Description Usage Arguments Value See Also Examples

These functions are similar to accessors and predicates, but instead of
recycling `x`

and `y`

to a common length and returning a vector of that
length, these functions return a vector of length `x`

with each element
`i`

containing information about how the entire vector `y`

relates to
the feature at `x[i]`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ```
s2_closest_feature(x, y)
s2_closest_edges(x, y, k, min_distance = -1, radius = s2_earth_radius_meters())
s2_farthest_feature(x, y)
s2_distance_matrix(x, y, radius = s2_earth_radius_meters())
s2_max_distance_matrix(x, y, radius = s2_earth_radius_meters())
s2_contains_matrix(x, y, options = s2_options(model = "open"))
s2_within_matrix(x, y, options = s2_options(model = "open"))
s2_covers_matrix(x, y, options = s2_options(model = "closed"))
s2_covered_by_matrix(x, y, options = s2_options(model = "closed"))
s2_intersects_matrix(x, y, options = s2_options())
s2_disjoint_matrix(x, y, options = s2_options())
s2_equals_matrix(x, y, options = s2_options())
s2_touches_matrix(x, y, options = s2_options())
s2_dwithin_matrix(x, y, distance, radius = s2_earth_radius_meters())
s2_may_intersect_matrix(x, y, max_edges_per_cell = 50, max_feature_cells = 4)
``` |

`x, y` |
Geography vectors, coerced using |

`k` |
The number of closest edges to consider when searching. Note that in S2 a point is also considered an edge. |

`min_distance` |
The minimum distance to consider when searching for
edges. This filter is applied after the search is complete (i.e.,
may cause fewer than |

`radius` |
Radius of the earth. Defaults to the average radius of
the earth in meters as defined by |

`options` |
An |

`distance` |
A distance on the surface of the earth in the same units
as |

`max_edges_per_cell` |
For |

`max_feature_cells` |
For |

A vector of length `x`

.

See pairwise predicate functions (e.g., `s2_intersects()`

).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
city_names <- c("Vatican City", "San Marino", "Luxembourg")
cities <- s2_data_cities(city_names)
country_names <- s2_data_tbl_countries$name
countries <- s2_data_countries()
# closest feature returns y indices of the closest feature
# for each feature in x
country_names[s2_closest_feature(cities, countries)]
# farthest feature returns y indices of the farthest feature
# for each feature in x
country_names[s2_farthest_feature(cities, countries)]
# use s2_closest_edges() to find the k-nearest neighbours
nearest <- s2_closest_edges(cities, cities, k = 2, min_distance = 0)
city_names
city_names[unlist(nearest)]
# predicate matrices
country_names[s2_intersects_matrix(cities, countries)[[1]]]
# distance matrices
s2_distance_matrix(cities, cities)
s2_max_distance_matrix(cities, countries[1:4])
``` |

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