"boxplot_outlier" Statistics

Computes outlier values on "box-plot" chart but can be used in alternative visualizations as well.

import pandas as pd
from lets_plot import *
from lets_plot.mapping import as_discrete

LetsPlot.setup_html()

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

p = (ggplot(mpg, aes(y='hwy')) 
     + scale_color_viridis(option="magma", end=0.8) 
     + ggsize(700, 400))

# Ordering by variable "..middle.." when using stat "boxplot" or "boxplot_outlier".
class_by_middle=as_discrete('class', order_by='..middle..', order=1)

# Equivalent ordering by variable "..y.." when using `stat_summary()`.
class_by_y=as_discrete('class', order_by='..y..', order=1)

p + geom_boxplot(aes(x=class_by_middle, color='..middle..'))

1. Show Just Outliers

Use stat="boxplot_outlier".

outliers = geom_point(aes(x=class_by_middle, color='..middle..'), stat="boxplot_outlier")

p + outliers

2. Add Ribbons and Mid-points

ribbon1 = geom_ribbon(aes(
                    x=class_by_middle, 
                    ymin="..ymin..",
                    ymax="..ymax.."), stat="boxplot")

ribbon2 = geom_ribbon(aes(
                    x=class_by_middle, 
                    ymin="..lower..",
                    ymax="..upper.."), stat="boxplot")

mid_points = stat_summary(aes(x=class_by_y, color="..y.."), 
                          fun="mq", 
                          geom="point", shape=15, size=6)

p + ribbon1 + ribbon2 + mid_points + outliers + labs(color="Middle")