examples/aggregate_map.R

In mtennekes/oldtmaptools: Old Thematic Map Tools

\dontrun{
if (require(tmap) && packageVersion("tmap") >= "2.0") {
    data(land)

    # original map
    qtm(land, raster="cover_cls")

    # map decreased by factor 4 for each dimension
    land4 <- aggregate_map(land, fact=4, agg.fun="modal")
    qtm(land4, raster="cover_cls")

    # map decreased by factor 8, where the variable trees is
    # aggregated with mean, min, and max
    land_trees <- aggregate_map(land, fact=8,
        agg.fun=list(trees="mean", trees="min", trees="max"))

    tm_shape(land_trees) +
    	tm_raster(c("trees.1", "trees.2", "trees.3"), title="Trees (%)") +
    	tm_facets(free.scales=FALSE) +
    	tm_layout(panel.labels = c("mean", "min", "max"))

    data(NLD_muni, NLD_prov)

    # aggregate Dutch municipalities to provinces
    NLD_prov2 <- aggregate_map(NLD_muni, by="province",
        agg.fun = list(population="sum", origin_native="mean", origin_west="mean",
        origin_non_west="mean", name="modal"), weights = "population")

    # see original provinces data
    as.data.frame(NLD_prov)[, c("name", "population", "origin_native",
                                "origin_west", "origin_non_west")]

    # see aggregates data (the last column corresponds to the most populated municipalities)
    sf::st_set_geometry(NLD_prov2, NULL)

    # largest municipalities in area per province
    NLD_largest_muni <- aggregate_map(NLD_muni, by="province",
        agg.fun = list(name="modal"), weights = "AREA")

    sf::st_set_geometry(NLD_largest_muni, NULL)
}
}