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

# S3 method for 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); 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.

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

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")
#> [1] "2018-07-31" "2018-08-01" "2018-08-02" "2018-08-03"
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")
#> [1] "2018-07-31" "2018-08-02"
x_agg_time - x_agg_time2
#> Warning: longer object length is not a multiple of shorter object length
#> stars object with 4 dimensions and 1 attribute #> attribute(s): #> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's #> L7_ETMs.tif -109 -13 3 1.896111 18 90 5400 #> dimension(s): #> from to offset delta refsys point values x/y #> time 1 3 2018-07-31 2 days Date NA NULL #> x 1 30 288776 28.5 UTM Zone 25, Southern Hem... FALSE NULL [x] #> y 1 30 9120761 -28.5 UTM Zone 25, Southern Hem... FALSE NULL [y] #> band 1 6 NA NA NA NA NULL
# 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")
#> [1] "2018-07-31 UTC" "2018-08-02 UTC"
x_agg_time - x_agg_posix
#> Warning: longer object length is not a multiple of shorter object length
#> stars object with 4 dimensions and 1 attribute #> attribute(s): #> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's #> L7_ETMs.tif -104 -13 3 1.943889 18 90 10800 #> dimension(s): #> from to offset delta refsys point values x/y #> time 1 3 2018-07-31 2 days Date NA NULL #> x 1 30 288776 28.5 UTM Zone 25, Southern Hem... FALSE NULL [x] #> y 1 30 9120761 -28.5 UTM Zone 25, Southern Hem... FALSE NULL [y] #> band 1 6 NA NA NA NA NULL
aggregate(x, "2 days", mean)
#> stars object with 4 dimensions and 1 attribute #> attribute(s): #> Min. 1st Qu. Median Mean 3rd Qu. Max. #> L7_ETMs.tif 17 43 58 57.58796 70 145 #> dimension(s): #> from to offset delta refsys point values #> time 1 2 2018-07-31 UTC 2 days POSIXct NA NULL #> x 1 30 288776 28.5 UTM Zone 25, Southern Hem... FALSE NULL #> y 1 30 9120761 -28.5 UTM Zone 25, Southern Hem... FALSE NULL #> band 1 6 NA NA NA NA NULL #> x/y #> time #> x [x] #> y [y] #> band
# 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"))
#> no 'var' specified, using Total_precipitation_surface_1_Hour_Accumulation
#> other available variables: #> time_bounds, lon, lat, time
#> No projection information found in nc file. #> Coordinate variable units found to be degrees, #> assuming WGS84 Lat/Lon.
#> Warning: bounds for time seem to be reversed; reverting them
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)
#> although coordinates are longitude/latitude, st_intersects assumes that they are planar
#> although coordinates are longitude/latitude, st_intersects assumes that they are planar
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)
#> downsample set to c(0,0,1)