In JohnCoene/echarts4r.maps: Maps for 'echarts4r'

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

You see the maps available by running the following:

em_bank()

Otherwise, the full list of maps is vailable here.

Basics

There are 215 maps, here are two.

library(echarts4r)
library(echarts4r.maps)

jp <- e_charts() %>%
  em_map("Japan") %>% 
  e_map(map = "Japan")

ca <- e_charts() %>%
  em_map("Canada") %>% 
  e_map(map = "Canada")

e_arrange(jp, ca, cols = 2, rows = 1)

Map

Use em_map to add the map, simply pass the name of the country, a valid name as returned by em_bank, then reference that name in e_map.

library(echarts4r)
library(echarts4r.maps)

data(state)
state.x77 <- as.data.frame(state.x77)
state.x77$state <- row.names(state.x77)

state.x77 %>% 
  e_charts(state) %>%
  em_map("USA") %>% 
  e_map(Population, map = "USA") %>% 
  e_visual_map(Population)

Map 3D

This will also work in 3d with e_map_3d.

state.x77 %>% 
  e_charts(state) %>%
  em_map("USA") %>% 
  e_map_3d(Income, map = "USA") %>% 
  e_visual_map(Income)

Geo 3D

And e_geo_3d

state.x77 %>% 
    e_color_range(Murder, Color) %>% 
    dplyr::mutate(Frost = sqrt(Frost)) %>% 
    e_charts(state) %>%
    em_map("USA") %>% 
    e_geo_3d(Frost, Color, type = "USA") %>% 
    e_visual_map(Frost)

Geo

library(echarts4r.maps)

flights <- read.csv(
  paste0("https://raw.githubusercontent.com/plotly/datasets/",
         "master/2011_february_aa_flight_paths.csv")
)

flights %>% 
  e_charts() %>% 
  em_map("USA") %>%
  e_geo("USA") %>% 
  e_lines(
    start_lon, 
    start_lat, 
    end_lon, 
    end_lat,
    name = "flights",
    lineStyle = list(normal = list(curveness = 0.3))
   )

Cities

library(dplyr)
data("cities")

cities %>% 
  filter(country == "IN") %>% 
  mutate(val = runif(n(), 1, 3)) %>% 
  e_charts(lon) %>%
  em_map("India") %>% 
  e_geo(map = "India") %>% 
  e_scatter(lat, val, coord_system = "geo", scale = NULL) %>% 
  e_visual_map(min = 1, max = 3)