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# Remove most objects from the working environment
rm(list = ls())
options(stringsAsFactors = F)
# 6.1. Bar plots
library(vcd)
head(Arthritis)
dim(Arthritis)
# 6.1.1. Simple bar plots
counts <- table(Arthritis$Improved)
counts
# code listing 6.1
barplot(counts,
main = "Simple Bar Plot",
xlab = "Improvement",
ylab = "Frequency")
barplot(counts,
main = "Horizontal Bar Plot",
ylab = "Improvement",
xlab = "Frequency",
horiz = T)
# 6.1.2. Stacked and grouped bar plots
counts <- table(Arthritis$Improved, Arthritis$Treatment)
counts
# code listing 6.2
barplot(counts,
main = "Stacked Bar Plot",
xlab = "Treatment",
ylab = "Frequency",
col = c("red", "yellow", "green"),
legend=rownames(counts))
barplot(counts,
main = "Grouped Bar Plot",
xlab = "Treatment",
ylab = "Frequency",
col = c("red", "yellow", "green"),
legend=rownames(counts), beside = T)
# 6.1.3. Mean bar plots
# code listing 6.3
states <- data.frame(state.region, state.x77)
head(states)
means <- aggregate(states$Illiteracy, by=list(state.region), FUN=mean)
means
means <- means[order(means$x),]
barplot(means$x, names.arg = means$Group.1, title("Mean Illiteracy Rate"))
# 6.1.4. Tweaking bar plots
# code listing 6.4
par(mar=c(5,8,4,2))
par(las=2)
counts <- table(Arthritis$Improved)
barplot(counts,
main="Treatment Outcome",
horiz=TRUE, cex.names=0.8,
names.arg=c("No Improvement", "Some Improvement",
"Marked Improvement"))
# 6.1.5. Spinograms spine()
library(vcd)
attach(Arthritis)
counts <- table(Treatment, Improved)
spine(counts, main="Spinogram Example")
detach(Arthritis)
# 6.2. Pie charts
pie(x,labels) # pie charts
fan.plot() # fan plot, a variation of the pie chart
# code Listing 6.5. Pie charts
par(mfrow=c(2,2))
slices <- c(10, 12, 4, 16, 8)
lbls <- c("US", "UK", "Australia", "Germany", "France")
pie(slices, labels = lbls,
main = "Simple Pie Chart")
pct <- round(slices/sum(slices)*100)
lbls2 <- paste(lbls, " ", pct, "%", sep = "")
pie(slices, labels = lbls2, col = rainbow(length(lbls2)),
main = "Pie Chart with Percentages")
library(plotrix)
pie3D(slices, labels=lbls, explode=0.1,
main="3D Pie Chart")
mytable <- table(state.region)
lbls3 <- paste(names(mytable), "\n", mytable, sep = "")
pie(mytable, labels = lbls3,
main = "Pie Chart from a Table\n (with sample sizes)")
# 6.3. Histograms
# code listing 6.6. Histograms
par(mfrow=c(2,2))
hist(mtcars$mpg)
hist(mtcars$mpg,
breaks = 12,
col = "red",
xlab = "Miles Per Gallon",
main = "Colored histogram with 12 bins")
hist(mtcars$mpg,
freq = F,
breaks = 12,
col = "red",
xlab = "Miles Per Gallon",
main = "Histogram, rug plot, density curve")
# rug plot
rug(jitter(mtcars$mpg)) # a one-dimensional representation of the actual data values
lines(density(mtcars$mpg), col="blue", lwd=2)
x <- mtcars$mpg
h <- hist(x,
breaks = 12,
col = "red",
xlab = "Miles Per Gallon",
main = "Histogram with normal curve and box")
xfit <- seq(min(x), max(x), length=40)
yfit <- dnorm(xfit, mean = mean(x), sd=sd(x))
yfit <- yfit*diff(h$mids[1:2])*length(x)
lines(xfit, yfit, col="blue", lwd=2)
box() # produce surrounding box
# 6.4. Kernel density plots
plot(density(x))
# if overlap with an existing graph
lines()
# code listing 6.7. Kernel density plots
par(mfrow=c(2, 2))
d <- density(mtcars$mpg)
plot(d)
d <- density(mtcars$mpg)
plot(d, main = "Kernel Density of Miles Per Gallon")
polygon(d, col = "red", border = "blue")
rug(mtcars$mpg, col = "brown")
# code listing 6.8. Comparative kernel density plots
par(lwd=2)
library(sm)
attach(mtcars)
cyl.f <- factor(cyl, levels = c(4, 6, 8),
labels = c("4 cylinder", "6 cylinder",
"8 cylinder"))
sm.density.compare(mpg, cyl, xlab="Miles Per Gallon")
title(main = "MPG Distribution by Car Cylinders")
colfill <- c(2:(1+length(levels(cyl.f))))
# locator(1), indicates that you’ll place the legend interactively
# by clicking on the graph where you want the legend to appear.
legend(locator(1), levels(cyl.f), fill = colfill)
detach(mtcars)
# 6.5. Box plots
boxplot.stats(mtcars$mpg)
## 6.5.1. Using parallel box plots to compare groups
table(mtcars$cyl)
table(mtcars$mpg)
summary(mtcars$mpg)
table(mtcars$mpg, mtcars$cyl)
table(mtcars$mpg, mtcars$cyl)
# code listing 6.9. Box plots for two crossed factors
mtcars$cyl.f <- factor(mtcars$cyl,
levels = c(4, 6, 8),
labels = c("4", "6", "8"))
mtcars$am.f <- factor(mtcars$am,
levels = c(0, 1),
labels = c("auto", "standard"))
boxplot(mpg ~ am.f * cyl.f,
data = mtcars,
varwidth=T,
col=c("gold", "darkgreen"),
main="MPG Distribution by Auto Type",
xlab = "Auto Type")
# 6.5.2. Violin plots
# A violin plot is a combination of a box plot and a kernel density plot.
vioplot(x1, x2, ..., names=, col=)
# code listing 6.10. Violin plots
library(vioplot)
x1 <- mtcars$mpg[mtcars$cyl == 4]
x2 <- mtcars$mpg[mtcars$cyl == 6]
x3 <- mtcars$mpg[mtcars$cyl == 8]
vioplot(x1, x2, x3,
names=c("4 cyl", "6 cyl", "8 cyl"),
col="gold")
title("Violin Plots of Miles Per Gallon")
# 6.6. Dot plots
# code listing 6.11.Dot plot grouped, sorted, and colored
x <- mtcars[order(mtcars$mpg),]
x$cyl <- factor(x$cyl)
x$color[x$cyl==4] <- "red"
x$color[x$cyl==6] <- "blue"
x$color[x$cyl==8] <- "darkgreen"
dotchart(x$mpg,
labels = row.names(x),
cex=.7,
groups = x$cyl,
gcolor = "black",
color = x$color,
pch=19,
main = "Gas Mileage for Car Models\ngrouped by cylinder",
xlab = "Miles Per Gallon")
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