
Create a ggplot2 scatterplot with marginal density plots (default) or histograms, or add the marginal plots to an existing scatterplot.
ggMarginal(
p,
data,
x,
y,
type = c("density", "histogram", "boxplot", "violin", "densigram"),
margins = c("both", "x", "y"),
size = 5,
...,
xparams = list(),
yparams = list(),
groupColour = FALSE,
groupFill = FALSE
)
An object of class ggExtraPlot
. This object can be printed to show the
plots or saved using any of the typical image-saving functions (for example, using
png()
or pdf()
).
A ggplot2 scatterplot to add marginal plots to. If p
is
not provided, then all of data
, x
, and y
must be
provided.
The data.frame to use for creating the marginal plots. Ignored
if p
is provided.
The name of the variable along the x axis. Ignored if p
is
provided.
The name of the variable along the y axis. Ignored if p
is
provided.
What type of marginal plot to show. One of: [density, histogram, boxplot, violin, densigram] (a "densigram" is when a density plot is overlaid on a histogram).
Along which margins to show the plots. One of: [both, x, y].
Integer describing the relative size of the marginal plots compared to the main plot. A size of 5 means that the main plot is 5x wider and 5x taller than the marginal plots.
Extra parameters to pass to the marginal plots. Any parameter that
geom_line()
, geom_histogram()
, geom_boxplot()
, or geom_violin()
accepts
can be used. For example, colour = "red"
can be used for any marginal plot type,
and binwidth = 10
can be used for histograms.
List of extra parameters to use only for the marginal plot along the x axis.
List of extra parameters to use only for the marginal plot along the y axis.
If TRUE
, the colour (or outline) of the marginal
plots will be grouped according to the variable mapped to colour
in the
scatter plot. The variable mapped to colour
in the scatter plot must
be a character or factor variable. See examples below.
If TRUE
, the fill of the marginal
plots will be grouped according to the variable mapped to colour
in the
scatter plot. The variable mapped to colour
in the scatter plot must
be a character or factor variable. See examples below.
if (FALSE) {
library(ggplot2)
# basic usage
p <- ggplot(mtcars, aes(wt, mpg)) + geom_point()
ggMarginal(p)
# using some parameters
set.seed(30)
df <- data.frame(x = rnorm(500, 50, 10), y = runif(500, 0, 50))
p2 <- ggplot(df, aes(x, y)) + geom_point()
ggMarginal(p2)
ggMarginal(p2, type = "histogram")
ggMarginal(p2, margins = "x")
ggMarginal(p2, size = 2)
ggMarginal(p2, colour = "red")
ggMarginal(p2, colour = "red", xparams = list(colour = "blue", size = 3))
ggMarginal(p2, type = "histogram", bins = 10)
# Using violin plot
ggMarginal(p2, type = "violin")
# Using a "densigram" plot
ggMarginal(p2, type = "densigram")
# specifying the data directly instead of providing a plot
ggMarginal(data = df, x = "x", y = "y")
# more examples showing how the marginal plots are properly aligned even when
# the main plot axis/margins/size/etc are changed
set.seed(30)
df2 <- data.frame(x = c(rnorm(250, 50, 10), rnorm(250, 100, 10)),
y = runif(500, 0, 50))
p2 <- ggplot(df2, aes(x, y)) + geom_point()
ggMarginal(p2)
p2 <- p2 + ggtitle("Random data") + theme_bw(30)
ggMarginal(p2)
p3 <- ggplot(df2, aes(log(x), y - 500)) + geom_point()
ggMarginal(p3)
p4 <- p3 + scale_x_continuous(limits = c(2, 6)) + theme_bw(50)
ggMarginal(p4)
# Using groupColour and groupFill
# In order to use either of these arguments, we must map 'colour' in the
# scatter plot to a factor or character variable
p <- ggplot(mtcars, aes(x = wt, y = drat, colour = factor(vs))) +
geom_point()
ggMarginal(p, groupColour = TRUE)
ggMarginal(p, groupColour = TRUE, groupFill = TRUE)
}
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