The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
#include "geometries/geometries.hpp"SEXP make_geometries()When one thinks of geometries in R, one of the most
common data structures is the matrix. For example, in the
sf world, an POINT is a single-row matrix (i.e, a
vector)
sf::st_point( 1:2 )
POINT (1 2)A LINESTRING is a matrix
sf::st_linestring( matrix( c(1,1,1,2,2,2,2,1,1,1), ncol = 2, byrow = T) )
LINESTRING (1 1, 1 2, 2 2, 2 1, 1 1)and a POLYGON is a list of matrices
sf::st_polygon( list( matrix( c(1,1,1,2,2,2,2,1,1,1), ncol = 2, byrow = T) ) )
POLYGON ((1 1, 1 2, 2 2, 2 1, 1 1))And to group these into a collection you would put each geometry inside a list
sf::st_sfc(
list(
sf::st_linestring( matrix( c(1,1,1,2,2,2,2,1,1,1), ncol = 2, byrow = T) )
, sf::st_polygon( list( matrix( c(1,1,1,2,2,2,2,1,1,1), ncol = 2, byrow = T) ) )
)
)
Geometry set for 2 features
geometry type: GEOMETRY
dimension: XY
bbox: xmin: 1 ymin: 1 xmax: 2 ymax: 2
CRS: NA
LINESTRING (1 1, 1 2, 2 2, 2 1, 1 1)
POLYGON ((1 1, 1 2, 2 2, 2 1, 1 1))From my limited research (i.e., practically none), I estimate most
users will have a data.frame and will want to convert it
into a collection of geometries.
For example, take a data.frame of x and
y coordinates, and two id columns.
df <- data.frame(
id1 = c( rep(1,12), rep(2, 12) )
, id2 = c( rep(1:4, each = 3), rep(1:4, each = 3) )
, x = 1:24
, y = 24:1
)
df
# id1 id2 x y
# 1 1 1 1 24
# 2 1 1 2 23
# 3 1 1 3 22
# 4 1 2 4 21
# 5 1 2 5 20
# 6 1 2 6 19
# 7 1 3 7 18
# 8 1 3 8 17
# 9 1 3 9 16
# 10 1 4 10 15
# 11 1 4 11 14
# 12 1 4 12 13
# 13 2 1 13 12
# 14 2 1 14 11
# 15 2 1 15 10
# 16 2 2 16 9
# 17 2 2 17 8
# 18 2 2 18 7
# 19 2 3 19 6
# 20 2 3 20 5
# 21 2 3 21 4
# 22 2 4 22 3
# 23 2 4 23 2
# 24 2 4 24 1You can think of the ID columns in this example as
id1 defines a polygonid2 is each ring inside the polygonCalling geometries::make_geometries() will split this
data.frame by these ID columns and put the resulting matrices inside
list elements.
cppFunction(
depends = "geometries"
, includes = '#include "geometries/geometries.hpp"'
, code = '
SEXP my_shape( SEXP df, SEXP id_cols, SEXP geometry_cols ) {
return geometries::make_geometries( df, id_cols, geometry_cols );
}
'
, plugins = "cpp11"
)
my_shape( df, c(0L,1L), c(2L,3L) )
# [[1]]
# [[1]][[1]]
# [,1] [,2]
# [1,] 1 24
# [2,] 2 23
# [3,] 3 22
#
# [[1]][[2]]
# [,1] [,2]
# [1,] 4 21
# [2,] 5 20
# [3,] 6 19
#
# [[1]][[3]]
# [,1] [,2]
# [1,] 7 18
# [2,] 8 17
# [3,] 9 16
#
# [[1]][[4]]
# [,1] [,2]
# [1,] 10 15
# [2,] 11 14
# [3,] 12 13
#
#
# [[2]]
# [[2]][[1]]
# [,1] [,2]
# [1,] 13 12
# [2,] 14 11
# [3,] 15 10
#
# [[2]][[2]]
# [,1] [,2]
# [1,] 16 9
# [2,] 17 8
# [3,] 18 7
#
# [[2]][[3]]
# [,1] [,2]
# [1,] 19 6
# [2,] 20 5
# [3,] 21 4
#
# [[2]][[4]]
# [,1] [,2]
# [1,] 22 3
# [2,] 23 2
# [3,] 24 1Notice here there are no class attributes on the shapes. In
{geometries} I only want to provide the tools to build
these structures, then each user can define what they mean.
For example, if you want to define a class for each geometry you can supply a list containing a “class” vector as the 4th argument
cppFunction(
depends = "geometries"
, includes = '#include "geometries/geometries.hpp"'
, code = '
SEXP my_shape( Rcpp::DataFrame df, Rcpp::IntegerVector id_cols, Rcpp::IntegerVector geometry_cols, Rcpp::List class_attributes ) {
return geometries::make_geometries( df, id_cols, geometry_cols, class_attributes );
}
'
, plugins = "cpp11"
)
my_shape( df, c(0,1), c(2,3), list(class = "my_polygon") )
# [[1]]
# [[1]]
# [,1] [,2]
# [1,] 1 24
# [2,] 2 23
# [3,] 3 22
#
# [[2]]
# [,1] [,2]
# [1,] 4 21
# [2,] 5 20
# [3,] 6 19
#
# [[3]]
# [,1] [,2]
# [1,] 7 18
# [2,] 8 17
# [3,] 9 16
#
# [[4]]
# [,1] [,2]
# [1,] 10 15
# [2,] 11 14
# [3,] 12 13
#
# attr(,"class")
# [1] "my_polygon"
#
# [[2]]
# [[1]]
# [,1] [,2]
# [1,] 13 12
# [2,] 14 11
# [3,] 15 10
#
# [[2]]
# [,1] [,2]
# [1,] 16 9
# [2,] 17 8
# [3,] 18 7
#
# [[3]]
# [,1] [,2]
# [1,] 19 6
# [2,] 20 5
# [3,] 21 4
#
# [[4]]
# [,1] [,2]
# [1,] 22 3
# [2,] 23 2
# [3,] 24 1
#
# attr(,"class")
# [1] "my_polygon"Notice here that each list element now has a
"my_polygon" class.
And if you have library(sf) loaded, setting the class as
sfg POLYGON, you should see each element
printed in the usual sf way
library(sf)
my_shape( df, c(0,1), c(2,3), list( class = c("XY", "POLYGON","sfg") ) )
# [[1]]
# POLYGON ((1 24, 2 23, 3 22), (4 21, 5 20, 6 19), (7 18, 8 17, 9 16), (10 15, 11 14, 12 13))
#
# [[2]]
# POLYGON ((13 12, 14 11, 15 10), (16 9, 17 8, 18 7), (19 6, 20 5, 21 4), (22 3, 23 2, 24 1))
You can use this function to define any shape you want. The number of
id columns you supply will determine how deeply nested the
matrices are. If I add two more id columns, this will nest
each matrix 2-levels deeper
df$id0 <- 1
df$id00 <- 1
head( df )
my_shape( df, c(0,1,4,5), c(2,3), list(class = "my_new_shape") )
# [[1]]
# [[1]]
# [[1]][[1]]
# [[1]][[1]][[1]]
# [,1] [,2]
# [1,] 1 24
# [2,] 2 23
# [3,] 3 22
#
#
#
# [[2]]
# [[2]][[1]]
# [[2]][[1]][[1]]
# [,1] [,2]
# [1,] 4 21
# [2,] 5 20
# [3,] 6 19
#
#
#
# [[3]]
# [[3]][[1]]
# [[3]][[1]][[1]]
# [,1] [,2]
# [1,] 7 18
# [2,] 8 17
# [3,] 9 16
#
#
#
# [[4]]
# [[4]][[1]]
# [[4]][[1]][[1]]
# [,1] [,2]
# [1,] 10 15
# [2,] 11 14
# [3,] 12 13
#
#
#
# attr(,"class")
# [1] "my_new_shape"
#
# [[2]]
# [[1]]
# [[1]][[1]]
# [[1]][[1]][[1]]
# [,1] [,2]
# [1,] 13 12
# [2,] 14 11
# [3,] 15 10
#
#
#
# [[2]]
# [[2]][[1]]
# [[2]][[1]][[1]]
# [,1] [,2]
# [1,] 16 9
# [2,] 17 8
# [3,] 18 7
#
#
#
# [[3]]
# [[3]][[1]]
# [[3]][[1]][[1]]
# [,1] [,2]
# [1,] 19 6
# [2,] 20 5
# [3,] 21 4
#
#
#
# [[4]]
# [[4]][[1]]
# [[4]][[1]][[1]]
# [,1] [,2]
# [1,] 22 3
# [2,] 23 2
# [3,] 24 1
#
#
#
# attr(,"class")
# [1] "my_new_shape"These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.