Small multiples of one choropleth, drawn once per classification method, plus the break table and the count of countries in each class. The point is that the choice is consequential and usually unexamined: Brewer & Pickle (2002) found quantiles among the best methods for general choropleth reading and natural breaks (Jenks) below 70% as accurate, which is the reverse of the common GIS default.
Usage
classify_compare(
data,
value,
methods = c("quantile", "jenks", "equal", "pretty"),
n = 5,
ncol = NULL,
...
)Arguments
- data
A map-ready frame (polygon or
sf).- value
The value column (unquoted).
- methods
Classification styles to compare. Any of
"quantile","jenks","equal","pretty"and"sd"."jenks"needs the optionalclassInt; without it, it falls back to quantile breaks with a warning.- n
Number of classes (default
5).- ncol
Number of facet columns.
- ...
Passed to
world_map().
Value
A faceted ggplot object, with the per-method break and class-count
table attached as the "countryatlas_classification" attribute (and
readable with map_provenance()).
References
Brewer, C. A. & Pickle, L. (2002). Evaluation of methods for classifying epidemiological data on choropleth maps in series. Annals of the Association of American Geographers 92(4), 662-681. doi:10.1111/1467-8306.00310
Examples
# \donttest{
snap <- countryatlas::world_snapshot$countries
if (requireNamespace("maps", quietly = TRUE)) {
cmp <- attach_geometry(snap, geometry = "polygon") |>
classify_compare(gdp_per_capita)
attr(cmp, "countryatlas_classification")
}
#> # A tibble: 20 × 4
#> method class n share
#> <chr> <chr> <int> <dbl>
#> 1 quantile [268.7,1662] 38 0.201
#> 2 quantile (1662,4594] 38 0.201
#> 3 quantile (4594,1.029e+04] 37 0.196
#> 4 quantile (1.029e+04,2.937e+04] 38 0.201
#> 5 quantile (2.937e+04,2.472e+05] 38 0.201
#> 6 jenks [268.7,1.312e+04] 123 0.651
#> 7 jenks (1.312e+04,3.484e+04] 37 0.196
#> 8 jenks (3.484e+04,6.771e+04] 23 0.122
#> 9 jenks (6.771e+04,1.221e+05] 5 0.0265
#> 10 jenks (1.221e+05,2.472e+05] 1 0.00529
#> 11 equal [268.7,4.965e+04] 173 0.915
#> 12 equal (4.965e+04,9.903e+04] 13 0.0688
#> 13 equal (9.903e+04,1.484e+05] 2 0.0106
#> 14 equal (1.484e+05,1.978e+05] 0 0
#> 15 equal (1.978e+05,2.472e+05] 1 0.00529
#> 16 pretty [0,5e+04] 173 0.915
#> 17 pretty (5e+04,1e+05] 13 0.0688
#> 18 pretty (1e+05,1.5e+05] 2 0.0106
#> 19 pretty (1.5e+05,2e+05] 0 0
#> 20 pretty (2e+05,2.5e+05] 1 0.00529
# }