Lollipop Plot

A lollipop plot displays each element of a dataset as a segment and a circle. It is usually combined with the count stat, and is especially useful when you have several bars of the same height.

1. Parameters size, stroke and linewidth

2. Parameter fatten

3. Horizontal Sticks

4. Sloped Baseline

5. Parameter stat

6. Lollipops in Marginal Layer

7. Lollipops and a Regression Line

import random
import pandas as pd
from sklearn.linear_model import LinearRegression

from lets_plot import *

LetsPlot.setup_html()

random.seed(42)
data = {
    'x': [v - 15 for v in range(30)],
    'y': [random.uniform(1, 5) for _ in range(30)],
    'sugar': [v + 150 for v in range(30)]
}

ggplot(data, aes('x', 'y')) + geom_lollipop() + ggsize(600, 200)

1. Parameters size, stroke and linewidth

gggrid([
    ggplot(data, aes('x', 'y', size='sugar')) + geom_lollipop() + ggtitle("variable 'size'"),
    ggplot(data, aes('x', 'y', size='sugar', stroke='sugar')) + geom_lollipop() + ggtitle("variable 'size' and 'stroke'"),
    ggplot(data, aes('x', 'y', size='sugar', linewidth='sugar')) + geom_lollipop() + ggtitle("variable 'size' and 'linewidth'")
], ncol=1) + ggsize(800, 800)

2. Parameter fatten

gggrid([
    ggplot(data, aes('x', 'y')) + geom_lollipop() + ggtitle("fatten=2.5 (default)"),
    ggplot(data, aes('x', 'y')) + geom_lollipop(fatten=5) + ggtitle("fatten=5"),
])

3. Horizontal Sticks

ggplot(data, aes('y', 'x')) + geom_lollipop(dir="h")

4. Sloped Baseline

slope=0.5
intercept=1

abline=( 
    ggplot(data, aes('x', 'y')) + 
    geom_abline(intercept=intercept, slope=slope, color='black', linetype='dotted', size=1.5) + 
    coord_fixed(ylim=[-12, 12])     
       )

gggrid([
    abline + geom_lollipop(intercept=intercept, slope=slope, shape=21) + ggtitle("dir='v' (default)"),
    abline + geom_lollipop(intercept=intercept, slope=slope, shape=21, dir="h") + ggtitle("dir='h'"),
    abline + geom_lollipop(intercept=intercept, slope=slope, shape=21, dir="s") + ggtitle("dir='s'"),
])

5. Parameter stat

df = pd.read_csv("https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv")
df.head(3)

	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

gggrid([
    ggplot(df, aes(x="class")) + geom_lollipop(stat='count') + ggtitle("stat='count'"),
    ggplot(df, aes(x="hwy")) + geom_lollipop(stat='bin') + ggtitle("stat='bin'"),
    ggplot(df, aes(x="hwy")) + geom_lollipop(stat='density', n=30) + ggtitle("stat='density'"),
])

6. Lollipops in Marginal Layer

ggplot(df, aes("hwy", "cty")) + \
    geom_bin2d(binwidth=[1, 1]) + \
    ggmarginal("r", size=.2, \
               layer=geom_lollipop(aes(color='..count..'), \
                                   stat='count', orientation='y', size=1))

7. Lollipops and a Regression Line

model = LinearRegression().fit(df[["hwy"]], df["cty"])
slope, intercept = model.coef_[0], model.intercept_

ggplot(df, aes("hwy", "cty")) + \
    geom_smooth(level=.99) + \
    geom_lollipop(slope=slope, intercept=intercept, \
                  size=1.2, shape=21, color="black", fill="magenta") + \
    coord_fixed()