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README.md
In rsgeo: An Interface to Rust's 'geo' Library
rsgeo
rsgeo is an interface to the Rust libraries geo-types and geo.
geo-types implements pure rust geometry primitives. The geo library
adds additional algorithm functionalities on top of geo-types. This
package lets you harness the speed, safety, and memory efficiency of
these libraries. geo-types does not support Z or M dimensions. There
is no support for CRS at this moment.
# install.packages(
# 'rsgeo',
# repos = c('https://josiahparry.r-universe.dev', 'https://cloud.r-project.org')
# )
library(rsgeo)
rsgeo works with vectors of geometries. When we compare this to sf
this is always the geometry column which is a class sfc object (simple
feature column).
# get geometry from sf
data(guerry, package = "sfdep")
polys <- guerry[["geometry"]] |>
sf::st_cast("POLYGON")
# cast to rust geo-types
rs_polys <- as_rsgeo(polys)
head(rs_polys)
#> <rs_POLYGON[6]>
#> [1] Polygon { exterior: LineString([Coord { x: 801150.0, y: 2092615.0 }, Coord...
#> [2] Polygon { exterior: LineString([Coord { x: 729326.0, y: 2521619.0 }, Coord...
#> [3] Polygon { exterior: LineString([Coord { x: 710830.0, y: 2137350.0 }, Coord...
#> [4] Polygon { exterior: LineString([Coord { x: 882701.0, y: 1920024.0 }, Coord...
#> [5] Polygon { exterior: LineString([Coord { x: 886504.0, y: 1922890.0 }, Coord...
#> [6] Polygon { exterior: LineString([Coord { x: 747008.0, y: 1925789.0 }, Coord...
Cast geometries to sf
sf::st_as_sfc(rs_polys)
#> Geometry set for 116 features
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: 47680 ymin: 1703258 xmax: 1031401 ymax: 2677441
#> CRS: NA
#> First 5 geometries:
#> POLYGON ((801150 2092615, 800669 2093190, 80068...
#> POLYGON ((729326 2521619, 729320 2521230, 72928...
#> POLYGON ((710830 2137350, 711746 2136617, 71243...
#> POLYGON ((882701 1920024, 882408 1920733, 88177...
#> POLYGON ((886504 1922890, 885733 1922978, 88547...
Calculate the unsigned area of polygons.
bench::mark(
rust = unsigned_area(rs_polys),
sf = sf::st_area(polys),
check = FALSE
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 rust 55.6µs 57.65µs 16411. 3.8KB 0
#> 2 sf 1.36ms 1.44ms 649. 786.9KB 8.42
Find centroids
bench::mark(
centroids(rs_polys),
sf::st_centroid(polys),
check = FALSE
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 centroids(rs_polys) 174.95µs 213µs 3720. 3.8KB 9.53
#> 2 sf::st_centroid(polys) 2.43ms 2.6ms 359. 892.9KB 4.70
Extract points coordinates
coords(rs_polys) |>
head()
#> x y line_id polygon_id
#> 1 801150 2092615 1 1
#> 2 800669 2093190 1 1
#> 3 800688 2095430 1 1
#> 4 800780 2095795 1 1
#> 5 800589 2096112 1 1
#> 6 800333 2097190 1 1
Plot the polygons and their centroids
plot(rs_polys)
plot(centroids(rs_polys), add = TRUE)
Calculate a distance matrix. Note that there is often floating point
error differences so check = FALSE in this case.
pnts <- centroids(rs_polys)
pnts_sf <- sf::st_as_sfc(pnts)
bench::mark(
rust = distance_euclidean_matrix(pnts, pnts),
sf = sf::st_distance(pnts_sf, pnts_sf),
check = FALSE
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 rust 323.53µs 573.06µs 1540. 108KB 4.08
#> 2 sf 3.48ms 3.69ms 256. 351KB 0
Simplify geometries.
x <- rs_polys
x_simple <- simplify_geoms(x, 5000)
plot(x_simple)
bench::mark(
rust = simplify_geoms(rs_polys, 500),
sf = sf::st_simplify(polys, FALSE, 500),
check = FALSE
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 rust 6.29ms 6.76ms 141. 1.91KB 0
#> 2 sf 8.52ms 9.02ms 108. 1.24MB 2.08
Union geometries with union_geoms(). Some things sf is better at! One
of which is performing unary unions of complex geometries.
plot(union_geoms(rs_polys))
bench::mark(
union_geoms(rs_polys),
sf::st_union(polys),
check = FALSE
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 union_geoms(rs_polys) 205ms 209ms 4.78 0B 0
#> 2 sf::st_union(polys) 120ms 134ms 7.49 921KB 0
We can cast between geometries as well.
lns <- cast_geoms(rs_polys, "linestring")
Some unions are faster when using rsgeo vectors like linestrings.
lns_sf <- sf::st_cast(polys, "LINESTRING")
bench::mark(
union_geoms(lns),
sf::st_union(lns_sf),
check = FALSE
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 union_geoms(lns) 117.5µs 174µs 4275. 0B 0
#> 2 sf::st_union(lns_sf) 87.8ms 94ms 10.7 2.46MB 2.68
Find the closest point to a geometry
close_pnt <- closest_point(
rs_polys,
geom_point(800000, 2090000)
)
plot(rs_polys[1])
plot(close_pnt, pch = 15, add = TRUE)
Find the haversine destination of a point, bearing, and distance.
Compare to the very fast geosphere destination point function.
bench::mark(
rust = haversine_destination(geom_point(10, 10), 45, 10000),
Cpp = geosphere::destPoint(c(10, 10), 45, 10000),
check = FALSE
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 rust 5.86µs 7.34µs 120442. 3.2KB 12.0
#> 2 Cpp 17.06µs 19.11µs 34150. 11.8MB 34.2
origin <- geom_point(10, 10)
destination <- haversine_destination(origin, 45, 10000)
plot(c(origin, destination), col = c("red", "blue"))
Find intermediate point on a great circle.
middle <- haversine_intermediate(origin, destination, 1/2)
plot(origin)
plot(destination, add = TRUE, col = "red")
plot(middle, add = TRUE, col = "blue")
Find extreme coordinates with extreme_coords()
france <- union_geoms(rs_polys)
plot(france)
plot(extreme_coords(france)[[1]], add = TRUE, pch = 15)
Get bounding rectangles
rects <- bounding_rect(rs_polys)
plot(rects)
Convex hulls
convex_hull(rs_polys) |>
plot()
Expand into constituent geometries as a list of geometry vectors
expand_geoms(rs_polys) |>
head()
#> [[1]]
#> <rs_LINESTRING[1]>
#> [1] LineString([Coord { x: 801150.0, y: 2092615.0 }, Coord { x: 800669.0, y: 2...
#>
#> [[2]]
#> <rs_LINESTRING[2]>
#> [1] LineString([Coord { x: 729326.0, y: 2521619.0 }, Coord { x: 729320.0, y: 2...
#> [2] LineString([Coord { x: 647667.0, y: 2468296.0 }, Coord { x: 647777.0, y: 2...
#>
#> [[3]]
#> <rs_LINESTRING[1]>
#> [1] LineString([Coord { x: 710830.0, y: 2137350.0 }, Coord { x: 711746.0, y: 2...
#>
#> [[4]]
#> <rs_LINESTRING[1]>
#> [1] LineString([Coord { x: 882701.0, y: 1920024.0 }, Coord { x: 882408.0, y: 1...
#>
#> [[5]]
#> <rs_LINESTRING[1]>
#> [1] LineString([Coord { x: 886504.0, y: 1922890.0 }, Coord { x: 885733.0, y: 1...
#>
#> [[6]]
#> <rs_LINESTRING[1]>
#> [1] LineString([Coord { x: 747008.0, y: 1925789.0 }, Coord { x: 746630.0, y: 1...
We can flatten the resultant geometries into a single vector using
flatten_geoms()
expand_geoms(rs_polys) |>
flatten_geoms() |>
head()
#> <rs_LINESTRING[6]>
#> [1] LineString([Coord { x: 801150.0, y: 2092615.0 }, Coord { x: 800669.0, y: 2...
#> [2] LineString([Coord { x: 729326.0, y: 2521619.0 }, Coord { x: 729320.0, y: 2...
#> [3] LineString([Coord { x: 647667.0, y: 2468296.0 }, Coord { x: 647777.0, y: 2...
#> [4] LineString([Coord { x: 710830.0, y: 2137350.0 }, Coord { x: 711746.0, y: 2...
#> [5] LineString([Coord { x: 882701.0, y: 1920024.0 }, Coord { x: 882408.0, y: 1...
#> [6] LineString([Coord { x: 886504.0, y: 1922890.0 }, Coord { x: 885733.0, y: 1...
Combine geometries into a single multi- geometry
combine_geoms(lns)
#> <rs_LINESTRING[1]>
#> [1] MultiLineString([LineString([Coord { x: 801150.0, y: 2092615.0 }, Coord { ...
Spatial predicates
x <- rs_polys[1:5]
intersects_sparse(x, rs_polys)
#> [[1]]
#> [1] 1 48 50 92 94
#>
#> [[2]]
#> [1] 2 7 63 78 80 81 98 101
#>
#> [[3]]
#> [1] 3 20 27 53 77 84 94
#>
#> [[4]]
#> [1] 4 5 30 107 109
#>
#> [[5]]
#> [1] 4 5 30 48
Notes
Right now plotting is done using wk by first casting the rsgeo into an
sfc object.
Try the rsgeo package in your browser
Any scripts or data that you put into this service are public.
rsgeo documentation built on Sept. 9, 2023, 1:07 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
rsgeo
rsgeo is an interface to the Rust libraries geo-types and geo.
geo-types implements pure rust geometry primitives. The geo library
adds additional algorithm functionalities on top of geo-types. This
package lets you harness the speed, safety, and memory efficiency of
these libraries. geo-types does not support Z or M dimensions. There
is no support for CRS at this moment.
# install.packages(
# 'rsgeo',
# repos = c('https://josiahparry.r-universe.dev', 'https://cloud.r-project.org')
# )
library(rsgeo)
rsgeo works with vectors of geometries. When we compare this to sf
this is always the geometry column which is a class sfc object (simple
feature column).
# get geometry from sf
data(guerry, package = "sfdep")
polys <- guerry[["geometry"]] |>
sf::st_cast("POLYGON")
# cast to rust geo-types
rs_polys <- as_rsgeo(polys)
head(rs_polys)
#> <rs_POLYGON[6]>
#> [1] Polygon { exterior: LineString([Coord { x: 801150.0, y: 2092615.0 }, Coord...
#> [2] Polygon { exterior: LineString([Coord { x: 729326.0, y: 2521619.0 }, Coord...
#> [3] Polygon { exterior: LineString([Coord { x: 710830.0, y: 2137350.0 }, Coord...
#> [4] Polygon { exterior: LineString([Coord { x: 882701.0, y: 1920024.0 }, Coord...
#> [5] Polygon { exterior: LineString([Coord { x: 886504.0, y: 1922890.0 }, Coord...
#> [6] Polygon { exterior: LineString([Coord { x: 747008.0, y: 1925789.0 }, Coord...
Cast geometries to sf
sf::st_as_sfc(rs_polys)
#> Geometry set for 116 features
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: 47680 ymin: 1703258 xmax: 1031401 ymax: 2677441
#> CRS: NA
#> First 5 geometries:
#> POLYGON ((801150 2092615, 800669 2093190, 80068...
#> POLYGON ((729326 2521619, 729320 2521230, 72928...
#> POLYGON ((710830 2137350, 711746 2136617, 71243...
#> POLYGON ((882701 1920024, 882408 1920733, 88177...
#> POLYGON ((886504 1922890, 885733 1922978, 88547...
Calculate the unsigned area of polygons.
bench::mark(
rust = unsigned_area(rs_polys),
sf = sf::st_area(polys),
check = FALSE
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 rust 55.6µs 57.65µs 16411. 3.8KB 0
#> 2 sf 1.36ms 1.44ms 649. 786.9KB 8.42
Find centroids
bench::mark(
centroids(rs_polys),
sf::st_centroid(polys),
check = FALSE
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 centroids(rs_polys) 174.95µs 213µs 3720. 3.8KB 9.53
#> 2 sf::st_centroid(polys) 2.43ms 2.6ms 359. 892.9KB 4.70
Extract points coordinates
coords(rs_polys) |>
head()
#> x y line_id polygon_id
#> 1 801150 2092615 1 1
#> 2 800669 2093190 1 1
#> 3 800688 2095430 1 1
#> 4 800780 2095795 1 1
#> 5 800589 2096112 1 1
#> 6 800333 2097190 1 1
Plot the polygons and their centroids
plot(rs_polys)
plot(centroids(rs_polys), add = TRUE)
Calculate a distance matrix. Note that there is often floating point
error differences so check = FALSE in this case.
pnts <- centroids(rs_polys)
pnts_sf <- sf::st_as_sfc(pnts)
bench::mark(
rust = distance_euclidean_matrix(pnts, pnts),
sf = sf::st_distance(pnts_sf, pnts_sf),
check = FALSE
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 rust 323.53µs 573.06µs 1540. 108KB 4.08
#> 2 sf 3.48ms 3.69ms 256. 351KB 0
Simplify geometries.
x <- rs_polys
x_simple <- simplify_geoms(x, 5000)
plot(x_simple)
bench::mark(
rust = simplify_geoms(rs_polys, 500),
sf = sf::st_simplify(polys, FALSE, 500),
check = FALSE
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 rust 6.29ms 6.76ms 141. 1.91KB 0
#> 2 sf 8.52ms 9.02ms 108. 1.24MB 2.08
Union geometries with union_geoms(). Some things sf is better at! One
of which is performing unary unions of complex geometries.
plot(union_geoms(rs_polys))
bench::mark(
union_geoms(rs_polys),
sf::st_union(polys),
check = FALSE
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 union_geoms(rs_polys) 205ms 209ms 4.78 0B 0
#> 2 sf::st_union(polys) 120ms 134ms 7.49 921KB 0
We can cast between geometries as well.
lns <- cast_geoms(rs_polys, "linestring")
Some unions are faster when using rsgeo vectors like linestrings.
lns_sf <- sf::st_cast(polys, "LINESTRING")
bench::mark(
union_geoms(lns),
sf::st_union(lns_sf),
check = FALSE
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 union_geoms(lns) 117.5µs 174µs 4275. 0B 0
#> 2 sf::st_union(lns_sf) 87.8ms 94ms 10.7 2.46MB 2.68
Find the closest point to a geometry
close_pnt <- closest_point(
rs_polys,
geom_point(800000, 2090000)
)
plot(rs_polys[1])
plot(close_pnt, pch = 15, add = TRUE)
Find the haversine destination of a point, bearing, and distance. Compare to the very fast geosphere destination point function.
bench::mark(
rust = haversine_destination(geom_point(10, 10), 45, 10000),
Cpp = geosphere::destPoint(c(10, 10), 45, 10000),
check = FALSE
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 rust 5.86µs 7.34µs 120442. 3.2KB 12.0
#> 2 Cpp 17.06µs 19.11µs 34150. 11.8MB 34.2
origin <- geom_point(10, 10)
destination <- haversine_destination(origin, 45, 10000)
plot(c(origin, destination), col = c("red", "blue"))
Find intermediate point on a great circle.
middle <- haversine_intermediate(origin, destination, 1/2)
plot(origin)
plot(destination, add = TRUE, col = "red")
plot(middle, add = TRUE, col = "blue")
Find extreme coordinates with extreme_coords()
france <- union_geoms(rs_polys)
plot(france)
plot(extreme_coords(france)[[1]], add = TRUE, pch = 15)
Get bounding rectangles
rects <- bounding_rect(rs_polys)
plot(rects)
Convex hulls
convex_hull(rs_polys) |>
plot()
Expand into constituent geometries as a list of geometry vectors
expand_geoms(rs_polys) |>
head()
#> [[1]]
#> <rs_LINESTRING[1]>
#> [1] LineString([Coord { x: 801150.0, y: 2092615.0 }, Coord { x: 800669.0, y: 2...
#>
#> [[2]]
#> <rs_LINESTRING[2]>
#> [1] LineString([Coord { x: 729326.0, y: 2521619.0 }, Coord { x: 729320.0, y: 2...
#> [2] LineString([Coord { x: 647667.0, y: 2468296.0 }, Coord { x: 647777.0, y: 2...
#>
#> [[3]]
#> <rs_LINESTRING[1]>
#> [1] LineString([Coord { x: 710830.0, y: 2137350.0 }, Coord { x: 711746.0, y: 2...
#>
#> [[4]]
#> <rs_LINESTRING[1]>
#> [1] LineString([Coord { x: 882701.0, y: 1920024.0 }, Coord { x: 882408.0, y: 1...
#>
#> [[5]]
#> <rs_LINESTRING[1]>
#> [1] LineString([Coord { x: 886504.0, y: 1922890.0 }, Coord { x: 885733.0, y: 1...
#>
#> [[6]]
#> <rs_LINESTRING[1]>
#> [1] LineString([Coord { x: 747008.0, y: 1925789.0 }, Coord { x: 746630.0, y: 1...
We can flatten the resultant geometries into a single vector using
flatten_geoms()
expand_geoms(rs_polys) |>
flatten_geoms() |>
head()
#> <rs_LINESTRING[6]>
#> [1] LineString([Coord { x: 801150.0, y: 2092615.0 }, Coord { x: 800669.0, y: 2...
#> [2] LineString([Coord { x: 729326.0, y: 2521619.0 }, Coord { x: 729320.0, y: 2...
#> [3] LineString([Coord { x: 647667.0, y: 2468296.0 }, Coord { x: 647777.0, y: 2...
#> [4] LineString([Coord { x: 710830.0, y: 2137350.0 }, Coord { x: 711746.0, y: 2...
#> [5] LineString([Coord { x: 882701.0, y: 1920024.0 }, Coord { x: 882408.0, y: 1...
#> [6] LineString([Coord { x: 886504.0, y: 1922890.0 }, Coord { x: 885733.0, y: 1...
Combine geometries into a single multi- geometry
combine_geoms(lns)
#> <rs_LINESTRING[1]>
#> [1] MultiLineString([LineString([Coord { x: 801150.0, y: 2092615.0 }, Coord { ...
Spatial predicates
x <- rs_polys[1:5]
intersects_sparse(x, rs_polys)
#> [[1]]
#> [1] 1 48 50 92 94
#>
#> [[2]]
#> [1] 2 7 63 78 80 81 98 101
#>
#> [[3]]
#> [1] 3 20 27 53 77 84 94
#>
#> [[4]]
#> [1] 4 5 30 107 109
#>
#> [[5]]
#> [1] 4 5 30 48
Notes
Right now plotting is done using wk by first casting the rsgeo into an
sfc object.
Try the rsgeo package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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