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
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"
Polyline to well-known text
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),(-... Well-known text to polyline
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
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