list(col="black", cex=.8, font=3)),
main="Distribution of Heights by Voice Pitch",
xlab="Height (inches)")
mysettings <- trellis.par.get()
mysettings$strip.background <- c("lightgrey", "lightgreen")
trellis.par.set(mysettings)
# Page arrangement
graph1 <- histogram(~ height | voice.part, data=singer,
main="Heights of Choral Singers by Voice Part")
graph2 <- bwplot(height~voice.part, data=singer)
plot(graph1, split=c(1, 1, 1, 2))
plot(graph2, split=c(1, 2, 1, 2), newpage=FALSE)
library(lattice)
graph1 <- histogram(~ height | voice.part, data=singer,
main="Heights of Choral Singers by Voice Part")
graph2 <- bwplot(height~voice.part, data=singer)
plot(graph1, position=c(0, .3, 1, 1))
plot(graph2, position=c(0, 0, 1, .3), newpage=FALSE)