R version 4.2.1 (2022-06-23) -- "Funny-Looking Kid"
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> suppressPackageStartupMessages(library(sf))
> # nc = st_read(system.file("gpkg/nc.gpkg", package="sf"))
> nc = st_read(system.file("shape/nc.shp", package="sf"), quiet = TRUE)
> nc_checked = st_transform(nc, 32119, check = TRUE)
> ncm = st_transform(nc, 32119)
>
> x = st_transform(nc[1:10,], 32119)
> st_distance(x)
Units: [m]
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
[1,] 0.00 0.00 25652.53 440561.15 299772.18 361529.53 419671.2
[2,] 0.00 0.00 0.00 409428.54 268944.31 332589.77 388544.5
[3,] 25652.53 0.00 0.00 367555.48 227017.51 290297.10 346667.9
[4,] 440561.15 409428.54 367555.48 0.00 67226.86 45537.55 0.0
[5,] 299772.18 268944.31 227017.51 67226.86 0.00 0.00 46527.4
[6,] 361529.53 332589.77 290297.10 45537.55 0.00 0.00 30213.0
[7,] 419671.16 388544.52 346667.93 0.00 46527.40 30213.00 0.0
[8,] 384592.90 354294.11 312350.68 16130.34 11926.81 0.00 0.0
[9,] 262354.29 231217.49 189310.35 140455.87 0.00 64606.24 119563.7
[10,] 71139.29 41943.83 0.00 330751.39 190182.39 252372.06 309862.0
[,8] [,9] [,10]
[1,] 384592.90 262354.29 71139.29
[2,] 354294.11 231217.49 41943.83
[3,] 312350.68 189310.35 0.00
[4,] 16130.34 140455.87 330751.39
[5,] 11926.81 0.00 190182.39
[6,] 0.00 64606.24 252372.06
[7,] 0.00 119563.73 309861.97
[8,] 0.00 85533.12 275389.78
[9,] 85533.12 0.00 152488.92
[10,] 275389.78 152488.92 0.00
>
> st_is_valid(nc)
[1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
>
> st_is_empty(st_sfc(st_point(), st_linestring()))
[1] TRUE TRUE
>
> ops = c("intersects", #"disjoint",
+ "touches", "crosses", "within", "contains", "overlaps", "equals", "covers", "covered_by", "equals_exact")
> for (op in ops) {
+ x = sf:::st_geos_binop(op, ncm[1:50,], ncm[51:100,], 0, NA_character_, FALSE)
+ x = sf:::st_geos_binop(op, ncm[1:50,], ncm[51:100,], 0, NA_character_, TRUE)
+ }
>
> ops = c("intersects", #"disjoint",
+ "touches", "crosses", "within", "contains", "overlaps", "covers", "covered_by")
> df = data.frame(ops = ops)
> df$equal = NA
> for (op in ops)
+ df[df$ops == op, "equal"] = identical(
+ sf:::st_geos_binop(op, ncm[1:50,], ncm[51:100,], 0, NA_character_, TRUE, FALSE),
+ sf:::st_geos_binop(op, ncm[1:50,], ncm[51:100,], 0, NA_character_, TRUE, TRUE)
+ )
> df
ops equal
1 intersects TRUE
2 touches TRUE
3 crosses TRUE
4 within TRUE
5 contains TRUE
6 overlaps TRUE
7 covers TRUE
8 covered_by TRUE
>
> st_contains_properly(ncm[1:3,], ncm[1:3])
Sparse geometry binary predicate list of length 3, where the predicate
was `contains_properly'
1: (empty)
2: (empty)
3: (empty)
>
> st_combine(nc)
Geometry set for 1 feature
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
Geodetic CRS: NAD27
MULTIPOLYGON (((-81.47276 36.23436, -81.54084 3...
>
> st_dimension(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(1,1), c(0,1), c(0,0))))))
[1] 0 1 2
>
> ncbb = st_as_sfc(st_bbox(nc))
> g = st_make_grid(ncbb)
> x = st_intersection(nc, g)
Warning message:
attribute variables are assumed to be spatially constant throughout all geometries
> x = st_intersection(g, nc)
>
> ls = st_sfc(st_linestring(rbind(c(0,0),c(0,1))),
+ st_linestring(rbind(c(0,0),c(10,0))))
>
> suppressWarnings(RNGversion("3.5.3"))
> set.seed(13531)
>
> st_line_sample(ls, density = 1, type = "random")
Geometry set for 2 features
Geometry type: MULTIPOINT
Dimension: XY
Bounding box: xmin: 0 ymin: 0 xmax: 6.880179 ymax: 0.8878369
CRS: NA
MULTIPOINT ((0 0.8878369))
MULTIPOINT ((0.2986488 0), (2.48417 0), (2.5678...
>
> g = st_make_grid(ncbb, n = c(20,10))
>
> a1 = st_interpolate_aw(nc["BIR74"], g, FALSE)
Warning message:
In st_interpolate_aw.sf(nc["BIR74"], g, FALSE) :
st_interpolate_aw assumes attributes are constant or uniform over areas of x
> sum(a1$BIR74) / sum(nc$BIR74) # not close to one: property is assumed spatially intensive
[1] 1.436123
> a2 = st_interpolate_aw(nc["BIR74"], g, extensive = TRUE)
Warning message:
In st_interpolate_aw.sf(nc["BIR74"], g, extensive = TRUE) :
st_interpolate_aw assumes attributes are constant or uniform over areas of x
> sum(a2$BIR74) / sum(nc$BIR74)
[1] 1
>
> # missing x:
> g = st_make_grid(offset = c(0,0), cellsize = c(1,1), n = c(10,10))
> g = st_make_grid(what = "centers")
> length(g)
[1] 648
> g = st_make_grid(what = "corners")
> length(g)
[1] 703
>
> g1 = st_make_grid(ncbb, 0.1, what = "polygons", square = FALSE)
> g2 = st_make_grid(ncbb, 0.1, what = "points", square = FALSE)
>
> # st_line_merge:
> mls = st_multilinestring(list(rbind(c(0,0), c(1,1)), rbind(c(2,0), c(1,1))))
> st_line_merge(mls)
LINESTRING (0 0, 1 1, 2 0)
>
> if (isTRUE(try(compareVersion(sf_extSoftVersion()["GEOS"], "3.5.0") > -1, silent = TRUE))) {
+ # voronoi:
+ 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))))
+ 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)
+
+ v = st_voronoi(x)
+ print(class(v))
+ v = st_sfc(st_voronoi(st_sfc(x)))
+ print(class(v))
+ v = st_voronoi(st_sf(a = 1, geom = st_sfc(x)))
+ print(class(v))
+ }
[1] "XY" "GEOMETRYCOLLECTION" "sfg"
[1] "sfc_GEOMETRYCOLLECTION" "sfc"
[1] "sf" "data.frame"
>
> i = st_intersects(ncm, ncm[1:88,])
> all.equal(i, t(t(i)))
[1] TRUE
>
> # check use of pattern in st_relate:
> sfc = st_as_sfc(st_bbox(st_sfc(st_point(c(0,0)), st_point(c(3,3)))))
> grd = st_make_grid(sfc, n = c(3,3))
> st_intersects(grd)
Sparse geometry binary predicate list of length 9, where the predicate
was `intersects'
1: 1, 2, 4, 5
2: 1, 2, 3, 4, 5, 6
3: 2, 3, 5, 6
4: 1, 2, 4, 5, 7, 8
5: 1, 2, 3, 4, 5, 6, 7, 8, 9
6: 2, 3, 5, 6, 8, 9
7: 4, 5, 7, 8
8: 4, 5, 6, 7, 8, 9
9: 5, 6, 8, 9
> st_relate(grd, pattern = "****1****")
Sparse geometry binary predicate list of length 9, where the predicate
was `relate_pattern'
1: 1, 2, 4
2: 1, 2, 3, 5
3: 2, 3, 6
4: 1, 4, 5, 7
5: 2, 4, 5, 6, 8
6: 3, 5, 6, 9
7: 4, 7, 8
8: 5, 7, 8, 9
9: 6, 8, 9
> st_relate(grd, pattern = "****0****")
Sparse geometry binary predicate list of length 9, where the predicate
was `relate_pattern'
1: 5
2: 4, 6
3: 5
4: 2, 8
5: 1, 3, 7, 9
6: 2, 8
7: 5
8: 4, 6
9: 5
> st_rook = function(a, b = a, ...) st_relate(a, b, pattern = "F***1****", ...)
> st_rook(grd, sparse = FALSE)
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
[1,] FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
[2,] TRUE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE
[3,] FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
[4,] TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE
[5,] FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE
[6,] FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE TRUE
[7,] FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE
[8,] FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE
[9,] FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE
>
> #if (Sys.getenv("USER") %in% c("edzer", "travis")) { # memory leaks:
> try(st_relate(st_point(), st_point(), pattern = "FF*FF****")) # error: use st_disjoint
Error in CPL_geos_binop(st_geometry(x), st_geometry(y), op, par, pattern, :
use st_disjoint for this pattern
> #}
>
> a = st_is_within_distance(nc[c(1:3,20),], nc[1:3,], 100000, sparse = FALSE)
> b = st_is_within_distance(nc[c(1:3,20),], nc[1:3,], units::set_units(100000, m), sparse = FALSE)
> all.equal(a, b)
[1] TRUE
> x = st_is_within_distance(nc[1:3,], nc[1:5,], 100000)
> y = st_is_within_distance(nc[1:3,], nc[1:5,], units::set_units(100, km))
> all.equal(x, y)
[1] TRUE
>
> nc_3857 = st_transform(nc, 3857)
> a = st_is_within_distance(nc_3857[c(1:3,20),], nc_3857[1:3,], 100000, sparse = FALSE)
> b = st_is_within_distance(nc_3857[c(1:3,20),], nc_3857[1:3,], units::set_units(100000, m), sparse = FALSE)
> all.equal(a, b)
[1] TRUE
> x = st_is_within_distance(nc_3857, nc_3857, 100000)
> y = st_is_within_distance(nc_3857, nc_3857, units::set_units(100, km))
> all.equal(x, y)
[1] TRUE
>
> pe = st_sfc(st_point())
> p = st_sfc(st_point(c(0,0)), st_point(c(0,1)), st_point(c(0,2)))
> st_distance(p, p)
[,1] [,2] [,3]
[1,] 0 1 2
[2,] 1 0 1
[3,] 2 1 0
> st_distance(p, pe)
[,1]
[1,] NA
[2,] NA
[3,] NA
> st_distance(p, p, by_element = TRUE)
[1] 0 0 0
> st_crs(p) = 4326
> st_distance(p, p[c(2,3,1)], by_element = TRUE)
Units: [m]
[1] 111195.1 111195.1 222390.2
> p = st_transform(p, 3587)
> st_distance(p, p[c(2,3,1)], by_element = TRUE)
Units: [m]
[1] 144589.5 142873.3 287462.8
>
> # from https://github.com/r-spatial/sf/issues/458 :
> 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))))
> pol_df <- data.frame(id = 1)
> st_geometry(pol_df) <- st_sfc(pol)
> st_intersects(pts, pol_df[pol_df$id == 2,]) # with empty sf/sfc
Sparse geometry binary predicate list of length 3, where the predicate
was `intersects'
1: (empty)
2: (empty)
3: (empty)
> st_intersects(pts, pol_df[pol_df$id == 2,], sparse = FALSE) # with empty sf/sfc
[1,]
[2,]
[3,]
>
> # st_node
> l = st_linestring(rbind(c(0,0), c(1,1), c(0,1), c(1,0), c(0,0)))
> st_node(l)
MULTILINESTRING ((0 0, 0.5 0.5), (0.5 0.5, 1 1, 0 1, 0.5 0.5), (0.5 0.5, 1 0, 0 0))
> st_node(st_sfc(l))
Geometry set for 1 feature
Geometry type: MULTILINESTRING
Dimension: XY
Bounding box: xmin: 0 ymin: 0 xmax: 1 ymax: 1
CRS: NA
MULTILINESTRING ((0 0, 0.5 0.5), (0.5 0.5, 1 1,...
> st_node(st_sf(a = 1, st_sfc(l)))
Simple feature collection with 1 feature and 1 field
Geometry type: MULTILINESTRING
Dimension: XY
Bounding box: xmin: 0 ymin: 0 xmax: 1 ymax: 1
CRS: NA
a st_sfc.l.
1 1 MULTILINESTRING ((0 0, 0.5 ...
>
> # print.sgbp:
> (lst = st_disjoint(nc, nc))
Sparse geometry binary predicate list of length 100, where the
predicate was `disjoint'
first 10 elements:
1: 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, ...
2: 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, ...
3: 1, 4, 5, 6, 7, 8, 9, 11, 12, 13, ...
4: 1, 2, 3, 5, 6, 8, 9, 10, 11, 12, ...
5: 1, 2, 3, 4, 7, 8, 10, 11, 12, 13, ...
6: 1, 2, 3, 4, 7, 9, 10, 11, 12, 13, ...
7: 1, 2, 3, 5, 6, 9, 10, 11, 12, 13, ...
8: 1, 2, 3, 4, 5, 9, 10, 11, 12, 13, ...
9: 1, 2, 3, 4, 6, 7, 8, 10, 11, 12, ...
10: 1, 2, 4, 5, 6, 7, 8, 9, 11, 13, ...
> # dim.sgbp:
> dim(lst)
[1] 100 100
> # as.matrix.sgbp:
> as.matrix(lst)[1:5, 1:5]
[,1] [,2] [,3] [,4] [,5]
[1,] FALSE FALSE TRUE TRUE TRUE
[2,] FALSE FALSE FALSE TRUE TRUE
[3,] TRUE FALSE FALSE TRUE TRUE
[4,] TRUE TRUE TRUE FALSE TRUE
[5,] TRUE TRUE TRUE TRUE FALSE
> # negate:
> !lst
Sparse geometry binary predicate list of length 100, where the
predicate was `!disjoint'
first 10 elements:
1: 1, 2, 18, 19
2: 1, 2, 3, 18
3: 2, 3, 10, 18, 23, 25
4: 4, 7, 56
5: 5, 6, 9, 16, 28
6: 5, 6, 8, 28
7: 4, 7, 8, 17
8: 6, 7, 8, 17, 20, 21
9: 5, 9, 15, 16, 24, 31
10: 3, 10, 12, 25, 26
> # as.data.frame:
> head(as.data.frame(lst), 10)
row.id col.id
1 1 3
2 1 4
3 1 5
4 1 6
5 1 7
6 1 8
7 1 9
8 1 10
9 1 11
10 1 12
>
> # snap:
> nc1 = st_transform(nc, 32119)
> g = st_make_grid(nc1, c(5000,5000), what = "centers")
> s = st_snap(nc1[1:3,], g, 2501*sqrt(2))
> sfg = st_snap(st_geometry(nc1)[[1]], g, 2501*sqrt(2))
> sfg = st_snap(st_geometry(nc1)[[1]], st_combine(g), 2501*sqrt(2))
>
> # Hausdorff distance: http://geos.refractions.net/ro/doxygen_docs/html/classgeos_1_1algorithm_1_1distance_1_1DiscreteHausdorffDistance.html
> A = st_as_sfc("LINESTRING (0 0, 100 0, 10 100, 10 100)")
> B = st_as_sfc("LINESTRING (0 100, 0 10, 80 10)")
> st_distance(c(A,B))
[,1] [,2]
[1,] 0.000000 8.176236
[2,] 8.176236 0.000000
> st_distance(c(A,B), which = "Hausdorff")
[,1] [,2]
[1,] 0.00000 22.36068
[2,] 22.36068 0.00000
> st_distance(c(A,B), which = "Hausdorff", par = 0.001)
[,1] [,2]
[1,] 2.929643e-14 4.789000e+01
[2,] 4.789000e+01 2.131628e-14
> LE = st_as_sfc("LINESTRING EMPTY")
> st_distance(c(A, LE), which = "Hausdorff", par = 0.001)
[,1] [,2]
[1,] 2.929643e-14 NA
[2,] NA NA
>
> # one-argument st_intersection and st_difference:
> set.seed(131)
> 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))
> d = st_difference(s) # sequential differences: s1, s2-s1, s3-s2-s1, ...
> plot(d, col = sf.colors(categorical = TRUE, alpha = .5))
> i = st_intersection(s) # all intersections
> plot(i, col = sf.colors(categorical = TRUE, alpha = .5))
> summary(lengths(st_overlaps(s, s)))
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 2.00 3.50 3.66 5.00 8.00
> summary(lengths(st_overlaps(d, d)))
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 0 0 0
> summary(lengths(st_overlaps(i, i)))
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 0 0 0
>
> sf = st_sf(s)
> i = st_intersection(sf) # all intersections
> plot(i["n.overlaps"])
> summary(i$n.overlaps - lengths(i$origins))
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 0 0 0
>
> # st_nearest_points:
> pt1 = st_point(c(.1,.1))
> pt2 = st_point(c(.9,.9))
> b1 = st_buffer(pt1, 0.1)
> b2 = st_buffer(pt2, 0.1)
> plot(b1, xlim = c(0,1), ylim = c(0,1))
> plot(b2, add = TRUE)
> (ls0 = try(st_nearest_points(b1, b2))) # sfg
Geometry set for 1 feature
Geometry type: LINESTRING
Dimension: XY
Bounding box: xmin: 0.1707107 ymin: 0.1707107 xmax: 0.8292893 ymax: 0.8292893
CRS: NA
LINESTRING (0.1707107 0.1707107, 0.8292893 0.82...
> (ls = try(st_nearest_points(st_sfc(b1), st_sfc(b2)))) # sfc
Geometry set for 1 feature
Geometry type: LINESTRING
Dimension: XY
Bounding box: xmin: 0.1707107 ymin: 0.1707107 xmax: 0.8292893 ymax: 0.8292893
CRS: NA
LINESTRING (0.1707107 0.1707107, 0.8292893 0.82...
> (ls = try(st_nearest_points(st_sfc(b1), st_sfc(b2), pairwise = TRUE))) # sfc
Geometry set for 1 feature
Geometry type: LINESTRING
Dimension: XY
Bounding box: xmin: 0.1707107 ymin: 0.1707107 xmax: 0.8292893 ymax: 0.8292893
CRS: NA
LINESTRING (0.1707107 0.1707107, 0.8292893 0.82...
> identical(ls0, ls)
[1] TRUE
> # plot(ls, add = TRUE, col = 'red')
>
> nc = read_sf(system.file("gpkg/nc.gpkg", package="sf"))
> plot(st_geometry(nc))
> ls = try(st_nearest_points(nc[1,], nc))
> # plot(ls, col = 'red', add = TRUE)
> pts = st_cast(ls, "POINT") # gives all start & end points
There were 50 or more warnings (use warnings() to see the first 50)
> # starting, "from" points, corresponding to x:
> plot(pts[seq(1, 200, 2)], add = TRUE, col = 'blue')
> # ending, "to" points, corresponding to y:
> plot(pts[seq(2, 200, 2)], add = TRUE, col = 'red')
>
> # points to nearest features
> ls1 = st_linestring(rbind(c(0,0), c(1,0)))
> ls2 = st_linestring(rbind(c(0,0.1), c(1,0.1)))
> ls3 = st_linestring(rbind(c(0,1), c(1,1)))
> (l = st_sfc(ls1, ls2, ls3))
Geometry set for 3 features
Geometry type: LINESTRING
Dimension: XY
Bounding box: xmin: 0 ymin: 0 xmax: 1 ymax: 1
CRS: NA
LINESTRING (0 0, 1 0)
LINESTRING (0 0.1, 1 0.1)
LINESTRING (0 1, 1 1)
>
> p1 = st_point(c(0.1, -0.1))
> p2 = st_point(c(0.1, 0.11))
> p3 = st_point(c(0.1, 0.09))
> p4 = st_point(c(0.1, 0.9))
> p5 = st_point()
>
> (p = st_sfc(p1, p2, p3, p4, p5))
Geometry set for 5 features (with 1 geometry empty)
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 0.1 ymin: -0.1 xmax: 0.1 ymax: 0.9
CRS: NA
POINT (0.1 -0.1)
POINT (0.1 0.11)
POINT (0.1 0.09)
POINT (0.1 0.9)
POINT EMPTY
> #st_nearest_points(p, l)
> n = try(st_nearest_feature(p,l))
> if (!inherits(n, "try-error")) {
+ print(st_nearest_points(p, l[n], pairwise = TRUE))
+ print(st_nearest_feature(p, l))
+ print(st_nearest_feature(p, st_sfc()))
+ print(st_nearest_feature(st_sfc(), l))
+ }
Geometry set for 5 features (with 1 geometry empty)
Geometry type: LINESTRING
Dimension: XY
Bounding box: xmin: 0.1 ymin: -0.1 xmax: 0.1 ymax: 1
CRS: NA
LINESTRING (0.1 -0.1, 0.1 0)
LINESTRING (0.1 0.11, 0.1 0.1)
LINESTRING (0.1 0.09, 0.1 0.1)
LINESTRING (0.1 0.9, 0.1 1)
LINESTRING EMPTY
[1] 1 2 2 3 NA
[1] NA NA NA NA NA
integer(0)
>
> # can do centroid of empty geom:
> st_centroid(st_polygon())
POINT EMPTY
>
> #999:
> pt = data.frame(x=1:2, y=1:2,a=letters[1:2])
> pt = st_as_sf(pt, coords=c("x","y"))
>
> bf =st_buffer(pt, dist=0.3)
>
> st_within(pt,bf, sparse=FALSE)
[,1] [,2]
[1,] TRUE FALSE
[2,] FALSE TRUE
> st_within(pt[1,], bf[1,], sparse = FALSE)
[,1]
[1,] TRUE
> st_relate(pt[1,], bf[1,], pattern = "T*F**F***", sparse = FALSE)
[,1]
[1,] TRUE
>
> sf:::is_symmetric(pattern = "010121010")
[1] TRUE
> sf:::is_symmetric(pattern = "010121021")
[1] FALSE
>
> st_intersects(st_point(0:1), st_point(2:3)) # sfg method
Sparse geometry binary predicate list of length 1, where the predicate
was `intersects'
1: (empty)
>
> if (isTRUE(try(compareVersion(sf_extSoftVersion()["GEOS"], "3.7.0") > -1, silent = TRUE))) {
+ ls = st_linestring(rbind(c(1,1), c(2,2), c(3,3)))
+ print(st_reverse(ls))
+ print(st_reverse(st_sfc(ls)))
+ print(st_reverse(st_sf(a = 2, geom = st_sfc(ls))))
+ }
LINESTRING (3 3, 2 2, 1 1)
Geometry set for 1 feature
Geometry type: LINESTRING
Dimension: XY
Bounding box: xmin: 1 ymin: 1 xmax: 3 ymax: 3
CRS: NA
LINESTRING (3 3, 2 2, 1 1)
Simple feature collection with 1 feature and 1 field
Geometry type: LINESTRING
Dimension: XY
Bounding box: xmin: 1 ymin: 1 xmax: 3 ymax: 3
CRS: NA
a geom
1 2 LINESTRING (3 3, 2 2, 1 1)
>
> p = st_polygon(list(rbind(c(0,0), c(1,0), c(1,1), c(0,1), c(0,0))))
> y = st_sfc(p)
> x = st_sfc(p + 1.001)
>
> x %>% st_set_precision(0) %>% st_intersects(y)
Sparse geometry binary predicate list of length 1, where the predicate
was `intersects'
1: (empty)
> x %>% st_set_precision(10000) %>% st_intersects(y)
Sparse geometry binary predicate list of length 1, where the predicate
was `intersects'
1: (empty)
> x %>% st_set_precision(1000) %>% st_intersects(y)
Sparse geometry binary predicate list of length 1, where the predicate
was `intersects'
1: (empty)
> x %>% st_set_precision(501) %>% st_intersects(y) # no
Sparse geometry binary predicate list of length 1, where the predicate
was `intersects'
1: (empty)
> x %>% st_set_precision(500) %>% st_intersects(y) # yes
Sparse geometry binary predicate list of length 1, where the predicate
was `intersects'
1: 1
> x %>% st_set_precision(100) %>% st_intersects(y)
Sparse geometry binary predicate list of length 1, where the predicate
was `intersects'
1: 1
> x %>% st_set_precision(10) %>% st_intersects(y)
Sparse geometry binary predicate list of length 1, where the predicate
was `intersects'
1: 1
>
> 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)
Sparse geometry binary predicate list of length 8, where the predicate
was `equals'
1: 1, 2, 4, 5, 8
2: 1, 2, 4, 5, 8
3: 3, 6, 7
4: 1, 2, 4, 5, 8
5: 1, 2, 4, 5, 8
6: 3, 6, 7
7: 3, 6, 7
8: 1, 2, 4, 5, 8
> st_equals(p, remove_self = TRUE)
Sparse geometry binary predicate list of length 8, where the predicate
was `equals', with remove_self = TRUE
1: 2, 4, 5, 8
2: 1, 4, 5, 8
3: 6, 7
4: 1, 2, 5, 8
5: 1, 2, 4, 8
6: 3, 7
7: 3, 6
8: 1, 2, 4, 5
> (u = st_equals(p, retain_unique = TRUE))
Sparse geometry binary predicate list of length 8, where the predicate
was `equals', with retain_unique = TRUE
1: 2, 4, 5, 8
2: 4, 5, 8
3: 6, 7
4: 5, 8
5: 8
6: 7
7: (empty)
8: (empty)
> # retain the records with unique geometries:
> p[-unlist(u),]
Simple feature collection with 2 features and 1 field
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 0 ymin: 1 xmax: 2 ymax: 1
CRS: NA
a geom
1 a POINT (0 1)
3 c POINT (2 1)
>
> proc.time()
user system elapsed
10.184 0.779 10.082
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