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# Remove most objects from the working environment
rm(list = ls())
options(stringsAsFactors = F)
# 23.1. The lattice package
library(lattice)
histogram(~height | voice.part, data = singer,
main = "Distribution of Heights by Voice Pitch",
xlab = "Height (inches)")
# code listing 23.1. Lattice plot examples
library(lattice)
attach(mtcars)
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"))
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)
# 23.2. Conditioning variables
# figure 23-2
displacement <- equal.count(mtcars$disp, number=3, overlap=0)
xyplot(mpg~wt|displacement, data=mtcars,
main = "Miles per Gallon vs. Weight by Engine Displacement",
xlab = "Weight", ylab = "Miles per Gallon",
layout=c(3, 1), aspect=1.5)
# 23.3. Panel functions
# code listing 23.2. xyplot with custom panel function
# figure 23.3
library(lattice)
displacement <- equal.count(mtcars$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 = "Miles per Gallon",
panel = mypanel)
# code listing 23.3. xyplot with a custom panel function and additional options
# figure 23.4
library(lattice)
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 = "darkgreen")
}
xyplot(mpg~disp | transmission, data = mtcars,
scales = list(cex=.8, col="red"),
panel = panel.smoother,
xlab = "Displacement", ylab = "Miles per Gallon",
main = "MPG vs Displacement by Transmission Type",
sub = "Dotted lines are Group Means", aspect = 1)
#-----------------------------------------------------------
# Subsetting Data ｜
## Selection using the Subset Function ｜
sub <- subset(mtcars, transmission == "Manual") #｜
mean(sub$mpg) #｜
# ｜
# Selecting Observations ｜
mean(mtcars[which(mtcars$transmission=='Manual'),]$mpg) #｜
# [1] 24.39231 #｜
mean(mtcars[which(mtcars$transmission=='Automatic'),]$mpg)#｜
# [1] 17.14737 ｜
#----------------------------------------------------------｜
# 23.4. Grouping variables
# figure 23.5
library(lattice)
mtcars$transmission <- factor(mtcars$am, levels = c(0, 1),
labels = c("Automatic", "Manual"))
densityplot(~mpg, data = mtcars,
group = transmission,
main = "MPG Distribution by Transmission Type",
xlab = "Miles per Gallon",
auto.key = TRUE)
# code listing 23.4. Kernel-density plot with a group variable and customized legend
# figure 23.6
library(lattice)
mtcars$transmission <- factor(mtcars$am, levels = c(0, 1),
labels = c("Automatic", "Manual"))
# Color, line and point specifications
colors <- c("red", "blue")
lines <- c(1, 2)
points <- c(16, 17)
# Legend customization
key.trans <- list(title="Transmission",
space="bottom", columns=2,
text=list(levels(mtcars$transmission)),
points=list(pch=points, col=colors),
lines=list(col=colors, lty=lines),
cex.title=1, cex=.9)
# Density plot
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)
# code listing 23.5. xyplot with group and conditioning variables and customized legend
# figure 23.7
head(CO2)
library(lattice)
colors <- "darkgreen"
symbols <- c(1:12)
linetype <- c(1:3)
key.species <- list(title="Plant",
space="right",
text=list(levels(CO2$Plant)),
points=list(pch=symbols, col=colors))
xyplot(uptake ~ conc | Type*Treatment, data = CO2,
group = Plant,
type="o",
pch=symbols, col=colors, lty=linetype,
main="Carbon Dioxide Uptake\nin Grass Plants",
ylab = expression(paste("Uptake ",
bgroup("(", italic(frac("umol", "m"^2)), ")"))),
xlab = expression(paste("Concentration ",
bgroup("(", italic(frac(mL, L)), ")"))),
sub = "Grass Species: Eninochloa crus-galli",
key=key.species)
# 23.5. Graphic parameters
show.settings() # view the current defaults
mysettings <- trellis.par.get() # save them into a list called mysettings
names(mysettings)
# [1] "grid.pars" "fontsize" "background" "panel.background"
# [5] "clip" "add.line" "add.text" "plot.polygon"
# [9] "box.dot" "box.rectangle" "box.umbrella" "dot.line"
# [13] "dot.symbol" "plot.line" "plot.symbol" "reference.line"
# [17] "strip.background" "strip.shingle" "strip.border" "superpose.line"
# [21] "superpose.symbol" "superpose.polygon" "regions" "shade.colors"
# [25] "axis.line" "axis.text" "axis.components" "layout.heights"
# [29] "layout.widths" "box.3d" "par.xlab.text" "par.ylab.text"
# [33] "par.zlab.text" "par.main.text" "par.sub.text"
mysettings$superpose.symbol
# $alpha
# [1] 1 1 1 1 1 1 1
#
# $cex
# [1] 0.8 0.8 0.8 0.8 0.8 0.8 0.8
#
# $col
# [1] "#0080ff" "#ff00ff" "darkgreen" "#ff0000" "orange" "#00ff00" "brown"
#
# $fill
# [1] "#CCFFFF" "#FFCCFF" "#CCFFCC" "#FFE5CC" "#CCE6FF" "#FFFFCC" "#FFCCCC"
#
# $font
# [1] 1 1 1 1 1 1 1
#
# $pch
# [1] 1 1 1 1 1 1 1
# To change the default:
mysettings$superpose.symbol$pch <- c(1:10)
trellis.par.set(mysettings)
show.settings()
# 23.6. Customizing plot strips
library(lattice)
histogram(~height | voice.part, data = singer,
strip = strip.custom(bg="lightgrey",
par.strip.text=list(col="black", cex=.8, font=3)),
main = "Distribution of Heights by Voice Pitch",
xlab = "Height (inches)")
# 23.7. Page arrangement
# figure 23.9
library(lattice)
graph1 <- histogram(~height | voice.part, data = singer,
main = "Heights of Choral Singers by Voice Part")
graph2 <- bwplot(height ~ voice.part, data = singer)
plot(graph1, split = c(1, 1, 1, 2))
plot(graph2, split = c(1, 2, 1, 2), newpage = FALSE)
# figure 23.10
library(lattice)
graph1 <- histogram(~height | voice.part, data = singer,
main = "Heights of Choral Singers by Voice Part")
graph2 <- bwplot(height ~ voice.part, data = singer)
plot(graph1, position = c(0, .33, 1, 1))
plot(graph2, position = c(0, 0, 1, .4), newpage = FALSE)
levels(singer$voice.part)
# [1] "Bass 2" "Bass 1" "Tenor 2" "Tenor 1" "Alto 2" "Alto 1" "Soprano 2"
# [8] "Soprano 1"
histogram(~height | voice.part, data = singer,
index.cond=list(c(2,4,6,8,1,3,5,7)))
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