I am trying to accomplish the equivalent of Ghybs Leaflet example found here, where selecting/deselecting an overlay group shows/hides the markers for a group and updates the clustering accordingly using R's leaflet package.
There is a partial solution with R here:
quakes <- quakes %>%
dplyr::mutate(mag.level = cut(mag,c(3,4,5,6),
labels = c('>3 & <=4', '>4 & <=5', '>5 & <=6')))
quakes.df <- split(quakes, quakes$mag.level)
l <- leaflet() %>% addTiles()
names(quakes.df) %>%
purrr::walk( function(df) {
l <<- l %>%
addMarkers(data=quakes.df[[df]],
lng=~long, lat=~lat,
label=~as.character(mag),
popup=~as.character(mag),
group = df,
clusterOptions = markerClusterOptions(removeOutsideVisibleBounds = F),
labelOptions = labelOptions(noHide = F,
direction = 'auto'))
})
l %>%
addLayersControl(
overlayGroups = names(quakes.df),
options = layersControlOptions(collapsed = FALSE)
)
This solution does not cluster markers across groups, even though they are proximate points. A heads up for anyone trying to recreate this solution: you need to remove label=~as.character(mag), and labelOptions = labelOptions(noHide = F, direction = 'auto') in order for the code to run.
How can I accomplish cross-group clustering while maintaining layer control ?
