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# s2

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The goal of s2 is to provide R bindings to Google’s
[S2Geometry](https://s2geometry.io) library. The package exposes an API
similar to Google’s [BigQuery Geography
API](https://cloud.google.com/bigquery/docs/reference/standard-sql/geography_functions),
whose functions also operate on spherical geometries. This package is a
complete rewrite of an earlier CRAN package s2 with versions up to
0.4-2, for which the sources are found
[here](https://github.com/spatstat/s2/).

## Installation

You can install the released version of s2 from
[CRAN](https://CRAN.R-project.org) with:

``` r
install.packages("s2")
```

And the development version from [GitHub](https://github.com/) with:

``` r
# install.packages("remotes")
remotes::install_github("r-spatial/s2")
```

## Example

The s2 package provides geometry transformers and predicates similar to
those found in [GEOS](https://trac.osgeo.org/geos/), except instead of
assuming a planar geometry, s2’s functions work in latitude and
longitude and assume a spherical geometry:

``` r
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](https://r-spatial.github.io/sf/) uses s2 for geographic
coordinates with `sf::sf_use_s2(TRUE)`, and will become the default
after sf version 1.0.0. The sf package also supports creating s2 vectors
using `as_s2_geography()`:

``` r
library(dplyr)
library(sf)

nc_s2 <- read_sf(system.file("shape/nc.shp", package = "sf")) %>% 
  mutate(geometry = as_s2_geography(geometry)) %>% 
  as_tibble() %>% 
  select(NAME, geometry)

nc_s2
#> # A tibble: 100 x 2
#>    NAME        geometry                                                         
#>    <chr>       <s2_geography>                                                   
#>  1 Ashe        <POLYGON ((-81.4528885 36.2395859, -81.4310379 36.2607193, -81.4…
#>  2 Alleghany   <POLYGON ((-81.1766739 36.4154434, -81.1533661 36.4247398, -81.1…
#>  3 Surry       <POLYGON ((-80.4530106 36.2570877, -80.4353104 36.5510445, -80.6…
#>  4 Currituck   <MULTIPOLYGON (((-75.9419327 36.2943382, -75.9575119 36.2594528,…
#>  5 Northampton <POLYGON ((-77.1419601 36.4170647, -77.1393204 36.4564781, -77.1…
#>  6 Hertford    <POLYGON ((-76.7074966 36.2661324, -76.7413483 36.3151665, -76.9…
#>  7 Camden      <POLYGON ((-76.0173492 36.3377304, -76.0328751 36.3359756, -76.0…
#>  8 Gates       <POLYGON ((-76.46035 36.3738976, -76.5024643 36.4522858, -76.498…
#>  9 Warren      <POLYGON ((-78.1347198 36.2365837, -78.1096268 36.2135086, -78.0…
#> 10 Stokes      <POLYGON ((-80.0240555 36.5450249, -80.0480957 36.5471344, -80.4…
#> # … with 90 more rows
```

Use accessors to extract information about geometries:

``` r
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 ((-81.4528885 36.2395859, -81.4310379 …   1.14e9   141627.
#>  2 Alleghany <POLYGON ((-81.1766739 36.4154434, -81.1533661 …   6.11e8   119876.
#>  3 Surry     <POLYGON ((-80.4530106 36.2570877, -80.4353104 …   1.42e9   160458.
#>  4 Currituck <MULTIPOLYGON (((-75.9419327 36.2943382, -75.95…   6.94e8   301644.
#>  5 Northamp… <POLYGON ((-77.1419601 36.4170647, -77.1393204 …   1.52e9   211794.
#>  6 Hertford  <POLYGON ((-76.7074966 36.2661324, -76.7413483 …   9.68e8   160780.
#>  7 Camden    <POLYGON ((-76.0173492 36.3377304, -76.0328751 …   6.16e8   150430.
#>  8 Gates     <POLYGON ((-76.46035 36.3738976, -76.5024643 36…   9.03e8   123170.
#>  9 Warren    <POLYGON ((-78.1347198 36.2365837, -78.1096268 …   1.18e9   141073.
#> 10 Stokes    <POLYGON ((-80.0240555 36.5450249, -80.0480957 …   1.23e9   140583.
#> # … with 90 more rows
```

Use predicates to subset vectors:

``` r
nc_s2 %>% 
  filter(s2_contains(geometry, "POINT (-80.9313 35.6196)"))
#> # A tibble: 1 x 2
#>   NAME    geometry                                                              
#>   <chr>   <s2_geography>                                                        
#> 1 Catawba <POLYGON ((-80.9312744 35.6195908, -81.0035782 35.6970558, -81.054779…
```

Use transformers to create new geometries:

``` r
nc_s2 %>% 
  mutate(geometry = s2_boundary(geometry))
#> # A tibble: 100 x 2
#>    NAME        geometry                                                         
#>    <chr>       <s2_geography>                                                   
#>  1 Ashe        <LINESTRING (-81.4528885 36.2395859, -81.4310379 36.2607193, -81…
#>  2 Alleghany   <LINESTRING (-81.1766739 36.4154434, -81.1533661 36.4247398, -81…
#>  3 Surry       <LINESTRING (-80.4530106 36.2570877, -80.4353104 36.5510445, -80…
#>  4 Currituck   <MULTILINESTRING ((-75.9419327 36.2943382, -75.9575119 36.259452…
#>  5 Northampton <LINESTRING (-77.1419601 36.4170647, -77.1393204 36.4564781, -77…
#>  6 Hertford    <LINESTRING (-76.7074966 36.2661324, -76.7413483 36.3151665, -76…
#>  7 Camden      <LINESTRING (-76.0173492 36.3377304, -76.0328751 36.3359756, -76…
#>  8 Gates       <LINESTRING (-76.46035 36.3738976, -76.5024643 36.4522858, -76.4…
#>  9 Warren      <LINESTRING (-78.1347198 36.2365837, -78.1096268 36.2135086, -78…
#> 10 Stokes      <LINESTRING (-80.0240555 36.5450249, -80.0480957 36.5471344, -80…
#> # … with 90 more rows
```

Finally, use the WKB or WKT exporters to export to sf or some other
package:

``` r
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
```

## Acknowledgment

This project gratefully acknowledges financial
[support](https://www.r-consortium.org/projects) from the

<a href="https://www.r-consortium.org/projects/awarded-projects">
<img src="man/figures/rc300.png" width="300" /> </a>
