Maps are still experimental dot not perform very well.
The function fig_map
accepts multiple dat formats. Beware of the size and level of details of these maps as they can seriously impact performances.
The function accepts sf
objects.
nc <- sf::st_read(system.file("shape/nc.shp", package="sf"))
#> Reading layer `nc' from data source `/home/jp/R/x86_64-pc-linux-gnu-library/4.0/sf/shape/nc.shp' using driver `ESRI Shapefile'
#> Simple feature collection with 100 features and 14 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
#> Geodetic CRS: NAD27
g2() %>%
fig_map(asp(color = AREA, tooltip = NAME), map = nc) %>%
axis_hide() %>%
gauge_color(c("#0D0887FF", "#CC4678FF", "#F0F921FF")) %>%
tooltip(
marker = FALSE,
showTitle = FALSE
) %>%
aka(NAME_3, "Province")
There is a convenience function to retrieve GADM data but will work with the raster::getData
function as well.
belgium <- get_gadm_data("BEL", level = 3, keep = 0.2)
#> Registered S3 method overwritten by 'geojsonlint':
#> method from
#> print.location dplyr
#> Loading required package: sp
g2() %>%
fig_map(
asp(tooltip = NAME_3),
stroke = "#fff", fill = "gray", map = belgium
) %>%
axis_hide() %>%
tooltip(
marker = FALSE,
showTitle = FALSE
) %>%
aka(NAME_3, "Province")
There is also a get_map_data
which resembles ggplot2::map_data
.