R: Reduce a data cube over spatial (x,y or lat,lon) dimensions

reduce_space.cube {gdalcubes} | R Documentation

## Reduce a data cube over spatial (x,y or lat,lon) dimensions

### Description

Create a proxy data cube, which applies one or more reducer functions to selected bands over spatial slices of a data cube

### Usage

    ## S3 method for class 'cube'
    reduce_space(x, expr, ..., FUN, names = NULL)

### Arguments

`x` | source data cube
`expr` | either a single string, or a vector of strings defining which reducers will be applied over which bands of the input cube
`...` | optional additional expressions (if `expr` is not a vector)
`FUN` | a user-defined R function applied over pixel time series (see Details)
`names` | character vector; if FUN is provided, names can be used to define the number and name of output bands

### Details

Notice that expressions have a very simple format: the reducer is followed by the name of a band in parantheses. You cannot add more complex functions or arguments.

Possible reducers currently are "min", "max", "sum", "prod", "count", "mean", "median", "var", "sd".

### Value

proxy data cube object

### Note

Implemented reducers will ignore any NAN values (as na.rm=TRUE does).

This function returns a proxy object, i.e., it will not start any computations besides deriving the shape of the result.

### Examples

    # create image collection from example Landsat data only
    # if not already done in other examples
    if (!file.exists(file.path(tempdir(), "L8.db"))) {
      L8_files <- list.files(system.file("L8NY18", package = "gdalcubes"),
                             ".TIF", recursive = TRUE, full.names = TRUE)
      create_image_collection(L8_files, "L8_L1TP", file.path(tempdir(), "L8.db"))
    }

    L8.col = image_collection(file.path(tempdir(), "L8.db"))
    v = cube_view(extent=list(left=388941.2, right=766552.4,
                  bottom=4345299, top=4744931, t0="2018-01", t1="2018-12"),
                  srs="EPSG:32618", nx = 497, ny=526, dt="P1M")
    L8.cube = raster_cube(L8.col, v)
    L8.b02 = select_bands(L8.cube, c("B02"))
    L8.b02.median = reduce_space(L8.b02, "median(B02)")
    L8.b02.median

    plot(L8.b02.median, key.pos=1)

[Package gdalcubes version 0.6.0 Index]