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The raster package is extremely powerful in the R ecosystem for spatial data. It can be used very efficiently to drive data extraction and summary tools using its consistent cell-index and comprehensive helper functions for converting between cell values and less abstract raster grid properties.

Tabularaster provides some more helpers for working with cells and tries to fill some of the (very few!) gaps in raster functionality. When raster returns cell values of hierarchical objects it returns a hierarchical (list) of cells to match the input query.

Tabularaster provides on a few simple functions.

All functions that work with sp Spatial also work with `sf simple features.

There is some overlap with quadmesh and spex while I figure out where things belong.


Install from CRAN, or get the development version from Github.



Basic usage is to extract the cell numbers from an object, where object is a a matrix of points, a Spatial object or a simple features sf object.

cells <- cellnumbers(raster, object)

The value in this approach is not for getting cell numbers per se, but for using those downstream. The cell number is an index into the raster that means the geometric hard work is done, so we can apply the index for subsequent extractions, grouping aggregations, or for determining the coordinates or other structure summaries of where the cell belongs.


## summarize by object grouping
cells %>% mutate(value= extract(raster, cell_)) %>% group_by(object_) %>% summarize(mean(value))

## summarize by cell grouping
cells %>% mutate(value= extract(raster, cell_)) %>% group_by(cell_) %>% summarize(mean(value))

The utility of this is very much dependent on individual workflow, so this in its own right is not very exciting. Tabularaster simply provides an easier way to create your tools.

See the vignettes for more.