nomove_agg: Direct data aggregation
In grainchanger: Moving-Window and Direct Data Aggregation

Description Usage Arguments Details Value Examples

Calculate the value for a given function for each cell in a larger resolution grid.

1	nomove_agg(coarse_dat, fine_dat, agg_fun, is_grid = TRUE, quiet = FALSE, ...)

`coarse_dat`	sf, Raster* or Spatial* object. The coarse grain data (response data) across which to calculate the aggregated function
`fine_dat`	Raster* object. Raster* object. The fine grain data (predictor / covariate data) to aggregate
`agg_fun`	function The function to apply. The function fun should take multiple numbers, and return a single number. For example mean, modal, min or max. It should also accept a na.rm argument (or ignore it, e.g. as one of the 'dots' arguments. For example, length will fail, but function(x, ...)na.omit(length(x)) works. See Details
`is_grid`	logical. Use `TRUE` (default) if `g` contains only rectangular cells (i.e. a grid). If `g` is any other polygon file, this should be set to false
`quiet`	logical. If `FALSE` (default) and `is_grid == TRUE` the user gets a warning that the aggregation assumes all cells are rectangular
`...`	further arguments passed to or from other methods

grainchanger has several built-in functions. Functions currently included are:

shdi - Shannon diversity, requires the additional argument lc_class (vector or scalar)
shei - Shannon evenness, requires the additional argument lc_class (vector or scalar)
prop - Proportion, requires the additional argument lc_class (scalar)
var_range - Range (max - min)

Note that nomove_agg can be run in parallel using plan(multiprocess) from the future package.

Raster (if input is Raster) or numeric vector (if input is sp or sf object) containing values calculated for each coarser cell

# load required data
data(g_sf)
data(cont_ls)
data(cat_ls)

# aggregate using mean
d <- nomove_agg(g_sf, cont_ls, mean)

# aggregate using Shannon evenness
d <- nomove_agg(g_sf, cont_ls, shei, lc_class = 1:4)