This vignetted describes how simple feature geometries can be manipulated, where manipulations include
POLYGON
to MULTIPOLYGON
)This sections discusses how simple feature geometries of one type can be converted to another. For converting lines to polygons, see also st_polygonize
below.
For single geometries, st_cast
will
LINESTRING
to MULTILINESTRING
Examples of the first three types are
library(sf)
suppressPackageStartupMessages(library(dplyr))
st_point(c(1,1)) %>% st_cast("MULTIPOINT")
## MULTIPOINT (1 1)
st_multipoint(rbind(c(1,1))) %>% st_cast("POINT")
## Warning in st_cast.MULTIPOINT(., "POINT"): point from first coordinate only
## POINT (1 1)
st_multipoint(rbind(c(1,1),c(2,2))) %>% st_cast("POINT")
## Warning in st_cast.MULTIPOINT(., "POINT"): point from first coordinate only
## POINT (1 1)
Examples of the fourth type are
st_geometrycollection(list(st_point(c(1,1)))) %>% st_cast("POINT")
## POINT (1 1)
It should be noted here that when reading geometries using st_read
, the type
argument can be used to control the class of the returned geometry:
shp = system.file("shape/nc.shp", package="sf")
class(st_geometry(st_read(shp, quiet = TRUE)))
## [1] "sfc_MULTIPOLYGON" "sfc"
class(st_geometry(st_read(shp, quiet = TRUE, type = 3)))
## [1] "sfc_POLYGON" "sfc"
class(st_geometry(st_read(shp, quiet = TRUE, type = 1)))
## [1] "sfc_GEOMETRY" "sfc"
This option is handled by the GDAL library; in case of failure to convert to the target type, the original types are returned, which in this case is a mix of POLYGON
and MULTIPOLYGON
geometries, leading to a GEOMETRY
as superclass. When we try to read multipolygons as polygons, all secondary rings of multipolygons get lost.
When functions return objects with mixed geometry type (GEOMETRY
), downstream functions such as st_write
may have difficulty handling them. For some of these cases, st_cast
may help modifying their type. For sets of geometry objects (sfc
) and simple feature sets (sf),
st_cast` can be used by specifying the target type, or without specifying it.
ls <- st_linestring(rbind(c(0,0),c(1,1),c(2,1)))
mls <- st_multilinestring(list(rbind(c(2,2),c(1,3)), rbind(c(0,0),c(1,1),c(2,1))))
(sfc <- st_sfc(ls,mls))
## Geometry set for 2 features
## geometry type: GEOMETRY
## dimension: XY
## bbox: xmin: 0 ymin: 0 xmax: 2 ymax: 3
## epsg (SRID): NA
## proj4string: NA
## LINESTRING (0 0, 1 1, 2 1)
## MULTILINESTRING ((2 2, 1 3), (0 0, 1 1, 2 1))
st_cast(sfc, "MULTILINESTRING")
## Geometry set for 2 features
## geometry type: MULTILINESTRING
## dimension: XY
## bbox: xmin: 0 ymin: 0 xmax: 2 ymax: 3
## epsg (SRID): NA
## proj4string: NA
## MULTILINESTRING ((0 0, 1 1, 2 1))
## MULTILINESTRING ((2 2, 1 3), (0 0, 1 1, 2 1))
sf <- st_sf(a = 5:4, geom = sfc)
st_cast(sf, "MULTILINESTRING")
## Simple feature collection with 2 features and 1 field
## geometry type: MULTILINESTRING
## dimension: XY
## bbox: xmin: 0 ymin: 0 xmax: 2 ymax: 3
## epsg (SRID): NA
## proj4string: NA
## a geometry
## 1 5 MULTILINESTRING ((0 0, 1 1,...
## 2 4 MULTILINESTRING ((2 2, 1 3)...
When no target type is given, st_cast
tries to be smart for two cases:
GEOMETRY
, and all elements are of identical type, andGEOMETRYCOLLECTION
objects, in which case GEOMETRYCOLLECTION
objects are replaced by their content (which may be a GEOMETRY
mix again)Examples are:
ls <- st_linestring(rbind(c(0,0),c(1,1),c(2,1)))
mls1 <- st_multilinestring(list(rbind(c(2,2),c(1,3)), rbind(c(0,0),c(1,1),c(2,1))))
mls2 <- st_multilinestring(list(rbind(c(4,4),c(4,3)), rbind(c(2,2),c(2,1),c(3,1))))
(sfc <- st_sfc(ls,mls1,mls2))
## Geometry set for 3 features
## geometry type: GEOMETRY
## dimension: XY
## bbox: xmin: 0 ymin: 0 xmax: 4 ymax: 4
## epsg (SRID): NA
## proj4string: NA
## LINESTRING (0 0, 1 1, 2 1)
## MULTILINESTRING ((2 2, 1 3), (0 0, 1 1, 2 1))
## MULTILINESTRING ((4 4, 4 3), (2 2, 2 1, 3 1))
class(sfc[2:3])
## [1] "sfc_MULTILINESTRING" "sfc"
class(st_cast(sfc[2:3]))
## [1] "sfc_MULTILINESTRING" "sfc"
gc1 <- st_geometrycollection(list(st_linestring(rbind(c(0,0),c(1,1),c(2,1)))))
gc2 <- st_geometrycollection(list(st_multilinestring(list(rbind(c(2,2),c(1,3)), rbind(c(0,0),c(1,1),c(2,1))))))
gc3 <- st_geometrycollection(list(st_multilinestring(list(rbind(c(4,4),c(4,3)), rbind(c(2,2),c(2,1),c(3,1))))))
(sfc <- st_sfc(gc1,gc2,gc3))
## Geometry set for 3 features
## geometry type: GEOMETRYCOLLECTION
## dimension: XY
## bbox: xmin: 0 ymin: 0 xmax: 4 ymax: 4
## epsg (SRID): NA
## proj4string: NA
## GEOMETRYCOLLECTION (LINESTRING (0 0, 1 1, 2 1))
## GEOMETRYCOLLECTION (MULTILINESTRING ((2 2, 1 3)...
## GEOMETRYCOLLECTION (MULTILINESTRING ((4 4, 4 3)...
class(st_cast(sfc))
## [1] "sfc_GEOMETRY" "sfc"
class(st_cast(st_cast(sfc), "MULTILINESTRING"))
## [1] "sfc_MULTILINESTRING" "sfc"
Affine transformations are transformations of the type \(f(x) = xA + b\), where matrix \(A\) is used to flatten, scale and/or rotate, and \(b\) to translate \(x\). Low-level examples are:
(p = st_point(c(0,2)))
## POINT (0 2)
p + 1
## POINT (1 3)
p + c(1,2)
## POINT (1 4)
p + p
## POINT (0 4)
p * p
## POINT (0 4)
rot = function(a) matrix(c(cos(a), sin(a), -sin(a), cos(a)), 2, 2)
p * rot(pi/4)
## POINT (1.414214 1.414214)
p * rot(pi/2)
## POINT (2 1.224647e-16)
p * rot(pi)
## POINT (2.449294e-16 -2)
Just to make the point, we can for instance rotate the counties of North Carolina 90 degrees clockwise around their centroid, and shrink them to 75% of their original size:
nc = st_read(system.file("shape/nc.shp", package="sf"), quiet = TRUE)
ncg = st_geometry(nc)
plot(ncg, border = 'grey')
cntrd = st_centroid(ncg)
## Warning in st_centroid.sfc(ncg): st_centroid does not give correct
## centroids for longitude/latitude data
ncg2 = (ncg - cntrd) * rot(pi/2) * .75 + cntrd
plot(ncg2, add = TRUE)
plot(cntrd, col = 'red', add = TRUE, cex = .5)