rbokeh

The rbokeh package

The package and the Python library website. Bokeh graphing library runs in different languages (Python, Scala, Julia, R). We can plot all kinds of charts and some maps: the gallery.

A quick example

With the mtcars dataset.

library(rbokeh)

z <- lm(dist ~ speed, data = cars)

p <- figure(width = 600, height = 600) %>%
  ly_points(cars, hover = cars) %>%
  ly_lines(lowess(cars), legend = "lowess") %>%
  ly_abline(z, type = 2, legend = "lm")
p

Scatter vs. hexbin plots

This is a dataset about 753 working women. 428 work outside the home, 325 don’t. We remove observations where hours is zero. We also have socioeconomic factors affecting the work decision.

p <- figure(width = 600, height = 600) %>%
  ly_points(faminc, hours, data = work, color = largecity, glyph = largecity,
    hover = list(faminc, hours, taxableinc, hfathereduc, hmothereduc, kids618, age))
p

figure(width = 600, height = 600) %>%
  ly_hexbin(faminc, hours, data = work)

Maps

rbokeh can work with map databases such as maps , mapdata (and mapproj). The only interactions are with the ‘events’ displayed on the map (points, bubbles, choropleth (fill)). However, for a fully interactive package: leaflet.

Crude map of the world with capital cities

library(maps)

data(world.cities)
caps <- subset(world.cities, capital == 1)
caps$population <- prettyNum(caps$pop, big.mark = ",")

figure(width = 800, height = 450, padding_factor = 0) %>%
  ly_map("world", col = "gray") %>%
  ly_points(long, lat, data = caps, size = 5, hover = c(name, country.etc, population))

Maps of Canadian major CMAs

CMA: Census Metropolitan Area.

From "world", "Canada".

library(maps)
library(mapdata)

data(canada.cities)
metro <- subset(canada.cities, pop >= 1000000)
metro$population <- prettyNum(metro$pop, big.mark = ",")

figure(width = 800, height = 450, padding_factor = 0) %>%
  ly_map("world", "Canada", col = "lightgray") %>%
  ly_points(long, lat, data = metro, size = 10, color = 'red',
            hover = c(name, country.etc, population))

From "worldHires", "Canada".

library(maps)
library(mapdata)

figure(width = 800, height = 450, padding_factor = 0) %>%
  ly_map("worldHires", "Canada", col = "lightgray") %>%
  ly_points(long, lat, data = metro, size = 10, color = 'red',
            hover = c(name, country.etc, population))

Spatial vectors

the ly_polygons function can hangle spatial vectors, but again, rbokeh should not be the first choice for plotting maps.

Google Maps

The distribution of the Joshua Tree (hover of the points to see the pollinator).

bkgd = read.csv('data/JoTrPresence02202008_dryad.csv')

gmap(lat = 35.75, lng = -116.25, zoom = 6, width = 700, height = 600) %>%
  ly_points(longitude, latitude, data = bkgd, alpha = 0.8, col = "red", hover = c(pollinator, longitude, latitude))

Nothing about OpenStreetMaps.

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