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# Remove most objects from the working environment
rm(list = ls())
options(stringsAsFactors = F)
# 19.2. An introduction to the ggplot2 package
# figure 19.1
library(ggplot2)
ggplot(data = mtcars, aes(x=wt, y=mpg)) +
geom_point() +
labs(title = "Automobile Data", x="Weight", y="Miles Per Gallon")
# figure 19.2
library(ggplot2)
ggplot(data=mtcars, aes(x=wt, y=mpg)) +
geom_point(pch=17, color="blue", size=2) +
geom_smooth(method="lm", color="red", linetype=2) +
labs(title="Automobile Data", x="Weight", y="Miles Per Gallon")
mtcars$am <- factor(mtcars$am, levels=c(0,1),
labels=c("Automatic", "Manual"))
mtcars$vs <- factor(mtcars$vs, levels=c(0,1),
labels=c("V-Engine", "Straight Engine"))
mtcars$cyl <- factor(mtcars$cyl)
# figure 19.3
library(ggplot2)
ggplot(data=mtcars, aes(x=hp, y=mpg,
shape=cyl, color=cyl)) +
geom_point(size=3) +
facet_grid(am~vs) +
labs(title="Automobile Data by Engine Type",
x="Horsepower", y="Miles Per Gallon")
# 19.3. Specifying the plot type with geoms
# figure 19.4
data(singer, package = "lattice")
ggplot(singer, aes(x=height)) + geom_histogram()
# figure 19.5
ggplot(singer, aes(x=voice.part, y=height)) + geom_boxplot()
# figure 19.6
data(Salaries, package="carData")
dim(Salaries)
# [1] 397 6
head(Salaries)
# rank discipline yrs.since.phd yrs.service sex salary
# 1 Prof B 19 18 Male 139750
# 2 Prof B 20 16 Male 173200
# 3 AsstProf B 4 3 Male 79750
# 4 Prof B 45 39 Male 115000
# 5 Prof B 40 41 Male 141500
# 6 AssocProf B 6 6 Male 97000
colnames(Salaries)
# [1] "rank" "discipline" "yrs.since.phd" "yrs.service" "sex"
# [6] "salary"
library(ggplot2)
ggplot(Salaries, aes(x=rank, y=salary)) +
geom_boxplot(fill="cornflowerblue",
color="black", notch=TRUE) +
geom_point(position="jitter", color="red", alpha=.5) +
geom_rug(side="l", color="black")
# figure 19.7
library(ggplot2)
data(singer, package="lattice")
ggplot(singer, aes(x=voice.part, y=height)) +
geom_violin(fill="lightpink") +
geom_boxplot(fill="lightgray", width=.2)
# 19.4. Grouping
# figure 19.8
data(Salaries, package="carData")
library(ggplot2)
ggplot(data=Salaries, aes(x=salary, fill=rank)) +
geom_density(alpha=.3)
# figure 19.9
ggplot(Salaries, aes(x=yrs.since.phd, y=salary, color=rank,
shape=sex)) + geom_point()
# figure 19.10
require(gridExtra)
plot1 <- ggplot(Salaries, aes(x=rank, fill=sex)) +
geom_bar(position="stack") + labs(title='position="stack"')
plot2 <- ggplot(Salaries, aes(x=rank, fill=sex)) +
geom_bar(position="dodge") + labs(title='position="dodge"')
plot3 <- ggplot(Salaries, aes(x=rank, fill=sex)) +
geom_bar(position="fill") + labs(title='position="fill"')
grid.arrange(plot1, plot2, plot3, ncol=3)
# 19.5. Faceting
# figure 19.11
data(singer, package="lattice")
library(ggplot2)
ggplot(data=singer, aes(x=height)) +
geom_histogram() +
facet_wrap(~voice.part, nrow=4)
# figure 19.12
library(ggplot2)
ggplot(Salaries, aes(x=yrs.since.phd, y=salary, color=rank,
shape=rank)) + geom_point() + facet_grid(.~sex)
# figure 19.13
data(singer, package="lattice")
library(ggplot2)
ggplot(data=singer, aes(x=height, fill=voice.part)) +
geom_density() +
facet_grid(voice.part~.)
# 19.6. Adding smoothed lines
# figure 19.14
data(Salaries, package="carData")
library(ggplot2)
ggplot(data=Salaries, aes(x=yrs.since.phd, y=salary)) +
geom_smooth() +
geom_point()
# figure 19.15
ggplot(data=Salaries, aes(x=yrs.since.phd, y=salary,
linetype=sex, shape=sex, color=sex)) +
geom_smooth(method=lm, formula=y~poly(x,2),
se=FALSE, size=1) +
geom_point(size=2)
# 19.7.1. Axes
# figure 19.16
data(Salaries,package="carData")
library(ggplot2)
ggplot(data=Salaries, aes(x=rank, y=salary, fill=sex)) +
geom_boxplot() +
scale_x_discrete(breaks=c("AsstProf", "AssocProf", "Prof"),
labels=c("Assistant\nProfessor",
"Associate\nProfessor",
"Full\nProfessor")) +
scale_y_continuous(breaks=c(50000, 100000, 150000, 200000),
labels=c("$50K", "$100K", "$150K", "$200K")) +
labs(title="Faculty Salary by Rank and Sex", x="", y="")
# 19.7.2. Legends
# figure 19.17
data(Salaries,package="carData")
library(ggplot2)
ggplot(data=Salaries, aes(x=rank, y=salary, fill=sex)) +
geom_boxplot() +
scale_x_discrete(breaks=c("AsstProf", "AssocProf", "Prof"),
labels=c("Assistant\nProfessor",
"Associate\nProfessor",
"Full\nProfessor")) +
scale_y_continuous(breaks=c(50000, 100000, 150000, 200000),
labels=c("$50K", "$100K", "$150K", "$200K")) +
labs(title="Faculty Salary by Rank and Gender",
x="", y="", fill="Gender") +
theme(legend.position=c(.1,.8))
# 19.7.3 Scales
# figure 19.18
ggplot(mtcars, aes(x=wt, y=mpg, size=disp)) +
geom_point(shape=21, color="black", fill="cornsilk") +
labs(x="Weight", y="Miles Per Gallon",
title="Bubble Chart", size="Engine\nDisplacement")
# figure 19.19
data(Salaries, package="carData")
ggplot(data=Salaries, aes(x=yrs.since.phd, y=salary, color=rank)) +
scale_color_manual(values=c("orange", "olivedrab", "navy")) +
geom_point(size=2)
# figure 19.19-2
ggplot(data=Salaries, aes(x=yrs.since.phd, y=salary, color=rank)) +
scale_color_brewer(palette="Set1") +
geom_point(size=2)
library(RColorBrewer)
display.brewer.all()
# 19.7.4. Themes
data(Salaries, package="carData")
library(ggplot2)
mytheme <- theme(plot.title=element_text(face="bold.italic",
size="14", color="brown"),
axis.title=element_text(face="bold.italic",
size=10, color="brown"),
axis.text=element_text(face="bold", size=9,
color="darkblue"),
panel.background=element_rect(fill="white",
color="darkblue"),
panel.grid.major.y=element_line(color="grey",
linetype=1),
panel.grid.minor.y=element_line(color="grey",
linetype=2),
panel.grid.minor.x=element_blank(),
legend.position="top")
ggplot(Salaries, aes(x=rank, y=salary, fill=sex)) +
geom_boxplot() +
labs(title="Salary by Rank and Sex", x="Rank", y="Salary") +
mytheme
# alternative no define of mytheme
ggplot(Salaries, aes(x=rank, y=salary, fill=sex)) +
geom_boxplot() +
labs(title="Salary by Rank and Sex", x="Rank", y="Salary") +
theme(plot.title=element_text(face="bold.italic",
size="14", color="brown"),
axis.title=element_text(face="bold.italic",
size=10, color="brown"),
axis.text=element_text(face="bold", size=9,
color="darkblue"),
panel.background=element_rect(fill="white",
color="darkblue"),
panel.grid.major.y=element_line(color="grey",
linetype=1),
panel.grid.minor.y=element_line(color="grey",
linetype=2),
panel.grid.minor.x=element_blank(),
legend.position="top")
# 19.7.5. Multiple graphs per page
data(Salaries, package="carData")
library(ggplot2)
p1 <- ggplot(data=Salaries, aes(x=rank)) + geom_bar()
p2 <- ggplot(data=Salaries, aes(x=sex)) + geom_bar()
p3 <- ggplot(data=Salaries, aes(x=yrs.since.phd, y=salary)) + geom_point()
library(gridExtra)
grid.arrange(p1, p2, p3, ncol=3)
# Saving graphs
myplot <- ggplot(data=mtcars, aes(x=mpg)) + geom_histogram()
ggsave(file="mygraph.png", plot=myplot, width=5, height=4)
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hugo
