Introdcution to `ggplot2`¶

In [1]:

library(ggplot2)
library(reshape2)
library(lattice)

Basic plots¶

In [2]:

head(mtcars)

Out[2]:

	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
Mazda RX4	21	6	160	110	3.9	2.62	16.46	0	1	4	4
Mazda RX4 Wag	21	6	160	110	3.9	2.875	17.02	0	1	4	4
Datsun 710	22.8	4	108	93	3.85	2.32	18.61	1	1	4	1
Hornet 4 Drive	21.4	6	258	110	3.08	3.215	19.44	1	0	3	1
Hornet Sportabout	18.7	8	360	175	3.15	3.44	17.02	0	0	3	2
Valiant	18.1	6	225	105	2.76	3.46	20.22	1	0	3	1

In [3]:

plot(mtcars$wt, mtcars$mpg)

In [4]:

ggplot(mtcars, aes(x=wt, y=mpg)) + geom_point()

In [5]:

scatter.smooth(mtcars$wt, mtcars$mpg)

In [6]:

ggplot(mtcars, aes(x=wt, y=mpg)) + geom_point() + geom_smooth(method=loess)

In [7]:

df <- mtcars[order(mtcars$wt),]
plot(df$wt, df$mpg, type="b"))

Error in parse(text = x, srcfile = src): <text>:2:30: unexpected ')'
1: df <- mtcars[order(mtcars$wt),]
2: plot(df$wt, df$mpg, type="b"))
                                ^

In [8]:

ggplot(mtcars, aes(x=wt, y=mpg)) + geom_point() + geom_line()

In [9]:

hist(mtcars$mpg, breaks=10)

In [10]:

ggplot(mtcars, aes(x=mpg)) + geom_histogram(binwidth=2)

In [11]:

plot(density(mtcars$mpg), main="Density plot")

In [12]:

density(mtcars$mpg)

Out[12]:

Call:
    density.default(x = mtcars$mpg)

Data: mtcars$mpg (32 obs.); Bandwidth 'bw' = 2.477

       x               y
 Min.   : 2.97   Min.   :6.481e-05
 1st Qu.:12.56   1st Qu.:5.461e-03
 Median :22.15   Median :1.926e-02
 Mean   :22.15   Mean   :2.604e-02
 3rd Qu.:31.74   3rd Qu.:4.530e-02
 Max.   :41.33   Max.   :6.795e-02

In [13]:

ggplot(mtcars, aes(x=mpg)) +
geom_line(stat="density") +
xlim(2.97, 41.33) +
labs(title="Density plot")

In [14]:

hist(mtcars$mpg, breaks=10, probability = TRUE, main="")
rug(mtcars$mpg)
x <- seq(min(mtcars$mpg), max(mtcars$mpg), length.out = 50)
lines(x, dnorm(x, mean=mean(x), sd=sd(x)), col="red", lwd=2)

In [15]:

ggplot(mtcars, aes(x=mpg)) +
geom_histogram(aes(y=..density..), binwidth=2, color="black", alpha=0) +
stat_function(fun = dnorm, arg=list(mean=mean(mtcars$mpg), sd=sd(mtcars$mpg)), color="red") +
geom_rug()

In [16]:

pie(table(mtcars$carb))

In [17]:

df <- data.frame(table(mtcars$carb))
colnames(df) <- c("Carb", "Freq")
df

Out[17]:

	Carb	Freq
1	1	7
2	2	10
3	3	3
4	4	10
5	6	1
6	8	1

In [18]:

ggplot(df, aes(x=1, y=Freq, fill=Carb)) +
geom_bar(stat="identity", color="black") +
coord_polar(theta="y") +
theme(axis.ticks=element_blank(),
      axis.text.y=element_blank(),
      axis.text.x=element_text(colour='black'),
      axis.title=element_blank())

In [19]:

barplot(table(mtcars$carb))

In [20]:

ggplot(mtcars, aes(x=factor(carb))) +
geom_bar()

In [21]:

barplot(table(mtcars$carb), horiz=TRUE)

In [22]:

ggplot(mtcars, aes(x=factor(carb))) +
geom_bar() +
coord_flip()

In [23]:

(tbl <- table(mtcars$carb, mtcars$am))
barplot(tbl, beside=TRUE, legend=rownames(tbl), col=heat.colors(mtcars$carb))

Out[23]:

In [24]:

#  Threebartable = as.data.frame(table(simData$FacVar1, simData$FacVar2, simData$FacVar3)) ## CrossTab
# ggplot(Threebartable,aes(x=Var3,y=Freq,fill=Var2))+geom_bar(position="dodge")+facet_wrap(~Var1) ## Bar plot with facetting

ggplot(mtcars, aes(x=factor(am), fill=factor(carb))) +
geom_bar(position="dodge") +
scale_fill_brewer(palette="Oranges")

In [25]:

boxplot(log1p(mtcars))

In [26]:

head(mtcars)

Out[26]:

	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
Mazda RX4	21	6	160	110	3.9	2.62	16.46	0	1	4	4
Mazda RX4 Wag	21	6	160	110	3.9	2.875	17.02	0	1	4	4
Datsun 710	22.8	4	108	93	3.85	2.32	18.61	1	1	4	1
Hornet 4 Drive	21.4	6	258	110	3.08	3.215	19.44	1	0	3	1
Hornet Sportabout	18.7	8	360	175	3.15	3.44	17.02	0	0	3	2
Valiant	18.1	6	225	105	2.76	3.46	20.22	1	0	3	1

In [27]:

df <- melt(mtcars)
ggplot(df, aes(x=variable, y=value)) +
geom_boxplot() +
scale_y_continuous(trans="log1p")

No id variables; using all as measure variables

In [28]:

df <- mtcars[order(-mtcars$mpg),]
dotchart(df$mpg, labels=row.names(df))

In [29]:

df <- mtcars[order(-mtcars$mpg),]
df$names <- as.factor(rownames(df))
ggplot(df, aes(x=reorder(names, -mpg), y=mpg)) +
geom_dotplot(binaxis="y", stackdir="center", binwidth=0.5) +
coord_flip()

In [30]:

dotchart(df$mpg, labels=row.names(df), groups=df$cyl, color=df$cyl, pch=19)

In [31]:

ggplot(df, aes(x=reorder(names, mpg), y=mpg, col=factor(cyl))) +
geom_point() +
facet_grid(. ~ cyl) +
guides(col=FALSE) +
coord_flip()

In [32]:

pairs(~mpg + drat + wt, data=mtcars)

Note: splom is from the lattice package - ggpolot does not do scatterplot matrices

In [33]:

splom(mtcars[, c("mpg", "drat", "wt")])

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