The goal of s2 is to provide R bindings to Google’s S2Geometry library. The package exposes an API similar to Google’s BigQuery Geography API, whose functions also operate on spherical geometries.
Installation
You can install the released version of s2 from CRAN with:
install.packages("s2")
And the development version from GitHub with:
# install.packages("remotes") remotes::install_github("r-spatial/s2")
Example
The s2 package provides geometry transformers and predicates similar to those found in GEOS, except instead of assuming a planar geometry, s2’s functions work in latitude and longitude and assume a spherical geometry:
library(s2) s2_contains( # polygon containing much of the northern hemisphere "POLYGON ((-63.5 44.6, -149.75 61.20, 116.4 40.2, 13.5 52.51, -63.5 44.6))", # ...should contain the north pole "POINT (0 90)" ) #> [1] TRUE
The sf package will soon support s2 natively, but s2 can also read well-known text and well-known binary:
library(dplyr) library(sf) nc_s2 <- read_sf(system.file("shape/nc.shp", package = "sf")) %>% mutate(geometry = s2_geog_from_wkb(st_as_binary(geometry))) %>% as_tibble() %>% select(NAME, geometry) nc_s2 #> # A tibble: 100 x 2 #> NAME geometry #> <chr> <s2_geography> #> 1 Ashe <POLYGON ((-81.4529 36.2396, -81.431 36.2607, -81.4123 36.2673, … #> 2 Alleghany <POLYGON ((-81.1767 36.4154, -81.1534 36.4247, -81.1384 36.4176,… #> 3 Surry <POLYGON ((-80.453 36.2571, -80.4353 36.551, -80.6111 36.5573, -… #> 4 Currituck <MULTIPOLYGON (((-75.9419 36.2943, -75.9575 36.2595, -75.9138 36… #> 5 Northampton <POLYGON ((-77.142 36.4171, -77.1393 36.4565, -77.1273 36.4707, … #> 6 Hertford <POLYGON ((-76.7075 36.2661, -76.7413 36.3152, -76.9241 36.3924,… #> 7 Camden <POLYGON ((-76.0173 36.3377, -76.0329 36.336, -76.044 36.3536, -… #> 8 Gates <POLYGON ((-76.4604 36.3739, -76.5025 36.4523, -76.4983 36.5039,… #> 9 Warren <POLYGON ((-78.1347 36.2366, -78.1096 36.2135, -78.0583 36.2113,… #> 10 Stokes <POLYGON ((-80.0241 36.545, -80.0481 36.5471, -80.4353 36.551, -… #> # … with 90 more rows
Use accessors to extract information about geometries:
nc_s2 %>% mutate( area = s2_area(geometry), perimeter = s2_perimeter(geometry) ) #> # A tibble: 100 x 4 #> NAME geometry area perimeter #> <chr> <s2_geography> <dbl> <dbl> #> 1 Ashe <POLYGON ((-81.4529 36.2396, -81.431 36.2607, -… 1.14e9 141627. #> 2 Alleghany <POLYGON ((-81.1767 36.4154, -81.1534 36.4247, … 6.11e8 119876. #> 3 Surry <POLYGON ((-80.453 36.2571, -80.4353 36.551, -8… 1.42e9 160458. #> 4 Currituck <MULTIPOLYGON (((-75.9419 36.2943, -75.9575 36.… 6.94e8 301644. #> 5 Northamp… <POLYGON ((-77.142 36.4171, -77.1393 36.4565, -… 1.52e9 211794. #> 6 Hertford <POLYGON ((-76.7075 36.2661, -76.7413 36.3152, … 9.68e8 160780. #> 7 Camden <POLYGON ((-76.0173 36.3377, -76.0329 36.336, -… 6.16e8 150430. #> 8 Gates <POLYGON ((-76.4604 36.3739, -76.5025 36.4523, … 9.03e8 123170. #> 9 Warren <POLYGON ((-78.1347 36.2366, -78.1096 36.2135, … 1.18e9 141073. #> 10 Stokes <POLYGON ((-80.0241 36.545, -80.0481 36.5471, -… 1.23e9 140583. #> # … with 90 more rows
Use predicates to subset vectors:
nc_s2 %>% filter(s2_contains(geometry, "POINT (-80.9313 35.6196)")) #> # A tibble: 1 x 2 #> NAME geometry #> <chr> <s2_geography> #> 1 Catawba <POLYGON ((-80.9313 35.6196, -81.0036 35.6971, -81.0548 35.7134, -81.…
Use transformers to create new geometries:
nc_s2 %>% mutate(geometry = s2_boundary(geometry)) #> # A tibble: 100 x 2 #> NAME geometry #> <chr> <s2_geography> #> 1 Ashe <LINESTRING (-81.4529 36.2396, -81.431 36.2607, -81.4123 36.2673… #> 2 Alleghany <LINESTRING (-81.1767 36.4154, -81.1534 36.4247, -81.1384 36.417… #> 3 Surry <LINESTRING (-80.453 36.2571, -80.4353 36.551, -80.6111 36.5573,… #> 4 Currituck <MULTILINESTRING ((-75.9419 36.2943, -75.9575 36.2595, -75.9138 … #> 5 Northampton <LINESTRING (-77.142 36.4171, -77.1393 36.4565, -77.1273 36.4707… #> 6 Hertford <LINESTRING (-76.7075 36.2661, -76.7413 36.3152, -76.9241 36.392… #> 7 Camden <LINESTRING (-76.0173 36.3377, -76.0329 36.336, -76.044 36.3536,… #> 8 Gates <LINESTRING (-76.4604 36.3739, -76.5025 36.4523, -76.4983 36.503… #> 9 Warren <LINESTRING (-78.1347 36.2366, -78.1096 36.2135, -78.0583 36.211… #> 10 Stokes <LINESTRING (-80.0241 36.545, -80.0481 36.5471, -80.4353 36.551,… #> # … with 90 more rows
Finally, use the WKB or WKT exporters to export to sf or some other package:
nc_s2 %>% mutate(geometry = st_as_sfc(s2_as_binary(geometry))) %>% st_as_sf() #> Simple feature collection with 100 features and 1 field #> geometry type: GEOMETRY #> dimension: XY #> bbox: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965 #> CRS: NA #> # A tibble: 100 x 2 #> NAME geometry #> <chr> <GEOMETRY> #> 1 Ashe POLYGON ((-81.45289 36.23959, -81.43104 36.26072, -81.41233 36.26… #> 2 Alleghany POLYGON ((-81.17667 36.41544, -81.15337 36.42474, -81.1384 36.417… #> 3 Surry POLYGON ((-80.45301 36.25709, -80.43531 36.55104, -80.61105 36.55… #> 4 Currituck MULTIPOLYGON (((-75.94193 36.29434, -75.95751 36.25945, -75.91376… #> 5 Northampt… POLYGON ((-77.14196 36.41706, -77.13932 36.45648, -77.12733 36.47… #> 6 Hertford POLYGON ((-76.7075 36.26613, -76.74135 36.31517, -76.92408 36.392… #> 7 Camden POLYGON ((-76.01735 36.33773, -76.03288 36.33598, -76.04395 36.35… #> 8 Gates POLYGON ((-76.46035 36.3739, -76.50246 36.45229, -76.49834 36.503… #> 9 Warren POLYGON ((-78.13472 36.23658, -78.10963 36.21351, -78.05835 36.21… #> 10 Stokes POLYGON ((-80.02406 36.54502, -80.0481 36.54713, -80.43531 36.551… #> # … with 90 more rows
