ggExtra (version 0.3.1)

ggMarginal: Add marginal density/histogram to ggplot2 scatterplots

Description

Create a ggplot2 scatterplot with marginal density plots (default) or histograms, or add the marginal plots to an existing scatterplot.

Usage

ggMarginal(p, data, x, y, type = c("density", "histogram", "boxplot"),
  margins = c("both", "x", "y"), size = 5, ..., xparams, yparams)

Arguments

p

A ggplot2 scatterplot to add marginal plots to. If p is not provided, then all of data, x, and y must be provided.

data

The data.frame to use for creating the marginal plots. Optional if p is provided and the marginal plots are reflecting the same data.

x

The name of the variable along the x axis. Optional if p is provided and the x aesthetic is set in the main plot.

y

The name of the variable along the y axis. Optional if p is provided and the y aesthetic is set in the main plot.

type

What type of marginal plot to show. One of: [density, histogram, boxplot].

margins

Along which margins to show the plots. One of: [both, x, y].

size

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(), or geom_boxplot() accept can be used. For example, colour = "red" can be used for any marginal plot typ

xparams

List of extra parameters to use only for the marginal plot along the x axis.

yparams

List of extra parameters to use only for the marginal plot along the y axis.

Value

• 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()).

See Also

http://daattali.com/shiny/ggExtra-ggMarginal-demo/{Demo Shiny app}

Examples

# basic usage
p <- ggplot2::ggplot(mtcars, ggplot2::aes(wt, mpg)) + ggplot2::geom_point()
ggMarginal(p)

# using some parameters
set.seed(30)
df <- data.frame(x = rnorm(500, 50, 10), y = runif(500, 0, 50))
p2 <- ggplot2::ggplot(df, ggplot2::aes(x, y)) + ggplot2::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)

# 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 <- ggplot2::ggplot(df2, ggplot2::aes(x, y)) + ggplot2::geom_point()
ggMarginal(p2)

p2 <- p2 + ggplot2::ggtitle("Random data") + ggplot2::theme_bw(30)
ggMarginal(p2)

p3 <- ggplot2::ggplot(df2, ggplot2::aes(log(x), y - 500)) + ggplot2::geom_point()
ggMarginal(p3)

p4 <- p3 + ggplot2::scale_x_continuous(limits = c(2, 6)) + ggplot2::theme_bw(50)
ggMarginal(p4)

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