aggregate.stars {stars}

spatially or temporally aggregate stars object

Description

spatially or temporally aggregate stars object, returning a data cube with lower spatial or temporal resolution

Usage

## S3 method for class 'stars'
aggregate(
  x,
  by,
  FUN,
  ...,
  drop = FALSE,
  join = st_intersects,
  as_points = any(st_dimension(by) == 2, na.rm = TRUE),
  rightmost.closed = FALSE,
  left.open = FALSE,
  exact = FALSE
)

Arguments

x

object of class stars with information to be aggregated

by

object of class sf or sfc for spatial aggregation, for temporal aggregation a vector with time values (Date, POSIXct, or PCICt) that is interpreted as a sequence of left-closed, right-open time intervals or a string like "months", "5 days" or the like (see cut.POSIXt), or a function that cuts time into intervals; if by is an object of class stars, it is converted to sfc by st_as_sfc(by, as_points = FALSE) thus ignoring its time component. Note: each pixel is assigned to only a single group (in the order the groups occur) so non-overlapping spatial features and temporal windows are recommended.

FUN

aggregation function, such as mean

...

arguments passed on to FUN, such as na.rm=TRUE

drop

logical; ignored

join

function; function used to find matches of x to by

as_points

see st_as_sf: shall raster pixels be taken as points, or small square polygons?

rightmost.closed

see findInterval

left.open

logical; used for time intervals, see findInterval and cut.POSIXt

exact

logical; if TRUE, use coverage_fraction to compute exact overlap fractions of polygons with raster cells

See Also

aggregate, st_interpolate_aw, st_extract, https://github.com/r-spatial/stars/issues/317

Examples

# aggregate time dimension in format Date
tif = system.file("tif/L7_ETMs.tif", package = "stars")
t1 = as.Date("2018-07-31")
x = read_stars(c(tif, tif, tif, tif), along = list(time = c(t1, t1+1, t1+2, t1+3)))[,1:30,1:30]
st_get_dimension_values(x, "time")
x_agg_time = aggregate(x, by = t1 + c(0, 2, 4), FUN = max) 

# aggregate time dimension in format Date - interval
by_t = "2 days"
x_agg_time2 = aggregate(x, by = by_t, FUN = max) 
st_get_dimension_values(x_agg_time2, "time")
#TBD:
#x_agg_time - x_agg_time2

# aggregate time dimension in format POSIXct
x = st_set_dimensions(x, 4, values = as.POSIXct(c("2018-07-31", 
                                                  "2018-08-01", 
                                                  "2018-08-02", 
                                                  "2018-08-03")), 
                      names = "time")
by_t = as.POSIXct(c("2018-07-31", "2018-08-02"))
x_agg_posix = aggregate(x, by = by_t, FUN = max)
st_get_dimension_values(x_agg_posix, "time")
#TBD:
# x_agg_time - x_agg_posix
aggregate(x, "2 days", mean)
if (require(ncmeta, quietly = TRUE)) {
 # Spatial aggregation, see https://github.com/r-spatial/stars/issues/299
 prec_file = system.file("nc/test_stageiv_xyt.nc", package = "stars")
 prec = read_ncdf(prec_file, curvilinear = c("lon", "lat"))
 prec_slice = dplyr::slice(prec, index = 17, along = "time")
 nc = sf::read_sf(system.file("gpkg/nc.gpkg", package = "sf"), "nc.gpkg")
 nc = st_transform(nc, st_crs(prec_slice))
 agg = aggregate(prec_slice, st_geometry(nc), mean)
 plot(agg)
}

# example of using a function for "by": aggregate by month-of-year
d = c(10, 10, 150)
a = array(rnorm(prod(d)), d) # pure noise
times = Sys.Date() + seq(1, 2000, length.out = d[3])
m = as.numeric(format(times, "%m"))
signal = rep(sin(m / 12 * pi), each = prod(d[1:2])) # yearly period
s = (st_as_stars(a) + signal) %>%
      st_set_dimensions(3, values = times)
f = function(x, format = "%B") {
	  months = format(as.Date(paste0("01-", 1:12, "-1970")), format)
	  factor(format(x, format), levels = months)
}
agg = aggregate(s, f, mean)
plot(agg)