Package 'sf'

Description

aggregate an sf object, possibly union-ing geometries

Usage

## S3 method for class 'sf'
aggregate(
  x,
  by,
  FUN,
  ...,
  do_union = TRUE,
  simplify = TRUE,
  join = st_intersects
)

Arguments

Details

In case do_union is FALSE, aggregate will simply combine geometries using c.sfg. When polygons sharing a boundary are combined, this leads to geometries that are invalid; see https://github.com/r-spatial/sf/issues/681.

Value

an sf object with aggregated attributes and geometries; additional grouping variables having the names of names(ids) or are named Group.i for ids[[i]]; see aggregate.

Note

Does not work using the formula notation involving ~ defined in aggregate.

Examples

m1 = cbind(c(0, 0, 1, 0), c(0, 1, 1, 0))
m2 = cbind(c(0, 1, 1, 0), c(0, 0, 1, 0))
pol = st_sfc(st_polygon(list(m1)), st_polygon(list(m2)))
set.seed(1985)
d = data.frame(matrix(runif(15), ncol = 3))
p = st_as_sf(x = d, coords = 1:2)
plot(pol)
plot(p, add = TRUE)
(p_ag1 = aggregate(p, pol, mean))
plot(p_ag1) # geometry same as pol
# works when x overlaps multiple objects in 'by':
p_buff = st_buffer(p, 0.2)
plot(p_buff, add = TRUE)
(p_ag2 = aggregate(p_buff, pol, mean)) # increased mean of second
# with non-matching features
m3 = cbind(c(0, 0, -0.1, 0), c(0, 0.1, 0.1, 0))
pol = st_sfc(st_polygon(list(m3)), st_polygon(list(m1)), st_polygon(list(m2)))
(p_ag3 = aggregate(p, pol, mean))
plot(p_ag3)
# In case we need to pass an argument to the join function:
(p_ag4 = aggregate(p, pol, mean, 
     join = function(x, y) st_is_within_distance(x, y, dist = 0.3)))

Description

as_Spatial() allows to convert sf and sfc to Spatial*DataFrame and ⁠Spatial*⁠ for sp compatibility. You can also use as(x, "Spatial") To transform sp objects to sf and sfc with as(x, "sf").

Usage

as_Spatial(from, cast = TRUE, IDs = paste0("ID", seq_along(from)))

Arguments

Details

Package sp supports three dimensions for POINT and MULTIPOINT (⁠SpatialPoint*⁠). Other geometries must be two-dimensional (XY). Dimensions can be dropped using st_zm() with what = "M" or what = "ZM".

For converting simple features (i.e., sf objects) to their Spatial counterpart, use as(obj, "Spatial")

Value

geometry-only object deriving from Spatial, of the appropriate class

Examples

nc <- st_read(system.file("shape/nc.shp", package="sf"))
if (require(sp, quietly = TRUE)) {
# convert to SpatialPolygonsDataFrame
spdf <- as_Spatial(nc)
# identical to
spdf <- as(nc, "Spatial")
# convert to SpatialPolygons
as(st_geometry(nc), "Spatial")
# back to sf
as(spdf, "sf")
}

Description

Bind rows (features) of sf objects

Bind columns (variables) of sf objects

Usage

## S3 method for class 'sf'
rbind(..., deparse.level = 1)

## S3 method for class 'sf'
cbind(..., deparse.level = 1, sf_column_name = NULL)

st_bind_cols(...)

Arguments

Details

both rbind and cbind have non-standard method dispatch (see cbind): the rbind or cbind method for sf objects is only called when all arguments to be binded are of class sf.

If you need to cbind e.g. a data.frame to an sf, use data.frame directly and use st_sf on its result, or use bind_cols; see examples.

st_bind_cols is deprecated; use cbind instead.

Value

cbind called with multiple sf objects warns about multiple geometry columns present when the geometry column to use is not specified by using argument sf_column_name; see also st_sf.

Examples

crs = st_crs(3857)
a = st_sf(a=1, geom = st_sfc(st_point(0:1)), crs = crs)
b = st_sf(a=1, geom = st_sfc(st_linestring(matrix(1:4,2))), crs = crs)
c = st_sf(a=4, geom = st_sfc(st_multilinestring(list(matrix(1:4,2)))), crs = crs)
rbind(a,b,c)
rbind(a,b)
rbind(a,b)
rbind(b,c)
cbind(a,b,c) # warns
if (require(dplyr, quietly = TRUE))
  dplyr::bind_cols(a,b)
c = st_sf(a=4, geomc = st_sfc(st_multilinestring(list(matrix(1:4,2)))), crs = crs)
cbind(a,b,c, sf_column_name = "geomc")
df = data.frame(x=3)
st_sf(data.frame(c, df))
if (require(dplyr, quietly = TRUE))
  dplyr::bind_cols(c, df)

Description

Drivers for which update should be TRUE by default

Usage

db_drivers

Format

An object of class character of length 12.

Description

Determine database type for R vector

Determine database type for R vector

Usage

## S4 method for signature 'PostgreSQLConnection,sf'
dbDataType(dbObj, obj)

## S4 method for signature 'DBIObject,sf'
dbDataType(dbObj, obj)

Arguments

Description

Write sf object to Database

Write sf object to Database

Usage

## S4 method for signature 'PostgreSQLConnection,character,sf'
dbWriteTable(
  conn,
  name,
  value,
  ...,
  row.names = FALSE,
  overwrite = FALSE,
  append = FALSE,
  field.types = NULL,
  binary = TRUE
)

## S4 method for signature 'DBIObject,character,sf'
dbWriteTable(
  conn,
  name,
  value,
  ...,
  row.names = FALSE,
  overwrite = FALSE,
  append = FALSE,
  field.types = NULL,
  binary = TRUE
)

Arguments

Description

Map extension to driver

Usage

extension_map

Format

An object of class list of length 26.

Description

add or remove overviews to/from a raster image

Usage

gdal_addo(
  file,
  overviews = c(2, 4, 8, 16),
  method = "NEAREST",
  layers = integer(0),
  options = character(0),
  config_options = character(0),
  clean = FALSE,
  read_only = FALSE
)

Arguments

Value

TRUE, invisibly, on success

See Also

gdal_utils for access to other gdal utilities that have a C API

Description

Native interface to gdal utils

Usage

gdal_utils(
  util = "info",
  source,
  destination,
  options = character(0),
  quiet = !(util %in% c("info", "gdalinfo", "ogrinfo", "vectorinfo", "mdiminfo")) ||
    ("-multi" %in% options),
  processing = character(0),
  colorfilename = character(0),
  config_options = character(0),
  read_only = FALSE
)

Arguments

Value

info returns a character vector with the raster metadata; all other utils return (invisibly) a logical indicating success (i.e., TRUE); in case of failure, an error is raised.

See Also

gdal_addo for adding overlays to a raster file; st_layers to query geometry type(s) and crs from layers in a (vector) data source

Examples

if (sf_extSoftVersion()["GDAL"] > "2.1.0") {
# info utils can be used to list information about a raster
# dataset. More info: https://gdal.org/programs/gdalinfo.html
in_file <- system.file("tif/geomatrix.tif", package = "sf")
gdal_utils("info", in_file, options = c("-mm", "-proj4"))

# vectortranslate utils can be used to convert simple features data between
# file formats. More info: https://gdal.org/programs/ogr2ogr.html
in_file <- system.file("shape/storms_xyz.shp", package="sf")
out_file <- paste0(tempfile(), ".gpkg")
gdal_utils(
  util = "vectortranslate",
  source = in_file,
  destination = out_file, # output format must be specified for GDAL < 2.3
  options = c("-f", "GPKG")
)
# The parameters can be specified as c("name") or c("name", "value"). The
# vectortranslate utils can perform also various operations during the
# conversion process. For example, we can reproject the features during the
# translation.
gdal_utils(
  util = "vectortranslate",
  source = in_file,
  destination = out_file,
  options = c(
  "-f", "GPKG", # output file format for GDAL < 2.3
  "-s_srs", "EPSG:4326", # input file SRS
  "-t_srs", "EPSG:2264", # output file SRS
  "-overwrite"
  )
)
st_read(out_file)
# The parameter s_srs had to be specified because, in this case, the in_file
# has no associated SRS.
st_read(in_file)
}

Description

Perform geometric set operations with simple feature geometry collections

Usage

st_intersection(x, y, ...)

## S3 method for class 'sfc'
st_intersection(x, y, ...)

## S3 method for class 'sf'
st_intersection(x, y, ...)

st_difference(x, y, ...)

## S3 method for class 'sfc'
st_difference(x, y, ...)

st_sym_difference(x, y, ...)

st_snap(x, y, tolerance)

Arguments

Details

When using GEOS and not using s2, a spatial index is built on argument x; see https://r-spatial.org/r/2017/06/22/spatial-index.html. The reference for the STR tree algorithm is: Leutenegger, Scott T., Mario A. Lopez, and Jeffrey Edgington. "STR: A simple and efficient algorithm for R-tree packing." Data Engineering, 1997. Proceedings. 13th international conference on. IEEE, 1997. For the pdf, search Google Scholar.

When called with missing y, the sfc method for st_intersection returns all non-empty intersections of the geometries of x; an attribute idx contains a list-column with the indexes of contributing geometries.

when called with a missing y, the sf method for st_intersection returns an sf object with attributes taken from the contributing feature with lowest index; two fields are added: n.overlaps with the number of overlapping features in x, and a list-column origins with indexes of all overlapping features.

When st_difference is called with a single argument, overlapping areas are erased from geometries that are indexed at greater numbers in the argument to x; geometries that are empty or contained fully inside geometries with higher priority are removed entirely. The st_difference.sfc method with a single argument returns an object with an "idx" attribute with the original index for returned geometries.

st_snap snaps the vertices and segments of a geometry to another geometry's vertices. If y contains more than one geometry, its geometries are merged into a collection before snapping to that collection.

(from the GEOS docs:) "A snap distance tolerance is used to control where snapping is performed. Snapping one geometry to another can improve robustness for overlay operations by eliminating nearly-coincident edges (which cause problems during noding and intersection calculation). Too much snapping can result in invalid topology being created, so the number and location of snapped vertices is decided using heuristics to determine when it is safe to snap. This can result in some potential snaps being omitted, however."

Value

The intersection, difference or symmetric difference between two sets of geometries. The returned object has the same class as that of the first argument (x) with the non-empty geometries resulting from applying the operation to all geometry pairs in x and y. In case x is of class sf, the matching attributes of the original object(s) are added. The sfc geometry list-column returned carries an attribute idx, which is an n-by-2 matrix with every row the index of the corresponding entries of x and y, respectively.

Note

To find whether pairs of simple feature geometries intersect, use the function st_intersects instead of st_intersection.

When using GEOS and not using s2 polygons contain their boundary. When using s2 this is determined by the model defaults of s2_options, which can be overridden via the ... argument, e.g. model = "closed" to force DE-9IM compliant behaviour of polygons (and reproduce GEOS results).

See Also

st_union for the union of simple features collections; intersect and setdiff for the base R set operations.

Examples

set.seed(131)
library(sf)
m = rbind(c(0,0), c(1,0), c(1,1), c(0,1), c(0,0))
p = st_polygon(list(m))
n = 100
l = vector("list", n)
for (i in 1:n)
  l[[i]] = p + 10 * runif(2)
s = st_sfc(l)
plot(s, col = sf.colors(categorical = TRUE, alpha = .5))
title("overlapping squares")
d = st_difference(s) # sequential differences: s1, s2-s1, s3-s2-s1, ...
plot(d, col = sf.colors(categorical = TRUE, alpha = .5))
title("non-overlapping differences")
i = st_intersection(s) # all intersections
plot(i, col = sf.colors(categorical = TRUE, alpha = .5))
title("non-overlapping intersections")
summary(lengths(st_overlaps(s, s))) # includes self-counts!
summary(lengths(st_overlaps(d, d)))
summary(lengths(st_overlaps(i, i)))
sf = st_sf(s)
i = st_intersection(sf) # all intersections
plot(i["n.overlaps"])
summary(i$n.overlaps - lengths(i$origins))
# A helper function that erases all of y from x:
st_erase = function(x, y) st_difference(x, st_union(st_combine(y)))
poly = st_polygon(list(cbind(c(0, 0, 1, 1, 0), c(0, 1, 1, 0, 0))))
lines = st_multilinestring(list(
 cbind(c(0, 1), c(1, 1.05)),
 cbind(c(0, 1), c(0, -.05)),
 cbind(c(1, .95, 1), c(1.05, .5, -.05))
))
snapped = st_snap(poly, lines, tolerance=.1)
plot(snapped, col='red')
plot(poly, border='green', add=TRUE)
plot(lines, lwd=2, col='blue', add=TRUE)

Description

Geometric binary predicates on pairs of simple feature geometry sets

Usage

st_intersects(x, y, sparse = TRUE, ...)

st_disjoint(x, y = x, sparse = TRUE, prepared = TRUE, ...)

st_touches(x, y, sparse = TRUE, prepared = TRUE, ...)

st_crosses(x, y, sparse = TRUE, prepared = TRUE, ...)

st_within(x, y, sparse = TRUE, prepared = TRUE, ...)

st_contains(x, y, sparse = TRUE, prepared = TRUE, ..., model = "open")

st_contains_properly(x, y, sparse = TRUE, prepared = TRUE, ...)

st_overlaps(x, y, sparse = TRUE, prepared = TRUE, ...)

st_equals(
  x,
  y,
  sparse = TRUE,
  prepared = FALSE,
  ...,
  retain_unique = FALSE,
  remove_self = FALSE
)

st_covers(x, y, sparse = TRUE, prepared = TRUE, ..., model = "closed")

st_covered_by(x, y = x, sparse = TRUE, prepared = TRUE, ..., model = "closed")

st_equals_exact(x, y, par, sparse = TRUE, prepared = FALSE, ...)

st_is_within_distance(x, y = x, dist, sparse = TRUE, ..., remove_self = FALSE)

Arguments

Details

If prepared is TRUE, and x contains POINT geometries and y contains polygons, then the polygon geometries are prepared, rather than the points.

For most predicates, a spatial index is built on argument x; see https://r-spatial.org/r/2017/06/22/spatial-index.html. Specifically, st_intersects, st_disjoint, st_touches st_crosses, st_within, st_contains, st_contains_properly, st_overlaps, st_equals, st_covers and st_covered_by all build spatial indexes for more efficient geometry calculations. st_relate, st_equals_exact, and do not; st_is_within_distance uses a spatial index for geographic coordinates when sf_use_s2() is true.

If y is missing, st_predicate(x, x) is effectively called, and a square matrix is returned with diagonal elements st_predicate(x[i], x[i]).

Sparse geometry binary predicate (sgbp) lists have the following attributes: region.id with the row.names of x (if any, else 1:n), ncol with the number of features in y, and predicate with the name of the predicate used.

for model, see https://github.com/r-spatial/s2/issues/32

st_contains_properly(A,B) is true if A intersects B's interior, but not its edges or exterior; A contains A, but A does not properly contain A.

See also st_relate and https://en.wikipedia.org/wiki/DE-9IM for a more detailed description of the underlying algorithms.

st_equals_exact returns true for two geometries of the same type and their vertices corresponding by index are equal up to a specified tolerance.

Value

If sparse=FALSE, st_predicate (with predicate e.g. "intersects") returns a dense logical matrix with element i,j equal to TRUE when predicate(x[i], y[j]) (e.g., when geometry of feature i and j intersect); if sparse=TRUE, an object of class sgbp is returned, which is a sparse list representation of the same matrix, with list element i an integer vector with all indices j for which predicate(x[i],y[j]) is TRUE (and hence a zero-length integer vector if none of them is TRUE). From the dense matrix, one can find out if one or more elements intersect by apply(mat, 1, any), and from the sparse list by lengths(lst) > 0, see examples below.

Note

For intersection on pairs of simple feature geometries, use the function st_intersection instead of st_intersects.

Examples

pts = st_sfc(st_point(c(.5,.5)), st_point(c(1.5, 1.5)), st_point(c(2.5, 2.5)))
pol = st_polygon(list(rbind(c(0,0), c(2,0), c(2,2), c(0,2), c(0,0))))
(lst = st_intersects(pts, pol))
(mat = st_intersects(pts, pol, sparse = FALSE))
# which points fall inside a polygon?
apply(mat, 1, any)
lengths(lst) > 0
# which points fall inside the first polygon?
st_intersects(pol, pts)[[1]]
# remove duplicate geometries:
p1 = st_point(0:1)
p2 = st_point(2:1)
p = st_sf(a = letters[1:8], geom = st_sfc(p1, p1, p2, p1, p1, p2, p2, p1))
st_equals(p)
st_equals(p, remove_self = TRUE)
(u = st_equals(p, retain_unique = TRUE))
# retain the records with unique geometries:
p[-unlist(u),]

Description

Combine several feature geometries into one, without unioning or resolving internal boundaries

Usage

st_combine(x)

st_union(x, y, ..., by_feature = FALSE, is_coverage = FALSE)

Arguments

Details

st_combine combines geometries without resolving borders, using c.sfg (analogous to c for ordinary vectors).

If st_union is called with a single argument, x, (with y missing) and by_feature is FALSE all geometries are unioned together and an sfg or single-geometry sfc object is returned. If by_feature is TRUE each feature geometry is unioned individually. This can for instance be used to resolve internal boundaries after polygons were combined using st_combine. If y is provided, all elements of x and y are unioned, pairwise if by_feature is TRUE, or else as the Cartesian product of both sets.

Unioning a set of overlapping polygons has the effect of merging the areas (i.e. the same effect as iteratively unioning all individual polygons together). Unioning a set of LineStrings has the effect of fully noding and dissolving the input linework. In this context "fully noded" means that there will be a node or endpoint in the output for every endpoint or line segment crossing in the input. "Dissolved" means that any duplicate (e.g. coincident) line segments or portions of line segments will be reduced to a single line segment in the output. Unioning a set of Points has the effect of merging all identical points (producing a set with no duplicates).

Value

st_combine returns a single, combined geometry, with no resolved boundaries; returned geometries may well be invalid.

If y is missing, st_union(x) returns a single geometry with resolved boundaries, else the geometries for all unioned pairs of x[i] and y[j].

See Also

st_intersection, st_difference, st_sym_difference

Examples

nc = st_read(system.file("shape/nc.shp", package="sf"))
st_combine(nc)
plot(st_union(nc))

Description

Compute Euclidean or great circle distance between pairs of geometries; compute, the area or the length of a set of geometries.

Usage

st_area(x, ...)

## S3 method for class 'sfc'
st_area(x, ...)

st_length(x, ...)

st_perimeter(x, ...)

st_distance(
  x,
  y,
  ...,
  dist_fun,
  by_element = FALSE,
  which = ifelse(isTRUE(st_is_longlat(x)), "Great Circle", "Euclidean"),
  par = 0,
  tolerance = 0
)

Arguments

Details

great circle distance calculations use by default spherical distances (s2_distance or s2_distance_matrix); if sf_use_s2() is FALSE, ellipsoidal distances are computed using st_geod_distance which uses function geod_inverse from GeographicLib (part of PROJ); see Karney, Charles FF, 2013, Algorithms for geodesics, Journal of Geodesy 87(1), 43–55

Value

If the coordinate reference system of x was set, these functions return values with unit of measurement; see set_units.

st_area returns the area of each feature geometry, computed in the coordinate reference system used. In case x has geodetic coordinates (unprojected), then if sf_use_s2() is FALSE st_geod_area is used for area calculation, if it is TRUE then s2_area is used: the former assumes an ellipsoidal shape, the latter a spherical shape of the Earth. In case of projected data, areas are computed in flat space. The argument ... can be used to specify radius to s2_area, to modify the Earth radius.

st_length returns the length of a LINESTRING or MULTILINESTRING geometry, using the coordinate reference system. POINT, MULTIPOINT, POLYGON or MULTIPOLYGON geometries return zero.

If by_element is FALSE st_distance returns a dense numeric matrix of dimension length(x) by length(y); otherwise it returns a numeric vector the same length as x and y with an error raised if the lengths of x and y are unequal. Distances involving empty geometries are NA.

See Also

st_dimension, st_cast to convert geometry types

Examples

b0 = st_polygon(list(rbind(c(-1,-1), c(1,-1), c(1,1), c(-1,1), c(-1,-1))))
b1 = b0 + 2
b2 = b0 + c(-0.2, 2)
x = st_sfc(b0, b1, b2)
st_area(x)
line = st_sfc(st_linestring(rbind(c(30,30), c(40,40))), crs = 4326)
st_length(line)

outer = matrix(c(0,0,10,0,10,10,0,10,0,0),ncol=2, byrow=TRUE)
hole1 = matrix(c(1,1,1,2,2,2,2,1,1,1),ncol=2, byrow=TRUE)
hole2 = matrix(c(5,5,5,6,6,6,6,5,5,5),ncol=2, byrow=TRUE)

poly = st_polygon(list(outer, hole1, hole2))
mpoly = st_multipolygon(list(
	list(outer, hole1, hole2),
	list(outer + 12, hole1 + 12)
))

st_length(st_sfc(poly, mpoly))
st_perimeter(poly)
st_perimeter(mpoly)
p = st_sfc(st_point(c(0,0)), st_point(c(0,1)), st_point(c(0,2)))
st_distance(p, p)
st_distance(p, p, by_element = TRUE)

Description

Dimension, simplicity, validity or is_empty queries on simple feature geometries

Usage

st_dimension(x, NA_if_empty = TRUE)

st_is_simple(x)

st_is_empty(x)

Arguments

Value

st_dimension returns a numeric vector with 0 for points, 1 for lines, 2 for surfaces, and, if NA_if_empty is TRUE, NA for empty geometries.

st_is_simple returns a logical vector, indicating for each geometry whether it is simple (e.g., not self-intersecting)

st_is_empty returns for each geometry whether it is empty

Examples

x = st_sfc(
	st_point(0:1),
	st_linestring(rbind(c(0,0),c(1,1))),
	st_polygon(list(rbind(c(0,0),c(1,0),c(0,1),c(0,0)))),
	st_multipoint(),
	st_linestring(),
	st_geometrycollection())
st_dimension(x)
st_dimension(x, FALSE)
ls = st_linestring(rbind(c(0,0), c(1,1), c(1,0), c(0,1)))
st_is_simple(st_sfc(ls, st_point(c(0,0))))
ls = st_linestring(rbind(c(0,0), c(1,1), c(1,0), c(0,1)))
st_is_empty(st_sfc(ls, st_point(), st_linestring()))

Description

Geometric unary operations on simple feature geometries. These are all generics, with methods for sfg, sfc and sf objects, returning an object of the same class. All operations work on a per-feature basis, ignoring all other features.

Usage

st_buffer(
  x,
  dist,
  nQuadSegs = 30,
  endCapStyle = "ROUND",
  joinStyle = "ROUND",
  mitreLimit = 1,
  singleSide = FALSE,
  ...
)

st_boundary(x)

st_convex_hull(x)

st_concave_hull(x, ratio, ..., allow_holes)

st_simplify(x, preserveTopology, dTolerance = 0)

st_triangulate(x, dTolerance = 0, bOnlyEdges = FALSE)

st_triangulate_constrained(x)

st_inscribed_circle(x, dTolerance, ...)

st_minimum_rotated_rectangle(x, ...)

st_minimum_bounding_circle(x, ...)

st_voronoi(
  x,
  envelope,
  dTolerance = 0,
  bOnlyEdges = FALSE,
  point_order = FALSE
)

st_polygonize(x)

st_line_merge(x, ..., directed = FALSE)

st_centroid(x, ..., of_largest_polygon = FALSE)

st_point_on_surface(x)

st_reverse(x)

st_node(x)

st_segmentize(x, dfMaxLength, ...)

st_exterior_ring(x, ...)

Arguments

Details

st_buffer computes a buffer around this geometry/each geometry. Depending on the spatial coordinate system, a different engine (GEOS or S2) can be used, which have different function arguments. The nQuadSegs, endCapsStyle, joinStyle, mitreLimit and singleSide parameters only work if the GEOS engine is used (i.e. projected coordinates or when sf_use_s2() is set to FALSE). See postgis.net/docs/ST_Buffer.html for details. The max_cells and min_level parameters (s2::s2_buffer_cells()) work with the S2 engine (i.e. geographic coordinates) and can be used to change the buffer shape (e.g. smoothing). If a negative buffer returns empty polygons instead of shrinking, set sf_use_s2() to FALSE.

st_boundary returns the boundary of a geometry

st_convex_hull creates the convex hull of a set of points

st_concave_hull creates the concave hull of a geometry

st_simplify simplifies lines by removing vertices.

st_triangulate triangulates set of points (not constrained). st_triangulate requires GEOS version 3.4 or above

st_triangulate_constrained returns the constrained delaunay triangulation of polygons; requires GEOS version 3.10 or above

st_inscribed_circle returns the maximum inscribed circle for polygon geometries. For st_inscribed_circle, if nQuadSegs is 0 a 2-point LINESTRING is returned with the center point and a boundary point of every circle, otherwise a circle (buffer) is returned where nQuadSegs controls the number of points per quadrant to approximate the circle. st_inscribed_circle requires GEOS version 3.9 or above

st_minimum_rotated_rectangle returns the minimum rotated rectangular POLYGON which encloses the input geometry. The rectangle has width equal to the minimum diameter, and a longer length. If the convex hill of the input is degenerate (a line or point) a linestring or point is returned.

st_minimum_bounding_circle returns a geometry which represents the "minimum bounding circle", the smallest circle that contains the input.

st_voronoi creates voronoi tessellation. st_voronoi requires GEOS version 3.5 or above

st_polygonize creates a polygon from lines that form a closed ring. In case of st_polygonize, x must be an object of class LINESTRING or MULTILINESTRING, or an sfc geometry list-column object containing these

st_line_merge merges lines. In case of st_line_merge, x must be an object of class MULTILINESTRING, or an sfc geometry list-column object containing these

st_centroid gives the centroid of a geometry

st_point_on_surface returns a point guaranteed to be on the (multi)surface.

st_reverse reverses the nodes in a line

st_node adds nodes to linear geometries at intersections without a node, and only works on individual linear geometries

st_segmentize adds points to straight lines

Value

an object of the same class of x, with manipulated geometry.

See Also

chull for a more efficient algorithm for calculating the convex hull

Examples

## st_buffer, style options (taken from rgeos gBuffer)
l1 = st_as_sfc("LINESTRING(0 0,1 5,4 5,5 2,8 2,9 4,4 6.5)")
op = par(mfrow=c(2,3))
plot(st_buffer(l1, dist = 1, endCapStyle="ROUND"), reset = FALSE, main = "endCapStyle: ROUND")
plot(l1,col='blue',add=TRUE)
plot(st_buffer(l1, dist = 1, endCapStyle="FLAT"), reset = FALSE, main = "endCapStyle: FLAT")
plot(l1,col='blue',add=TRUE)
plot(st_buffer(l1, dist = 1, endCapStyle="SQUARE"), reset = FALSE, main = "endCapStyle: SQUARE")
plot(l1,col='blue',add=TRUE)
plot(st_buffer(l1, dist = 1, nQuadSegs=1), reset = FALSE, main = "nQuadSegs: 1")
plot(l1,col='blue',add=TRUE)
plot(st_buffer(l1, dist = 1, nQuadSegs=2), reset = FALSE, main = "nQuadSegs: 2")
plot(l1,col='blue',add=TRUE)
plot(st_buffer(l1, dist = 1, nQuadSegs= 5), reset = FALSE, main = "nQuadSegs: 5")
plot(l1,col='blue',add=TRUE)
par(op)


l2 = st_as_sfc("LINESTRING(0 0,1 5,3 2)")
op = par(mfrow = c(2, 3))
plot(st_buffer(l2, dist = 1, joinStyle="ROUND"), reset = FALSE, main = "joinStyle: ROUND")
plot(l2, col = 'blue', add = TRUE)
plot(st_buffer(l2, dist = 1, joinStyle="MITRE"), reset = FALSE, main = "joinStyle: MITRE")
plot(l2, col= 'blue', add = TRUE)
plot(st_buffer(l2, dist = 1, joinStyle="BEVEL"), reset = FALSE, main = "joinStyle: BEVEL")
plot(l2, col= 'blue', add=TRUE)
plot(st_buffer(l2, dist = 1, joinStyle="MITRE" , mitreLimit=0.5), reset = FALSE,
   main = "mitreLimit: 0.5")
plot(l2, col = 'blue', add = TRUE)
plot(st_buffer(l2, dist = 1, joinStyle="MITRE",mitreLimit=1), reset = FALSE,
   main = "mitreLimit: 1")
plot(l2, col = 'blue', add = TRUE)
plot(st_buffer(l2, dist = 1, joinStyle="MITRE",mitreLimit=3), reset = FALSE,
   main = "mitreLimit: 3")
plot(l2, col = 'blue', add = TRUE)
par(op)
nc = st_read(system.file("shape/nc.shp", package="sf"))
nc_g = st_geometry(nc)
plot(st_convex_hull(nc_g))
plot(nc_g, border = grey(.5), add = TRUE)
pt = st_combine(st_sfc(st_point(c(0,80)), st_point(c(120,80)), st_point(c(240,80))))
st_convex_hull(pt) # R2
st_convex_hull(st_set_crs(pt, 'OGC:CRS84')) # S2
set.seed(131)
if (compareVersion(sf_extSoftVersion()[["GEOS"]], "3.11.0") > -1) {
 pts = cbind(runif(100), runif(100))
 m = st_multipoint(pts)
 co = sf:::st_concave_hull(m, 0.3)
 coh = sf:::st_concave_hull(m, 0.3, allow_holes = TRUE)
 plot(co, col = 'grey')
 plot(coh, add = TRUE, border = 'red')
 plot(m, add = TRUE)
}

# st_simplify examples:
op = par(mfrow = c(2, 3), mar = rep(0, 4))
plot(nc_g[1])
plot(st_simplify(nc_g[1], dTolerance = 1e3)) # 1000m
plot(st_simplify(nc_g[1], dTolerance = 5e3)) # 5000m
nc_g_planar = st_transform(nc_g, 2264) # planar coordinates, US foot
plot(nc_g_planar[1])
plot(st_simplify(nc_g_planar[1], dTolerance = 1e3)) # 1000 foot
plot(st_simplify(nc_g_planar[1], dTolerance = 5e3)) # 5000 foot
par(op)

if (compareVersion(sf_extSoftVersion()[["GEOS"]], "3.10.0") > -1) {
 pts = rbind(c(0,0), c(1,0), c(1,1), c(.5,.5), c(0,1), c(0,0))
 po = st_polygon(list(pts))
 co = st_triangulate_constrained(po)
 tr = st_triangulate(po)
 plot(po, col = NA, border = 'grey', lwd = 15)
 plot(tr, border = 'green', col = NA, lwd = 5, add = TRUE)
 plot(co, border = 'red', col = 'NA', add = TRUE)
}
if (compareVersion(sf_extSoftVersion()[["GEOS"]], "3.9.0") > -1) {
  nc_t = st_transform(nc, 'EPSG:2264')
  x = st_inscribed_circle(st_geometry(nc_t))
  plot(st_geometry(nc_t), asp = 1, col = grey(.9))
  plot(x, add = TRUE, col = '#ff9999')
}
set.seed(1)
x = st_multipoint(matrix(runif(10),,2))
box = st_polygon(list(rbind(c(0,0),c(1,0),c(1,1),c(0,1),c(0,0))))
if (compareVersion(sf_extSoftVersion()[["GEOS"]], "3.5.0") > -1) {
 v = st_sfc(st_voronoi(x, st_sfc(box)))
 plot(v, col = 0, border = 1, axes = TRUE)
 plot(box, add = TRUE, col = 0, border = 1) # a larger box is returned, as documented
 plot(x, add = TRUE, col = 'red', cex=2, pch=16)
 plot(st_intersection(st_cast(v), box)) # clip to smaller box
 plot(x, add = TRUE, col = 'red', cex=2, pch=16)
 # matching Voronoi polygons to data points:
 # https://github.com/r-spatial/sf/issues/1030
 # generate 50 random unif points:
 n = 100
 pts = st_as_sf(data.frame(matrix(runif(n), , 2), id = 1:(n/2)), coords = c("X1", "X2"))
 # compute Voronoi polygons:
 pols = st_collection_extract(st_voronoi(do.call(c, st_geometry(pts))))
 # match them to points:
 pts_pol = st_intersects(pts, pols)
 pts$pols = pols[unlist(pts_pol)] # re-order
 if (isTRUE(try(compareVersion(sf_extSoftVersion()["GEOS"], "3.12.0") > -1,
   silent = TRUE))) {
   pols_po = st_collection_extract(st_voronoi(do.call(c, st_geometry(pts)),
     point_order = TRUE)) # GEOS >= 3.12 can preserve order of inputs
   pts_pol_po = st_intersects(pts, pols_po)
   print(all(unlist(pts_pol_po) == 1:(n/2)))
 }
 plot(pts["id"], pch = 16) # ID is color
 plot(st_set_geometry(pts, "pols")["id"], xlim = c(0,1), ylim = c(0,1), reset = FALSE)
 plot(st_geometry(pts), add = TRUE)
 layout(matrix(1)) # reset plot layout
}
mls = st_multilinestring(list(matrix(c(0,0,0,1,1,1,0,0),,2,byrow=TRUE)))
st_polygonize(st_sfc(mls))
mls = st_multilinestring(list(rbind(c(0,0), c(1,1)), rbind(c(2,0), c(1,1))))
st_line_merge(st_sfc(mls))
plot(nc_g, axes = TRUE)
plot(st_centroid(nc_g), add = TRUE, pch = 3, col = 'red')
mp = st_combine(st_buffer(st_sfc(lapply(1:3, function(x) st_point(c(x,x)))), 0.2 * 1:3))
plot(mp)
plot(st_centroid(mp), add = TRUE, col = 'red') # centroid of combined geometry
plot(st_centroid(mp, of_largest_polygon = TRUE), add = TRUE, col = 'blue', pch = 3)
plot(nc_g, axes = TRUE)
plot(st_point_on_surface(nc_g), add = TRUE, pch = 3, col = 'red')
if (compareVersion(sf_extSoftVersion()[["GEOS"]], "3.7.0") > -1) {
  st_reverse(st_linestring(rbind(c(1,1), c(2,2), c(3,3))))
}
(l = st_linestring(rbind(c(0,0), c(1,1), c(0,1), c(1,0), c(0,0))))
st_polygonize(st_node(l))
st_node(st_multilinestring(list(rbind(c(0,0), c(1,1), c(0,1), c(1,0), c(0,0)))))
sf = st_sf(a=1, geom=st_sfc(st_linestring(rbind(c(0,0),c(1,1)))), crs = 4326)
if (require(lwgeom, quietly = TRUE)) {
 seg = st_segmentize(sf, units::set_units(100, km))
 seg = st_segmentize(sf, units::set_units(0.01, rad))
 nrow(seg$geom[[1]])
}

Description

Areal-weighted interpolation of polygon data

Usage

st_interpolate_aw(x, to, extensive, ...)

## S3 method for class 'sf'
st_interpolate_aw(x, to, extensive, ..., keep_NA = FALSE, na.rm = FALSE)

Arguments

Details

if extensive is TRUE and na.rm is set to TRUE, geometries with NA are effectively treated as having zero attribute values.

Examples

nc = st_read(system.file("shape/nc.shp", package="sf"))
g = st_make_grid(nc, n = c(10, 5))
a1 = st_interpolate_aw(nc["BIR74"], g, extensive = FALSE)
sum(a1$BIR74) / sum(nc$BIR74) # not close to one: property is assumed spatially intensive
a2 = st_interpolate_aw(nc["BIR74"], g, extensive = TRUE)
# verify mass preservation (pycnophylactic) property:
sum(a2$BIR74) / sum(nc$BIR74)
a1$intensive = a1$BIR74
a1$extensive = a2$BIR74
plot(a1[c("intensive", "extensive")], key.pos = 4)

Description

Search through the driver table if driver is listed

Usage

is_driver_available(drv, drivers = st_drivers())

Arguments

Description

Search through the driver table to match a driver name with an action (e.g. "write") and check if the action is supported.

Usage

is_driver_can(drv, drivers = st_drivers(), operation = "write")

Arguments

Description

Check if the columns could be of a coercable type for sf

Usage

is_geometry_column(con, x, classes = "")

Arguments

Description

merge method for sf and data.frame object

Usage

## S3 method for class 'sf'
merge(x, y, ...)

Arguments

Examples

a = data.frame(a = 1:3, b = 5:7)
st_geometry(a) = st_sfc(st_point(c(0,0)), st_point(c(1,1)), st_point(c(2,2)))
b = data.frame(x = c("a", "b", "c"), b = c(2,5,6))
merge(a, b)
merge(a, b, all = TRUE)

Description

Sudden Infant Death Syndrome (SIDS) sample data for North Carolina counties, two time periods (1974-78 and 1979-84). The details of the columns can be found in a spdep package vignette. Please note that, though this is basically the same as nc.sids dataset in spData package, nc only contains a subset of variables. The differences are also discussed on the vignette.

Format

A sf object

See Also

https://r-spatial.github.io/spdep/articles/sids.html

Examples

nc <- st_read(system.file("shape/nc.shp", package="sf"))

Description

Arithmetic operators for simple feature geometries

Usage

## S3 method for class 'sfg'
Ops(e1, e2)

## S3 method for class 'sfc'
Ops(e1, e2)

Arguments

Details

in case e2 is numeric, +, -, *, /, %% and %/% add, subtract, multiply, divide, modulo, or integer-divide by e2. In case e2 is an n x n matrix, * matrix-multiplies and / multiplies by its inverse. If e2 is an sfg object, |, /, & and %/% result in the geometric union, difference, intersection and symmetric difference respectively, and == and != return geometric (in)equality, using st_equals. If e2 is an sfg or sfc object, for operations + and - it has to have POINT geometries.

If e1 is of class sfc, and e2 is a length 2 numeric, then it is considered a two-dimensional point (and if needed repeated as such) only for operations + and -, in other cases the individual numbers are repeated; see commented examples.

It has been reported (https://github.com/r-spatial/sf/issues/2067) that certain ATLAS versions result in invalid polygons, where the final point in a ring is no longer equal to the first point. In that case, setting the precisions with st_set_precision may help.

Value

object of class sfg

Examples

st_point(c(1,2,3)) + 4
st_point(c(1,2,3)) * 3 + 4
m = matrix(0, 2, 2)
diag(m) = c(1, 3)
# affine:
st_point(c(1,2)) * m + c(2,5)
# world in 0-360 range:
if (require(maps, quietly = TRUE)) {
 w = st_as_sf(map('world', plot = FALSE, fill = TRUE))
 w2 = (st_geometry(w) + c(360,90)) %% c(360) - c(0,90)
 w3 = st_wrap_dateline(st_set_crs(w2 - c(180,0), 4326)) + c(180,0)
 plot(st_set_crs(w3, 4326), axes = TRUE)
}
(mp <- st_point(c(1,2)) + st_point(c(3,4))) # MULTIPOINT (1 2, 3 4)
mp - st_point(c(3,4)) # POINT (1 2)
opar = par(mfrow = c(2,2), mar = c(0, 0, 1, 0))
a = st_buffer(st_point(c(0,0)), 2)
b = a + c(2, 0)
p = function(m) { plot(c(a,b)); plot(eval(parse(text=m)), col=grey(.9), add = TRUE); title(m) }
o = lapply(c('a | b', 'a / b', 'a & b', 'a %/% b'), p)
par(opar)
sfc = st_sfc(st_point(0:1), st_point(2:3))
sfc + c(2,3) # added to EACH geometry
sfc * c(2,3) # first geometry multiplied by 2, second by 3
nc = st_transform(st_read(system.file("gpkg/nc.gpkg", package="sf")), 32119) # nc state plane, m
b = st_buffer(st_centroid(st_union(nc)), units::set_units(50, km)) # shoot a hole in nc:
plot(st_geometry(nc) / b, col = grey(.9))

Description

plot one or more attributes of an sf object on a map Plot sf object

Usage

## S3 method for class 'sf'
plot(
  x,
  y,
  ...,
  main,
  pal = NULL,
  nbreaks = 10,
  breaks = "pretty",
  max.plot = getOption("sf_max.plot", default = 9),
  key.pos = get_key_pos(x, ...),
  key.length = 0.618,
  key.width = kw_dflt(x, key.pos),
  reset = TRUE,
  logz = FALSE,
  extent = x,
  xlim = st_bbox(extent)[c(1, 3)],
  ylim = st_bbox(extent)[c(2, 4)],
  compact = FALSE
)

get_key_pos(x, ...)

## S3 method for class 'sfc_POINT'
plot(
  x,
  y,
  ...,
  pch = 1,
  cex = 1,
  col = 1,
  bg = 0,
  lwd = 1,
  lty = 1,
  type = "p",
  add = FALSE
)

## S3 method for class 'sfc_MULTIPOINT'
plot(
  x,
  y,
  ...,
  pch = 1,
  cex = 1,
  col = 1,
  bg = 0,
  lwd = 1,
  lty = 1,
  type = "p",
  add = FALSE
)

## S3 method for class 'sfc_LINESTRING'
plot(x, y, ..., lty = 1, lwd = 1, col = 1, pch = 1, type = "l", add = FALSE)

## S3 method for class 'sfc_CIRCULARSTRING'
plot(x, y, ...)

## S3 method for class 'sfc_MULTILINESTRING'
plot(x, y, ..., lty = 1, lwd = 1, col = 1, pch = 1, type = "l", add = FALSE)

## S3 method for class 'sfc_POLYGON'
plot(
  x,
  y,
  ...,
  lty = 1,
  lwd = 1,
  col = NA,
  cex = 1,
  pch = NA,
  border = 1,
  add = FALSE,
  rule = "evenodd",
  xpd = par("xpd")
)

## S3 method for class 'sfc_MULTIPOLYGON'
plot(
  x,
  y,
  ...,
  lty = 1,
  lwd = 1,
  col = NA,
  border = 1,
  add = FALSE,
  rule = "evenodd",
  xpd = par("xpd")
)

## S3 method for class 'sfc_GEOMETRYCOLLECTION'
plot(
  x,
  y,
  ...,
  pch = 1,
  cex = 1,
  bg = 0,
  lty = 1,
  lwd = 1,
  col = 1,
  border = 1,
  add = FALSE
)

## S3 method for class 'sfc_GEOMETRY'
plot(
  x,
  y,
  ...,
  pch = 1,
  cex = 1,
  bg = 0,
  lty = 1,
  lwd = 1,
  col = ifelse(st_dimension(x) == 2, NA, 1),
  border = 1,
  add = FALSE
)

## S3 method for class 'sfg'
plot(x, ...)

plot_sf(
  x,
  xlim = NULL,
  ylim = NULL,
  asp = NA,
  axes = FALSE,
  bgc = par("bg"),
  ...,
  xaxs,
  yaxs,
  lab,
  setParUsrBB = FALSE,
  bgMap = NULL,
  expandBB = c(0, 0, 0, 0),
  graticule = NA_crs_,
  col_graticule = "grey",
  border,
  extent = x
)

sf.colors(n = 10, cutoff.tails = c(0.35, 0.2), alpha = 1, categorical = FALSE)

## S3 method for class 'sf'
text(x, labels = row.names(x), ...)

## S3 method for class 'sfc'
text(x, labels = seq_along(x), ..., of_largest_polygon = FALSE)

## S3 method for class 'sf'
points(x, ...)

## S3 method for class 'sfc'
points(x, ..., of_largest_polygon = FALSE)

Arguments

Details

plot.sf maximally plots max.plot maps with colors following from attribute columns, one map per attribute. It uses sf.colors for default colors. For more control over placement of individual maps, set parameter mfrow with par prior to plotting, and plot single maps one by one; note that this only works in combination with setting parameters key.pos=NULL (no legend) and reset=FALSE.

plot.sfc plots the geometry, additional parameters can be passed on to control color, lines or symbols.

When setting reset to FALSE, the original device parameters are lost, and the device must be reset using dev.off() in order to reset it.

parameter at can be set to specify where labels are placed along the key; see examples.

The features are plotted in the order as they apppear in the sf object. See examples for when a different plotting order is wanted.

plot_sf sets up the plotting area, axes, graticule, or webmap background; it is called by all plot methods before anything is drawn.

The argument setParUsrBB may be used to pass the logical value TRUE to functions within plot.Spatial. When set to TRUE, par(“usr”) will be overwritten with c(xlim, ylim), which defaults to the bounding box of the spatial object. This is only needed in the particular context of graphic output to a specified device with given width and height, to be matched to the spatial object, when using par(“xaxs”) and par(“yaxs”) in addition to par(mar=c(0,0,0,0)).

The default aspect for map plots is 1; if however data are not projected (coordinates are long/lat), the aspect is by default set to 1/cos(My * pi/180) with My the y coordinate of the middle of the map (the mean of ylim, which defaults to the y range of bounding box). This implies an Equirectangular projection.

non-categorical colors from sf.colors were taken from bpy.colors, with modified cutoff.tails defaults If categorical is TRUE, default colors are from https://colorbrewer2.org/ (if n < 9, Set2, else Set3).

text.sf adds text to an existing base graphic. Text is placed at the centroid of each feature in x. Provide POINT features for further control of placement. points.sf adds point symbols to an existing base graphic. If points of text are not shown correctly, try setting argument reset to FALSE in the plot() call.

Examples

nc = st_read(system.file("gpkg/nc.gpkg", package="sf"), quiet = TRUE)
# plot single attribute, auto-legend:
plot(nc["SID74"])
# plot multiple:
plot(nc[c("SID74", "SID79")]) # better use ggplot2::geom_sf to facet and get a single legend!
# adding to a plot of an sf object only works when using reset=FALSE in the first plot:
plot(nc["SID74"], reset = FALSE)
plot(st_centroid(st_geometry(nc)), add = TRUE)
# log10 z-scale:
plot(nc["SID74"], logz = TRUE, breaks = c(0,.5,1,1.5,2), at = c(0,.5,1,1.5,2))
# and we need to reset the plotting device after that, e.g. by
layout(1)
# when plotting only geometries, the reset=FALSE is not needed:
plot(st_geometry(nc))
plot(st_geometry(nc)[1], col = 'red', add = TRUE)
# add a custom legend to an arbitray plot:
layout(matrix(1:2, ncol = 2), widths = c(1, lcm(2)))
plot(1)
.image_scale(1:10, col = sf.colors(9), key.length = lcm(8), key.pos = 4, at = 1:10)
# manipulate plotting order, plot largest polygons first:
p = st_polygon(list(rbind(c(0,0), c(1,0), c(1,1), c(0,1), c(0,0))))
x = st_sf(a=1:4, st_sfc(p, p * 2, p * 3, p * 4)) # plot(x, col=2:5) only shows the largest polygon!
plot(x[order(st_area(x), decreasing = TRUE),], col = 2:5) # plot largest polygons first

sf.colors(10)
text(nc, labels = substring(nc$NAME,1,1))

Description

Map prefix to driver

Usage

prefix_map

Format

An object of class list of length 10.

Description

Query or manage PROJ search path and network settings

Usage

sf_proj_search_paths(paths = character(0), with_proj = NA)

sf_proj_network(enable = FALSE, url = character(0))

sf_proj_pipelines(
  source_crs,
  target_crs,
  authority = character(0),
  AOI = numeric(0),
  Use = "NONE",
  grid_availability = "USED",
  desired_accuracy = -1,
  strict_containment = FALSE,
  axis_order_authority_compliant = st_axis_order()
)

Arguments

Value

sf_proj_search_paths() returns the search path (possibly after setting it)

sf_proj_network when called without arguments returns a logical indicating whether network search of datum grids is enabled, when called with arguments it returns a character vector with the URL of the CDN used (or specified with url).

sf_proj_pipelines() returns a table with candidate coordinate transformation pipelines along with their accuracy; NA accuracy indicates ballpark accuracy.

Description

Convert raw vector(s) into hexadecimal character string(s)

Usage

rawToHex(x)

Arguments

Description

functions for spherical geometry, using the s2 package based on the google s2geometry.io library

Usage

sf_use_s2(use_s2)

st_as_s2(x, ...)

## S3 method for class 'sf'
st_as_s2(x, ...)

## S3 method for class 'sfc'
st_as_s2(
  x,
  ...,
  oriented = getOption("s2_oriented", FALSE) || isTRUE(attr(x, "oriented")),
  rebuild = FALSE
)

Arguments

Details

st_as_s2 converts an sf POLYGON object into a form readable by s2.

Value

sf_use_s2 returns the value of this variable before (re)setting it, invisibly if use_s2 is not missing.

Examples

m = rbind(c(-1,-1), c(1,-1), c(1,1), c(-1,1), c(-1,-1))
m1 = rbind(c(-1,-1), c(1,-1), c(1,1), c(-1,1), c(-1,0), c(-1,-1))
m0 = m[5:1,]
mp = st_multipolygon(list(
list(m, 0.8 * m0, 0.01 * m1 + 0.9),
list(0.7* m, 0.6*m0),
list(0.5 * m0),
list(m+2),
list(m+4,(.9*m0)+4)
))
sf = st_sfc(mp, mp, crs = 'EPSG:4326')
s2 = st_as_s2(sf)

Description

Create sf, which extends data.frame-like objects with a simple feature list column. To convert a data frame object to sf, use st_as_sf()

Usage

st_sf(
  ...,
  agr = NA_agr_,
  row.names,
  stringsAsFactors = sf_stringsAsFactors(),
  crs,
  precision,
  sf_column_name = NULL,
  check_ring_dir = FALSE,
  sfc_last = TRUE
)

## S3 method for class 'sf'
x[i, j, ..., drop = FALSE, op = st_intersects]

## S3 method for class 'sf'
print(x, ..., n = getOption("sf_max_print", default = 10))

Arguments

Details

agr, attribute-geometry-relationship, specifies for each non-geometry attribute column how it relates to the geometry, and can have one of following values: "constant", "aggregate", "identity". "constant" is used for attributes that are constant throughout the geometry (e.g. land use), "aggregate" where the attribute is an aggregate value over the geometry (e.g. population density or population count), "identity" when the attributes uniquely identifies the geometry of particular "thing", such as a building ID or a city name. The default value, NA_agr_, implies we don't know.

When a single value is provided to agr, it is cascaded across all input columns; otherwise, a named vector like c(feature1='constant', ...) will set agr value to 'constant' for the input column named feature1. See demo(nc) for a worked example of this.

When confronted with a data.frame-like object, st_sf will try to find a geometry column of class sfc, and otherwise try to convert list-columns when available into a geometry column, using st_as_sfc.

[.sf will return a data.frame or vector if the geometry column (of class sfc) is dropped (drop=TRUE), an sfc object if only the geometry column is selected, and otherwise return an sf object; see also [.data.frame; for [.sf ... arguments are passed to op.

Examples

g = st_sfc(st_point(1:2))
st_sf(a=3,g)
st_sf(g, a=3)
st_sf(a=3, st_sfc(st_point(1:2))) # better to name it!
# create empty structure with preallocated empty geometries:
nrows <- 10
geometry = st_sfc(lapply(1:nrows, function(x) st_geometrycollection()))
df <- st_sf(id = 1:nrows, geometry = geometry)
g = st_sfc(st_point(1:2), st_point(3:4))
s = st_sf(a=3:4, g)
s[1,]
class(s[1,])
s[,1]
class(s[,1])
s[,2]
class(s[,2])
g = st_sf(a=2:3, g)
pol = st_sfc(st_polygon(list(cbind(c(0,3,3,0,0),c(0,0,3,3,0)))))
h = st_sf(r = 5, pol)
g[h,]
h[g,]

Description

Provide the external dependencies versions of the libraries linked to sf

Usage

sf_extSoftVersion()

Description

directly transform a set of coordinates

Usage

sf_add_proj_units()

sf_project(
  from = character(0),
  to = character(0),
  pts,
  keep = FALSE,
  warn = TRUE,
  authority_compliant = st_axis_order()
)

Arguments

Details

sf_add_proj_units loads the PROJ units link, us_in, ind_yd, ind_ft, and ind_ch into the udunits database, and returns TRUE invisibly on success.

Value

two-column numeric matrix with transformed/converted coordinates, returning invalid values as Inf

Examples

sf_add_proj_units()

Description

Create simple feature geometry list column, set class, and add coordinate reference system and precision. For data.frame alternatives see st_sf(). To convert a foreign object to sfc, see st_as_sfc()

Usage

st_sfc(
  ...,
  crs = NA_crs_,
  precision = 0,
  check_ring_dir = FALSE,
  dim,
  recompute_bbox = FALSE,
  oriented = NA
)

## S3 method for class 'sfc'
x[i, j, ..., op = st_intersects]

Arguments

Details

A simple feature geometry list-column is a list of class c("stc_TYPE", "sfc") which most often contains objects of identical type; in case of a mix of types or an empty set, TYPE is set to the superclass GEOMETRY.

if x has a dim attribute (i.e. is an array or matrix) then op cannot be used.

Value

an object of class sfc, which is a classed list-column with simple feature geometries.

Examples

pt1 = st_point(c(0,1))
pt2 = st_point(c(1,1))
(sfc = st_sfc(pt1, pt2))
sfc[sfc[1], op = st_is_within_distance, dist = 0.5]
d = st_sf(data.frame(a=1:2, geom=sfc))

Description

Methods for dealing with sparse geometry binary predicate lists

Usage

## S3 method for class 'sgbp'
print(x, ..., n = 10, max_nb = 10)

## S3 method for class 'sgbp'
t(x)

## S3 method for class 'sgbp'
as.matrix(x, ...)

## S3 method for class 'sgbp'
dim(x)

## S3 method for class 'sgbp'
Ops(e1, e2)

## S3 method for class 'sgbp'
as.data.frame(x, ...)

Arguments

Details

sgbp are sparse matrices, stored as a list with integer vectors holding the ordered TRUE indices of each row. This means that for a dense, $m \times n$ matrix Q and a list L, if Q[i,j] is TRUE then $j$ is an element of L[[i]]. Reversed: when $k$ is the value of L[[i]][j], then Q[i,k] is TRUE.

== compares only the dimension and index values, not the attributes of two sgbp object; use identical to check for equality of everything.

Description

Create simple feature from a numeric vector, matrix or list

Usage

st_point(x = c(NA_real_, NA_real_), dim = "XYZ")

st_multipoint(x = matrix(numeric(0), 0, 2), dim = "XYZ")

st_linestring(x = matrix(numeric(0), 0, 2), dim = "XYZ")

st_polygon(x = list(), dim = if (length(x)) "XYZ" else "XY")

st_multilinestring(x = list(), dim = if (length(x)) "XYZ" else "XY")

st_multipolygon(x = list(), dim = if (length(x)) "XYZ" else "XY")

st_geometrycollection(x = list(), dims = "XY")

## S3 method for class 'sfg'
print(x, ..., width = 0)

## S3 method for class 'sfg'
head(x, n = 10L, ...)

## S3 method for class 'sfg'
format(x, ..., width = 30)

## S3 method for class 'sfg'
c(..., recursive = FALSE, flatten = TRUE)

## S3 method for class 'sfg'
as.matrix(x, ...)

Arguments

Details

"XYZ" refers to coordinates where the third dimension represents altitude, "XYM" refers to three-dimensional coordinates where the third dimension refers to something else ("M" for measure); checking of the sanity of x may be only partial.

When flatten=TRUE, this method may merge points into a multipoint structure, and may not preserve order, and hence cannot be reverted. When given fish, it returns fish soup.

Value

object of the same nature as x, but with appropriate class attribute set

as.matrix returns the set of points that form a geometry as a single matrix, where each point is a row; use unlist(x, recursive = FALSE) to get sets of matrices.

Examples

(p1 = st_point(c(1,2)))
class(p1)
st_bbox(p1)
(p2 = st_point(c(1,2,3)))
class(p2)
(p3 = st_point(c(1,2,3), "XYM"))
pts = matrix(1:10, , 2)
(mp1 = st_multipoint(pts))
pts = matrix(1:15, , 3)
(mp2 = st_multipoint(pts))
(mp3 = st_multipoint(pts, "XYM"))
pts = matrix(1:20, , 4)
(mp4 = st_multipoint(pts))
pts = matrix(1:10, , 2)
(ls1 = st_linestring(pts))
pts = matrix(1:15, , 3)
(ls2 = st_linestring(pts))
(ls3 = st_linestring(pts, "XYM"))
pts = matrix(1:20, , 4)
(ls4 = st_linestring(pts))
outer = matrix(c(0,0,10,0,10,10,0,10,0,0),ncol=2, byrow=TRUE)
hole1 = matrix(c(1,1,1,2,2,2,2,1,1,1),ncol=2, byrow=TRUE)
hole2 = matrix(c(5,5,5,6,6,6,6,5,5,5),ncol=2, byrow=TRUE)
pts = list(outer, hole1, hole2)
(ml1 = st_multilinestring(pts))
pts3 = lapply(pts, function(x) cbind(x, 0))
(ml2 = st_multilinestring(pts3))
(ml3 = st_multilinestring(pts3, "XYM"))
pts4 = lapply(pts3, function(x) cbind(x, 0))
(ml4 = st_multilinestring(pts4))
outer = matrix(c(0,0,10,0,10,10,0,10,0,0),ncol=2, byrow=TRUE)
hole1 = matrix(c(1,1,1,2,2,2,2,1,1,1),ncol=2, byrow=TRUE)
hole2 = matrix(c(5,5,5,6,6,6,6,5,5,5),ncol=2, byrow=TRUE)
pts = list(outer, hole1, hole2)
(pl1 = st_polygon(pts))
pts3 = lapply(pts, function(x) cbind(x, 0))
(pl2 = st_polygon(pts3))
(pl3 = st_polygon(pts3, "XYM"))
pts4 = lapply(pts3, function(x) cbind(x, 0))
(pl4 = st_polygon(pts4))
pol1 = list(outer, hole1, hole2)
pol2 = list(outer + 12, hole1 + 12)
pol3 = list(outer + 24)
mp = list(pol1,pol2,pol3)
(mp1 = st_multipolygon(mp))
pts3 = lapply(mp, function(x) lapply(x, function(y) cbind(y, 0)))
(mp2 = st_multipolygon(pts3))
(mp3 = st_multipolygon(pts3, "XYM"))
pts4 = lapply(mp2, function(x) lapply(x, function(y) cbind(y, 0)))
(mp4 = st_multipolygon(pts4))
(gc = st_geometrycollection(list(p1, ls1, pl1, mp1)))
st_geometrycollection() # empty geometry
c(st_point(1:2), st_point(5:6))
c(st_point(1:2), st_multipoint(matrix(5:8,2)))
c(st_multipoint(matrix(1:4,2)), st_multipoint(matrix(5:8,2)))
c(st_linestring(matrix(1:6,3)), st_linestring(matrix(11:16,3)))
c(st_multilinestring(list(matrix(1:6,3))), st_multilinestring(list(matrix(11:16,3))))
pl = list(rbind(c(0,0), c(1,0), c(1,1), c(0,1), c(0,0)))
c(st_polygon(pl), st_polygon(pl))
c(st_polygon(pl), st_multipolygon(list(pl)))
c(st_linestring(matrix(1:6,3)), st_point(1:2))
c(st_geometrycollection(list(st_point(1:2), st_linestring(matrix(1:6,3)))),
  st_geometrycollection(list(st_multilinestring(list(matrix(11:16,3))))))
c(st_geometrycollection(list(st_point(1:2), st_linestring(matrix(1:6,3)))),
  st_multilinestring(list(matrix(11:16,3))), st_point(5:6),
  st_geometrycollection(list(st_point(10:11))))