#277 Marginal plot with ggExtra & ggplot2

A scatterplot is a graph in which the values of two variables are plotted along two axes, the pattern of the resulting points revealing any correlation present.

It is sometimes important to check the distribution of variables of the X and Y axis. This can be done by adding marginal plots. The ggExtra library allows one to do it easily thanks to Dean Attali.


# library
library(ggplot2)
library(ggExtra)

# The mtcars dataset is proposed in R
head(mtcars)

# classic plot :
p=ggplot(mtcars, aes(x=wt, y=mpg, color=cyl, size=cyl)) +
      geom_point() +
      theme(legend.position="none")

# with marginal histogram
ggMarginal(p, type="histogram")

# marginal density
ggMarginal(p, type="density")

# marginal boxplot
ggMarginal(p, type="boxplot")

# library

library(ggplot2)

library(ggExtra)

# The mtcars dataset is proposed in R

head(mtcars)

# classic plot :

p=ggplot(mtcars, aes(x=wt, y=mpg, color=cyl, size=cyl)) +

geom_point() +

theme(legend.position="none")

# with marginal histogram

ggMarginal(p, type="histogram")

# marginal density

ggMarginal(p, type="density")

# marginal boxplot

ggMarginal(p, type="boxplot")

Note that you can customize the margins’ width or their features, and you can show the margin for only one axis.


# Set relative size of marginal plots (main plot 10x bigger than marginals)
ggMarginal(p, type="histogram", size=10)

# Custom marginal plots:
ggMarginal(p, type="histogram", fill = "slateblue", xparams = list(  bins=10))

# Show only marginal plot for x axis
ggMarginal(p, margins = 'x', color="purple", size=4)