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Aggregate raster cells into larger cells or combine geometries of a vector

Source: R/aggregate.r
aggregate.Rd

When applied to a GRaster, aggregate() creates a new raster with cells that are a multiple of the size of the cells of the original raster. The new cells can be larger or smaller than the original cells (this function thus emulates both the terra::aggregate() and terra::disagg() functions in terra.)

When applied to a GVector, all geometries are combined into a "multipart" geometry, in which geometries are treated as if they were a single unit. Borders between aggregated geometries can be dissolved if the dissolve argument is TRUE. If the GVector has a data table associated with it, the output will also have a data table as long as there is at least one column with values that are all the same. Values of columns that do not have duplicated values will be converted to NA.

Usage

# S4 method for class 'GRaster'
aggregate(
  x,
  fact = 2,
  fun = "mean",
  weight = FALSE,
  prob = NULL,
  na.rm = FALSE
)

# S4 method for class 'GVector'
aggregate(x, dissolve = TRUE)

Arguments

x

A GRaster or GVector.

fact

Numeric vector (rasters only): One, two, or three positive values. These reflect the size of the new cells as multiples of the size of the old cells. If just one value is supplied, this is used for all two or three dimensions. If two values are supplied, the first is multiplied by the east-west size of cells, and the second north-south size of cells (the raster must be 2D). If three values are supplied, the third value is used as the multiplier of the vertical dimension of cells. Values are calculated using all cells that have their centers contained by the target cell.

Note that unlike terra::aggregate() and terra::disagg(), these values need not be integers.

fun

Character (rasters only): Name of the function used to aggregate. For GRasters, this is the function that summarizes across cells. For GVectors, this function will be used to calculate new values of numeric or integer cells.

  • mean: Average (default)

  • median: Median

  • mode: Most common value

  • min: Minimum

  • max: Maximum

  • range: Difference between maximum and minimum

  • sum: Sum

  • varpop: Population variance

  • sdpop: Population standard deviation

  • quantile: Quantile (see argument prob)

  • count: Number of non-NA cell

  • diversity: Number of unique values

weight

Logical (rasters only): If FALSE, each source cell that has its center in the destination cell will be counted equally. If TRUE, the value of each source will be weighted the proportion of the destination cell the source cell covers.

prob

Numeric (rasters only): Quantile at which to calculate quantile.

na.rm

Logical (rasters only): If FALSE (default), propagate NA cells or NA values.

dissolve

Logical (vectors only): If TRUE (default), then aggregated geometries will have their borders dissolved. This is ignored if the input GVector is not a "polygons" vector.

Value

A GRaster or GVector.

See also

stats::aggregate(), terra::aggregate(), disagg(), terra::disagg()

Examples

if (grassStarted()) {

# Setup
library(terra)

# Example data
madElev <- fastData("madElev")
madCoast4 <- fastData("madCoast4")

### aggregating a GRaster
#########################

# Convert:
elev <- fast(madElev)

### Aggregate GRaster by same factor in 2 dimensions
# fasterRaster
agg2 <- aggregate(elev, 2, "mean")
agg2

# terra
agg2terra <- aggregate(madElev, 2, "mean")
agg2terra

# Compare rasters aggregated by fasterRaster and terra.
# These should be the same.
agg2 <- rast(agg2)
agg2 <- extend(agg2, agg2terra)
agg2 - agg2terra

### Aggregate GRaster by a non-integer factor in 2 dimensions
# fasterRaster
agg2.9 <- aggregate(elev, 2.9, "mean")
agg2.9

# terra
agg2.9terra <- aggregate(madElev, 2.9, "mean")
agg2.9terra

# Compare rasters aggregated by fasterRaster and terra.
# These should be different.
res(agg2.9)
res(agg2.9terra) # terra rounds aggregation factor down
2 * res(madElev) # original resolution multiplied by 2

### Aggregate GRaster by different factor in 2 dimensions
agg2x3 <- aggregate(elev, c(2, 3), "mean")
agg2x3

### aggregating a GVector
#########################

madCoast4 <- fastData("madCoast4")

# Convert:
coast <- fast(madCoast4)

# Aggregate and disaggregate:
aggCoast <- aggregate(coast)
disaggCoast <- disagg(coast)

ngeom(coast)
ngeom(aggCoast)
ngeom(disaggCoast)

# plot
oldpar <- par(mfrow = c(1, 3))
plot(coast, main = "Original", col = 1:nrow(coast))
plot(aggCoast, main = "Aggregated", col = 1:nrow(aggCoast))
plot(disaggCoast, main = "Disaggregated", col = 1:nrow(disaggCoast))
par(oldpar)

}