Marginal Plots

A marginal plot is a scatterplot (sometimes a density plot or other bivariate plot) that has histograms, boxplots, or other distribution visualization layers in the margins of the x- and y-axes.

It allows studying the relationship between 2 numeric variables.

You can use the ggmarginal() function to add marginal layers to a plot.

import numpy as np
import pandas as pd

from lets_plot import *

LetsPlot.setup_html()

# The default plot theme and palette.
LetsPlot.set_theme(theme_light())
palette = scale_color_manual(values=["#394449", "#F7C443"]) + scale_fill_manual(values=["#394449", "#F7C443"])

np.random.seed(0)

cov0 = [[1, -.8],
        [-.8, 1]]
cov1 = [[10, .1],
        [.1, .1]]

x0, y0 = np.random.multivariate_normal(mean=[-2, 0], cov=cov0, size=200).T
x1, y1 = np.random.multivariate_normal(mean=[0, 1], cov=cov1, size=200).T

data = dict(
    x=np.concatenate((x0, x1)),
    y=np.concatenate((y0, y1)),
    c=["A"] * 200 + ["B"] * 200
)

p = ggplot(data, aes("x", "y", color="c", fill="c")) + geom_point() + palette
p

# Add marginal layers to any side on the plot:
# - [l]eft
# - [r]ight
# - [t]op
# - [b]ottom

p + ggmarginal("tr", layer=geom_histogram(color="white")) + \
    ggmarginal("lb", layer=geom_density(size=0))

# Set constant colors to disable the grouping.

p_hist = p + \
    ggmarginal("tr", layer=geom_histogram(fill="gray", color="white")) + \
    ggmarginal("tr", layer=geom_density(fill="rgba(0,0,0,0)", color="red"))
p_hist

# Add marginal boxplots.
# Make margins smaller using the parameter `size`.

p_hist + ggmarginal("lb", size=.03, layer=geom_boxplot(fill="white", color="black"))

# Combine marginal violin and boxplot.
# Make margins much larger.

p + ggmarginal("rb", size=.4, layer=geom_violin(trim=False, color="black")) + \
    ggmarginal("rb", layer=geom_boxplot(aes(group="c"), fill="white", color="white",
                                        alpha=.25, outlier_color="red", width=.2))

# Set the sum of the layers as marginals.

p + ggmarginal("rb", size=.2, layer=geom_violin() + \
                                    geom_boxplot(aes(group="c"), fill="white", color="black", alpha=.5, width=.2))

# Show marginal densities on a 2D-density plot.

ggplot(data, aes("x", "y")) + geom_density2df(aes(fill="..level..")) + coord_cartesian() + \
    ggmarginal("tr", layer=geom_area(stat="density"))

# A marginal plot with a significant number of groups on margins.

df = pd.read_csv("https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv")
df.head()

	Unnamed: 0	manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class
0	1	audi	a4	1.8	1999	4	auto(l5)	f	18	29	p	compact
1	2	audi	a4	1.8	1999	4	manual(m5)	f	21	29	p	compact
2	3	audi	a4	2.0	2008	4	manual(m6)	f	20	31	p	compact
3	4	audi	a4	2.0	2008	4	auto(av)	f	21	30	p	compact
4	5	audi	a4	2.8	1999	6	auto(l5)	f	16	26	p	compact

plot_settings = ggsize(900, 700) + \
    theme(plot_background=element_rect(fill="#eaeaea"),
          legend_background=element_rect(fill="#eaeaea"))

ggplot(df, aes("cty", "hwy")) + \
    geom_point(aes(fill="manufacturer"), size=7, shape=21, alpha=.5, color="black", position=position_jitter(seed=42)) + \
    ggmarginal("tr", size=.3,
               layer=geom_boxplot(aes(fill=as_discrete("manufacturer", order_by="..lower..")), width=0.5)) + \
    plot_settings