knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) library(echarts4r) e_common( font_family = "Raleway" )
This document describes the maps.
Pass countries as x
argument.
cns <- countrycode::codelist$country.name.en cns <- data.frame( country = cns, value = runif(length(cns), 1, 100) ) cns |> e_charts(country) |> e_map(value) |> e_visual_map(value)
Use e_lines
(not ~~e_line
~~)
flights <- read.csv( paste0("https://raw.githubusercontent.com/plotly/datasets/", "master/2011_february_aa_flight_paths.csv") ) flights |> e_charts() |> e_geo() |> e_lines( start_lon, start_lat, end_lon, end_lat, name = "flights", lineStyle = list(normal = list(curveness = 0.3)) )
The companion package echarts4r.maps comes with 215 maps.
You can install the package with:
install.packages("remotes") remotes::install_github('JohnCoene/echarts4r.maps')
View the full list of maps with echarts4r.maps::em_bank()
.
library(echarts4r.maps) df <- data.frame( region = c("Rajasthan", "Odisha", "Gujarat"), value = c(1,2, 3) ) df |> e_charts(region) |> em_map("India") |> e_map(value, map = "India") |> e_visual_map(value) |> e_theme("infographic")
Use a custom geojson map; 1) read the json and register it with e_register_map
.
json <- jsonlite::read_json("https://raw.githubusercontent.com/shawnbot/topogram/master/data/us-states.geojson") USArrests |> tibble::rownames_to_column("states") |> e_charts(states) |> e_map_register("USA", json) |> e_map(Murder, map = "USA") |> e_visual_map(Murder)
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