A fast and light-weight implementation of Google's polyline encoding algorithm.
Polyline encoding is a lossy compression algorithm that allows you to store a series of coordinates as a single string.
From CRAN
install.packages("googlePolylines")From github (dev version)
remotes::install_github("SymbolixAU/googlePolylines")Because googlePolylines uses Google's polyline encoding algorithm, all functions assume Google Web Mercator projection (WSG 84 / EPSG:3857 / EPSG:900913) for inputs and outputs. Objects that use other projections should be re-projected into EPSG:3857 before using these functions.
googlePolylines supports Simple Feature objects (from library(sf)), data.frames, and vectors of lon/lat coordinates.
Supported sf types
- POINT
- MULTIPOINT
- LINESTRING
- MULTILINESTRING
- POLYGON
- MULTIPOLYGON
- GEOMETRY
googlePolylines contains functions to encode coordinates into polylines, and also to parse polylines to and from well-known text format.
library(googlePolylines) library(sf) # create data df <- data.frame(myId = c(1,1,1,1,1,1,1,1,2,2,2,2), lineId = c(1,1,1,1,2,2,2,2,1,1,1,2), lon = c(-80.190, -66.118, -64.757, -80.190, -70.579, -67.514, -66.668, -70.579, -70, -49, -51, -70), lat = c(26.774, 18.466, 32.321, 26.774, 28.745, 29.570, 27.339, 28.745, 22, 23, 22, 22)) p1 <- as.matrix(df[1:4, c("lon", "lat")]) p2 <- as.matrix(df[5:8, c("lon", "lat")]) p3 <- as.matrix(df[9:12, c("lon", "lat")]) # create `sf` collections point <- sf::st_sfc(sf::st_point(x = c(df[1,"lon"], df[1,"lat"]))) multipoint <- sf::st_sfc(sf::st_multipoint(x = as.matrix(df[1:2, c("lon", "lat")]))) polygon <- sf::st_sfc(sf::st_polygon(x = list(p1, p2))) linestring <- sf::st_sfc(sf::st_linestring(p3)) multilinestring <- sf::st_sfc(sf::st_multilinestring(list(p1, p2))) multipolygon <- sf::st_sfc(sf::st_multipolygon(x = list(list(p1, p2), list(p3)))) # combine all types into one collection sf <- rbind( sf::st_sf(geo = polygon), sf::st_sf(geo = multilinestring), sf::st_sf(geo = linestring), sf::st_sf(geo = point), sf::st_sf(geo = multipoint) ) sf # Simple feature collection with 5 features and 0 fields # geometry type: GEOMETRY # dimension: XY # bbox: xmin: -80.19 ymin: 18.466 xmax: -49 ymax: 32.321 # epsg (SRID): NA # proj4string: NA # geo # 1 POLYGON ((-80.19 26.774, -6... # 2 MULTILINESTRING ((-80.19 26... # 3 LINESTRING (-70 22, -49 23,... # 4 POINT (-80.19 26.774) # 5 MULTIPOINT (-80.19 26.774, ... # encode sf objects encode(sf) geo # 1 POLYGON: ohlbDnbmhN~suq@am{tA... # 2 MULTILINESTRING: ohlbDnbmhN~suq@am{tA... # 3 LINESTRING: _{geC~zfjL_ibE_qd_C~... # 4 POINT: ohlbDnbmhN... # 5 MULTIPOINT: ohlbDnbmhN... # encode data frame as a list of points encode(df) # [1] "ohlbDnbmhN~suq@am{tAw`qsAeyhGvkz`@fge}Aw}_Kycty@gc`DesuQvvrLofdDorqGtzzVfkdh@uapB_ibE_qd_C~hbE~reK?~|}rB" enc <- encode(sf) wkt <- polyline_wkt(enc) wkt geo # 1 POLYGON ((-80.19 26.774, -66.1... # 2 MULTILINESTRING ((-80.19 26.77... # 3 LINESTRING (-70 22, -49 23, -5... # 4 POINT (-80.19 26.774)... # 5 MULTIPOINT ((-80.19 26.774),(-...enc2 <- wkt_polyline(wkt)Encoding coordinates into polylines reduces the size of objects and can increase the speed in plotting Google Maps and Mapdeck
library(sf) library(geojsonsf) sf <- geojsonsf::geojson_sf("https://raw.githubusercontent.com/SymbolixAU/data/master/geojson/SA1_2016_VIC.json") encoded <- encode(sf, FALSE) encodedLite <- encode(sf, TRUE) vapply(mget(c('sf', 'encoded', 'encodedLite') ), function(x) { format(object.size(x), units = "Kb") }, '') # sf encoded encodedLite # "38750.7 Kb" "14707.9 Kb" "9649.8 Kb"library(microbenchmark) library(sf) library(geojsonsf) library(leaflet) library(googleway) library(mapdeck) sf <- geojsonsf::geojson_sf("https://raw.githubusercontent.com/SymbolixAU/data/master/geojson/SA1_2016_VIC.json") microbenchmark( google = { ## you need a Google Map API key to use this function google_map(key = mapKey) %>% add_polygons(data = sf) }, mapdeck = { mapdeck(token = mapKey) %>% add_polygon(data = sf) }, leaflet = { leaflet(sf) %>% addTiles() %>% addPolygons() }, times = 25 ) # Unit: milliseconds # expr min lq mean median uq max neval # google 530.4193 578.3035 644.9472 606.3328 726.4577 897.9064 25 # mapdeck 527.7255 577.2322 628.5800 600.7449 682.2697 792.8950 25 # leaflet 3247.3318 3445.6265 3554.7433 3521.6720 3654.1177 4109.6708 25 These benchmarks don't account for the time taken for the browswer to render the maps