Variadic lines in geom_path() and geom_line()

Using the size and color aesthetics in a mapping now produces lines with variadic width and color even within a particular group.

import pandas as pd

from lets_plot import *

LetsPlot.setup_html()

airquality = pd.read_csv("https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/datasets/airquality.csv")
airquality.head()

	rownames	Ozone	Solar.R	Wind	Temp	Month	Day
0	1	41.0	190.0	7.4	67	5	1
1	2	36.0	118.0	8.0	72	5	2
2	3	12.0	149.0	12.6	74	5	3
3	4	18.0	313.0	11.5	62	5	4
4	5	NaN	NaN	14.3	56	5	5

Let’s use size mapping to visualize even more data on a plot:

ggplot(airquality) \
    + geom_line(aes(x = 'Day', y = 'Temp', size = 'Wind', color = as_discrete('Month'))) \
    + scale_size([0.5, 5.0]) \
    + ggsize(700, 500)

color mapping can also be used to visualize more data on a single plot:

ggplot(airquality) \
    + geom_line(aes(x = 'Day', y = 'Temp', color = 'Wind', group = 'Month'), size=2) \
    + scale_color_hue(h=[0, 150], h_start=240) \
    + ggsize(700, 500)

Both size and color aesthetics can be used together to make a plot even more expressive:

ggplot(airquality) \
    + geom_line(aes(x = 'Day', y = 'Temp', size = 'Wind', color = 'Wind', group = 'Month')) \
    + scale_size([0.5, 6.0]) \
    + scale_color_hue(h=[0, 150], h_start=240) \
    + ggsize(700, 500)

Variadic lines and interactive maps

The map of Napoleon’s Russian campaign
See: https://en.wikipedia.org/wiki/Charles_Joseph_Minard#The_map_of_Napoleon’s_Russian_campaign

minard = pd.read_csv('https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Minard.troops.csv')
minard.head()

	rownames	long	lat	survivors	direction	group
0	1	24.0	54.9	340000	A	1
1	2	24.5	55.0	340000	A	1
2	3	25.5	54.5	340000	A	1
3	4	26.0	54.7	320000	A	1
4	5	27.0	54.8	300000	A	1

ggplot(minard) \
    + geom_livemap() \
    + geom_path(aes(x='long', y='lat', size='survivors', group='group', color='direction')) \
    + scale_size([1.0, 20.0])