par(ask=TRUE)
opar <- par(no.readonly=TRUE) # record current settings
# Listing 11.1 - A scatter plot with best fit lines
attach(mtcars)
plot(wt, mpg,
main="Basic Scatterplot of MPG vs. Weight",
xlab="Car Weight (lbs/1000)",
ylab="Miles Per Gallon ", pch=19)
abline(lm(mpg ~ wt), col="red", lwd=2, lty=1)
lines(lowess(wt, mpg), col="blue", lwd=2, lty=2)
detach(mtcars)
# Scatter plot with fit lines by group
library(car)
scatterplot(mpg ~ wt | cyl, data=mtcars, lwd=2,
main="Scatter Plot of MPG vs. Weight by # Cylinders",
xlab="Weight of Car (lbs/1000)",
ylab="Miles Per Gallon", id.method="identify",
legend.plot=TRUE, labels=row.names(mtcars),
boxplots="xy")
# Scatter-plot matrices
pairs(~ mpg + disp + drat + wt, data=mtcars,
main="Basic Scatterplot Matrix")
library(car)
library(car)
scatterplotMatrix(~ mpg + disp + drat + wt, data=mtcars,
spread=FALSE, smoother.args=list(lty=2),
main="Scatter Plot Matrix via car Package")
# high density scatterplots
set.seed(1234)
n <- 10000
c1 <- matrix(rnorm(n, mean=0, sd=.5), ncol=2)
c2 <- matrix(rnorm(n, mean=3, sd=2), ncol=2)
mydata <- rbind(c1, c2)
mydata <- as.data.frame(mydata)
names(mydata) <- c("x", "y")
with(mydata,
plot(x, y, pch=19, main="Scatter Plot with 10000 Observations"))
with(mydata,
smoothScatter(x, y, main="Scatter Plot colored by Smoothed Densities"))
library(hexbin)
with(mydata, {
bin <- hexbin(x, y, xbins=50)
plot(bin, main="Hexagonal Binning with 10,000 Observations")
})
# 3-D Scatterplots
library(scatterplot3d)
attach(mtcars)
scatterplot3d(wt, disp, mpg,
main="Basic 3D Scatter Plot")
scatterplot3d(wt, disp, mpg,
pch=16,
highlight.3d=TRUE,
type="h",
main="3D Scatter Plot with Vertical Lines")
s3d <-scatterplot3d(wt, disp, mpg,
pch=16,
highlight.3d=TRUE,
type="h",
main="3D Scatter Plot with Vertical Lines and Regression Plane")
fit <- lm(mpg ~ wt+disp)
s3d$plane3d(fit)
detach(mtcars)
# spinning 3D plot
library(rgl)
attach(mtcars)
plot3d(wt, disp, mpg, col="red", size=5)
# alternative
library(car)
with(mtcars,
scatter3d(wt, disp, mpg))
# bubble plots
attach(mtcars)
## The following objects are masked from mtcars (pos = 3):
##
## am, carb, cyl, disp, drat, gear, hp, mpg, qsec, vs, wt
r <- sqrt(disp/pi)
symbols(wt, mpg, circle=r, inches=0.30,
fg="white", bg="lightblue",
main="Bubble Plot with point size proportional to displacement",
ylab="Miles Per Gallon",
xlab="Weight of Car (lbs/1000)")
text(wt, mpg, rownames(mtcars), cex=0.6)
detach(mtcars)
# Listing 11.2 - Creating side by side scatter and line plots
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,2))
t1 <- subset(Orange, Tree==1)
plot(t1$age, t1$circumference,
xlab="Age (days)",
ylab="Circumference (mm)",
main="Orange Tree 1 Growth")
plot(t1$age, t1$circumference,
xlab="Age (days)",
ylab="Circumference (mm)",
main="Orange Tree 1 Growth",
type="b")
par(opar)
# Listing 11.3 - Line chart displaying the growth of 5 Orange trees over time
Orange$Tree <- as.numeric(Orange$Tree)
ntrees <- max(Orange$Tree)
xrange <- range(Orange$age)
yrange <- range(Orange$circumference)
plot(xrange, yrange,
type="n",
xlab="Age (days)",
ylab="Circumference (mm)"
)
colors <- rainbow(ntrees)
linetype <- c(1:ntrees)
plotchar <- seq(18, 18+ntrees, 1)
for (i in 1:ntrees) {
tree <- subset(Orange, Tree==i)
lines(tree$age, tree$circumference,
type="b",
lwd=2,
lty=linetype[i],
col=colors[i],
pch=plotchar[i]
)
}
title("Tree Growth", "example of line plot")
legend(xrange[1], yrange[2],
1:ntrees,
cex=0.8,
col=colors,
pch=plotchar,
lty=linetype,
title="Tree"
)
# Correlograms
options(digits=2)
cor(mtcars)
## mpg cyl disp hp drat wt qsec vs am gear carb
## mpg 1.00 -0.85 -0.85 -0.78 0.681 -0.87 0.419 0.66 0.600 0.48 -0.551
## cyl -0.85 1.00 0.90 0.83 -0.700 0.78 -0.591 -0.81 -0.523 -0.49 0.527
## disp -0.85 0.90 1.00 0.79 -0.710 0.89 -0.434 -0.71 -0.591 -0.56 0.395
## hp -0.78 0.83 0.79 1.00 -0.449 0.66 -0.708 -0.72 -0.243 -0.13 0.750
## drat 0.68 -0.70 -0.71 -0.45 1.000 -0.71 0.091 0.44 0.713 0.70 -0.091
## wt -0.87 0.78 0.89 0.66 -0.712 1.00 -0.175 -0.55 -0.692 -0.58 0.428
## qsec 0.42 -0.59 -0.43 -0.71 0.091 -0.17 1.000 0.74 -0.230 -0.21 -0.656
## vs 0.66 -0.81 -0.71 -0.72 0.440 -0.55 0.745 1.00 0.168 0.21 -0.570
## am 0.60 -0.52 -0.59 -0.24 0.713 -0.69 -0.230 0.17 1.000 0.79 0.058
## gear 0.48 -0.49 -0.56 -0.13 0.700 -0.58 -0.213 0.21 0.794 1.00 0.274
## carb -0.55 0.53 0.39 0.75 -0.091 0.43 -0.656 -0.57 0.058 0.27 1.000
# install.packages("corrgram")
library(corrgram)
corrgram(mtcars, order=TRUE, lower.panel=panel.shade,
upper.panel=panel.pie, text.panel=panel.txt,
main="Corrgram of mtcars intercorrelations")
corrgram(mtcars, order=TRUE, lower.panel=panel.ellipse,
upper.panel=panel.pts, text.panel=panel.txt,
diag.panel=panel.minmax,
main="Corrgram of mtcars data using scatter plots
and ellipses")
cols <- colorRampPalette(c("darkgoldenrod4", "burlywood1",
"darkkhaki", "darkgreen"))
corrgram(mtcars, order=TRUE, col.regions=cols,
lower.panel=panel.shade,
upper.panel=panel.conf, text.panel=panel.txt,
main="A Corrgram (or Horse) of a Different Color")
# Mosaic Plots
ftable(Titanic)
## Survived No Yes
## Class Sex Age
## 1st Male Child 0 5
## Adult 118 57
## Female Child 0 1
## Adult 4 140
## 2nd Male Child 0 11
## Adult 154 14
## Female Child 0 13
## Adult 13 80
## 3rd Male Child 35 13
## Adult 387 75
## Female Child 17 14
## Adult 89 76
## Crew Male Child 0 0
## Adult 670 192
## Female Child 0 0
## Adult 3 20
library(vcd)
## Loading required package: grid
mosaic(Titanic, shade=TRUE, legend=TRUE)
library(vcd)
mosaic(~Class+Sex+Age+Survived, data=Titanic, shade=TRUE, legend=TRUE)
# type= options in the plot() and lines() functions
x <- c(1:5)
y <- c(1:5)
par(mfrow=c(2,4))
types <- c("p", "l", "o", "b", "c", "s", "S", "h")
for (i in types){
plottitle <- paste("type=", i)
plot(x,y,type=i, col="red", lwd=2, cex=1, main=plottitle)
}
