Lines and Shapes
Leaflet makes it easy to take spatial lines and shapes from R and add them to maps.
Polygons and Polylines
Line and polygon data can come from a variety of sources:
Polygonobjects (from the
Lineobjects (from the
LINESTRINGobjects (from the
mapobjects (from the
map(fill = TRUE)for polygons,
- Two-column numeric matrix; the first column is longitude and the
second is latitude. Polygons are separated by rows of
(NA, NA). It is not possible to represent multi-polygons nor polygons with holes using this method; use
# From https://www.census.gov/geo/maps-data/data/cbf/cbf_state.html states <- sf::st_read("shp/cb_2013_us_state_20m.shp", layer = "cb_2013_us_state_20m")
## Reading layer `cb_2013_us_state_20m' from data source ## `/Users/jcheng/Development/rstudio/leaflet/docs/shp/cb_2013_us_state_20m.shp' ## using driver `ESRI Shapefile' ## Simple feature collection with 52 features and 9 fields ## Geometry type: MULTIPOLYGON ## Dimension: XY ## Bounding box: xmin: -179.1473 ymin: 17.88481 xmax: 179.7785 ymax: 71.35256 ## Geodetic CRS: NAD83
neStates <- subset(states, states$STUSPS %in% c( "CT","ME","MA","NH","RI","VT","NY","NJ","PA" )) leaflet(neStates) %>% addPolygons(color = "#444444", weight = 1, smoothFactor = 0.5, opacity = 1.0, fillOpacity = 0.5, fillColor = ~colorQuantile("YlOrRd", ALAND)(ALAND), highlightOptions = highlightOptions(color = "white", weight = 2, bringToFront = TRUE))
The above example uses the
highlightOptions parameter to
emphasize the currently moused-over polygon. (The
bringToFront = TRUE argument is necessary to prevent the
thicker, white border of the active polygon from being hidden behind the
borders of other polygons that happen to be higher in the z-order.) You
highlightOptions with all of the shape layers
described on this page.
Simplifying complex polygons/polylines
Very detailed (i.e. large) shape data can present a problem for
Leafet, since it is all eventually passed into the browser and rendered
as SVG, which is very expressive and convenient but has scalability
limits. In these cases, consider using
rmapshaper::ms_simplify, which does topology-preserving
simplification conveniently from R.
library(albersusa) fullsize <- usa_sf() object.size(fullsize)
## 933016 bytes
simplified <- rmapshaper::ms_simplify(fullsize) object.size(simplified)
## 121552 bytes
Circles are added using
addCircles(). Circles are
similar to circle markers; the
only difference is that circles have their radii specified in meters,
while circle markers are specified in pixels. As a result, circles are
scaled with the map as the user zooms in and out, while circle markers
remain a constant size on the screen regardless of zoom level.
When plotting circles, only the circle centers (and radii) are required, so the set of valid data sources is different than for polygons and the same as for markers. See the introduction to Markers for specifics.
cities <- read.csv(textConnection(" City,Lat,Long,Pop Boston,42.3601,-71.0589,645966 Hartford,41.7627,-72.6743,125017 New York City,40.7127,-74.0059,8406000 Philadelphia,39.9500,-75.1667,1553000 Pittsburgh,40.4397,-79.9764,305841 Providence,41.8236,-71.4222,177994 ")) leaflet(cities) %>% addTiles() %>% addCircles(lng = ~Long, lat = ~Lat, weight = 1, radius = ~sqrt(Pop) * 30, popup = ~City )
Rectangles are added using the
lat2 vector arguments that define the corners of the
rectangles. These arguments are always required; the rectangle geometry
cannot be inferred from the data object.
leaflet() %>% addTiles() %>% addRectangles( lng1=-118.456554, lat1=34.078039, lng2=-118.436383, lat2=34.062717, fillColor = "transparent" )