spatSample,GRaster-method | R Documentation |
spatSample()
randomly locates points across a GRaster
or GVector
. It can return a GVector
, the coordinates, values associated with the points, or all of these. If you want to generate a raster with randomly-sampled cells, see sampleRast()
.
## S4 method for signature 'GRaster'
spatSample(
x,
size,
as.points = FALSE,
values = TRUE,
cats = TRUE,
xy = FALSE,
strata = NULL,
byStratum = FALSE,
zlim = NULL,
seed = NULL,
verbose = FALSE
)
## S4 method for signature 'GVector'
spatSample(
x,
size,
as.points = FALSE,
values = TRUE,
xy = FALSE,
byStratum = FALSE,
zlim = NULL,
seed = NULL
)
x |
A |
size |
Numeric value > 0: Number of points to create. |
as.points |
Logical: If |
values |
Logical: If |
cats |
Logical: If |
xy |
Logical: If |
strata |
Either |
byStratum |
Logical: If |
zlim |
Either |
seed |
Either |
verbose |
Logical: If |
A data.frame
, data.table
, or GVector
.
sampleRast()
, terra::spatSample()
, module v.random
in GRASS
if (grassStarted()) {
# Setup
library(sf)
library(terra)
# Example data
madElev <- fastData("madElev") # raster
# Convert to GRasters and GVectors
elev <- fast(madElev)
### spatSample()
# Random points as data.frame or data.table:
randVals <- spatSample(elev, size = 20, values = TRUE)
randVals
# Random points as a points GVector:
randPoints <- spatSample(elev, size = 20, as.points = TRUE)
randPoints
plot(elev)
plot(randPoints, add = TRUE)
# Random points in a select area:
madCoast <- fastData("madCoast4") # vector
coast <- fast(madCoast)
ant <- coast[coast$NAME_4 == "Antanambe"] # subset
restrictedPoints <- spatSample(elev, size = 20, as.points = TRUE,
strata = ant)
plot(elev)
plot(ant, add = TRUE)
plot(restrictedPoints, add = TRUE) # note 20 points for entire geometry
# Random points, one set per subgeometry:
stratifiedPoints <- spatSample(elev, size = 20, as.points = TRUE,
strata = ant, byStratum = TRUE)
plot(elev)
plot(ant, add = TRUE)
plot(stratifiedPoints, pch = 21, bg = "red", add = TRUE) # note 20 points per subgeometry
# Random categories:
madCover <- fastData("madCover") # raster
cover <- fast(madCover)
randCover <- spatSample(cover, size = 20, values = TRUE,
cat = TRUE, xy = TRUE)
randCover
### sampleRast()
# Random cells in non-NA cells:
rand <- sampleRast(elev, 10000)
plot(rand)
nonnacell(rand)
# Use custom values for the mask:
randCustomMask <- sampleRast(elev, 10000, maskvalues = 1:20)
plot(randCustomMask)
# Force selected values to a custom value:
randCustomUpdate <- sampleRast(elev, 10000, updatevalue = 7)
plot(randCustomUpdate)
# Custom values for mask and set selected cells to custom value:
randAll <- sampleRast(elev, 10000, maskvalues = 1:20, updatevalue = 7)
plot(randAll)
}
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