This document explains concepts and basics of {ggfortify}
. {ggfortify}
helps plotting some popular R packages with {ggplot2}
in a unified way. See github to check the list of supported packages / classes.
library(ggfortify)
ggplot2::autoplot
This is the easiest way provided by {ggfortify}
. Calling autoplot
with supported instance should output appropriate plot based on its type, as standard plot
function does.
autoplot(AirPassengers)
You can specify some options to control how plot looks. As is often the case with statistic classes, there are some objects to be drawn, such as actual value, predicted value and confidence interval, etc. Thus, each plotting option has a dot-separated format like <target name>.<ggplot option name>
such as ts.colour
and conf.int.linetype
.
These options can be used for the similar type of instances commonly. For example, ts.colour
works for all time-series-likes (there is no separate options like xts.colour
and timeSeries.colour
, etc). To check available options, use help(autoplot.ts)
or help(autoplot.*)
for any other objects.
autoplot(AirPassengers, ts.colour = 'blue')
autoplot
returns ggplot
instance. Thus, you can decorate as the same as in {ggplot2}
. To make autoplot
API simple, {ggfortify}
only offers some limited options which must be done during ggplot
instance creation. Additional decoration should be done against the returned ggplot
instance.
p <- autoplot(AirPassengers)
class(p)
## [1] "gg" "ggplot"
# plot as it is
p
# add title and labels
p + ggtitle('AirPassengers') + xlab('Year') + ylab('Passengers')
# these common options are supported as keywords
autoplot(AirPassengers, title = 'AirPassengers', xlab = 'Year', ylab = 'Passengers')
set.seed(1)
p <- autoplot(kmeans(iris[-5], 3), data = iris)
# plot as it is
p
# change colour mapping
p + scale_colour_brewer()
Internally, autoplot
calls a generic function named ggplot2::fortify
to convert the passed instance to data.frame
. {ggfortify}
defines fortify
function for all supported classes. If you want a different type of plot, you can use fortify
to get data.frame
, then call ggplot
in a normal way.
Following example shows a bar plot counting records per k-means clusters.
df <- fortify(kmeans(iris[-5], 3), data = iris)
head(df)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species cluster
## 1 5.1 3.5 1.4 0.2 setosa 1
## 2 4.9 3.0 1.4 0.2 setosa 1
## 3 4.7 3.2 1.3 0.2 setosa 1
## 4 4.6 3.1 1.5 0.2 setosa 1
## 5 5.0 3.6 1.4 0.2 setosa 1
## 6 5.4 3.9 1.7 0.4 setosa 1
ggplot(df, aes(x= cluster, fill = cluster)) + geom_bar()
autoplot
may draw subplots in some classes. For exmple, autoplot.lm
draws diagnostics plots as below.
res <- lm(Volume ~ Girth, data = trees)
mp <- autoplot(res, ncol = 4)
mp
Because {ggplot2}
itself cannot handle different kinds of plots in a single instance, {ggfortify}
handle them using its original class named ggmultiplot
. You can use +
operator to decorate ggmultiplot
.
class(mp)
mp + theme_bw()
Also, +
operator appends plots if ggplot
or ggmultiplot
instance is given as right-hand-side. Following example attaches 2 scatter plots after diagnostics.
mp +
(ggplot(trees, aes(Girth, Volume)) + geom_point()) +
(ggplot(trees, aes(Girth, Height)) + geom_point())
You can extract subset using [
and [[
operators.
mp[2:3]
mp[[1]]
You can extract arbitrary subset, modify, and draw.
mp[2:3] <- mp[2:3] + theme_bw()
mp
Assuming you want to plot multiple instances at once. {ggfortify}
can accept list
of supported instances and can output subplots.
Following example shows 3 kmeans
plots changing number of clusters.
res <- lapply(c(3, 4, 5), function(x) kmeans(iris[-5], x))
autoplot(res, data = iris[-5], ncol = 3)
Next example shows multiple survival curves as subplots.
library(survival)
res <- list(a = survfit(Surv(time, status) ~ 1, data = lung),
b = survfit(Surv(time, status) ~ sex, data = lung))
autoplot(res)
You can pass a list contains different classes to autoplot
.
library(vars)
data(Canada)
res <- list(a = Canada, b = AirPassengers)
autoplot(res)
You can instanciate ggmultiplot
from a list
of ggplot
instances.
p1 <- ggplot(iris, aes(Petal.Width, Petal.Length)) + geom_point()
p2 <- ggplot(iris, aes(Petal.Width, Petal.Length)) + geom_point()
new('ggmultiplot', plots = list(p1, p2))
Layout can be specified via nrow
and ncol
keywords.
new('ggmultiplot', plots = list(p1, p2), ncol = 1)