R: Reduce a data cube over the time dimension

reduce_time.cube {gdalcubes} | R Documentation

## Reduce a data cube over the time dimension

### Description

Create a proxy data cube, which applies one or more reducer functions to selected bands over pixel time series of a data cube

### Usage

    ## S3 method for class 'cube'
    reduce_time(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

The function can either apply a built-in reducer if expr is given, or apply a custom R reducer function if FUN is provided.

In the former case, 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", "which_min", and "which_max".

User-defined R reducer functions receive a two-dimensional array as input where rows correspond to the band and columns represent the time dimension. For example, one row is the time series of a specific band. FUN should always return a numeric vector with the same number of elements, which will be interpreted as bands in the result cube. Notice that it is recommended to specify the names of the output bands as a character vector. If names are missing, the number and names of output bands is tried to be derived automatically, which may fail in some cases.

### 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="2018-06"),
                  srs="EPSG:32618", nx = 497, ny=526, dt="P1M")
    L8.cube = raster_cube(L8.col, v)
    L8.rgb = select_bands(L8.cube, c("B02", "B03", "B04"))
    L8.rgb.median = reduce_time(L8.rgb, "median(B02)", "median(B03)", "median(B04)")
    L8.rgb.median

    plot(L8.rgb.median, rgb=3:1)

    # user defined reducer calculating interquartile ranges
    L8.rgb.iqr = reduce_time(L8.rgb, names=c("iqr_R", "iqr_G","iqr_B"), FUN = function(x) {
        c(diff(quantile(x["B04",],c(0.25,0.75), na.rm=TRUE)),
          diff(quantile(x["B03",],c(0.25,0.75), na.rm=TRUE)),
          diff(quantile(x["B02",],c(0.25,0.75), na.rm=TRUE)))
    })
    L8.rgb.iqr

    plot(L8.rgb.iqr, key.pos=1)

[Package gdalcubes version 0.3.1 Index]