Beyond the choropleth

“World data on a map” has many honest forms. A choropleth is only the first. The package offers a full vocabulary; this vignette tours the ones that need only maps – itself a suggested package, so the map chunks are skipped when it is absent – and points to the rest, which need sf or biscale as well.

Proportional-symbol (bubble) maps

For totals, a choropleth misleads: large values hide in small countries. Sized circles at centroids are the right idiom.

bubble_map(snap, population)

Bubble map: population drawn as proportional circles at country centroids.

Spike maps

The same “totals” job as bubbles, with a different overplotting trade-off: spikes only grow upward, so dense regions (Europe, the Caribbean) stay legible.

spike_map(snap, population)

Spike map: population drawn as vertical spikes rising from country centroids.

Both verbs place one symbol per country centroid, and the bundled centroid table does not cover every code in the codelist. On this snapshot five countries with population – Hong Kong, Macao, Gibraltar, the British Virgin Islands and Tuvalu – have no centroid and so no symbol. Each verb warns and names them, and counts them as missing rather than shown:

cov <- attr(suppressWarnings(bubble_map(snap, population)),
            "countryatlas_provenance")$coverage
unlist(cov[c("n_total", "n_shown", "n_missing")])
#>   n_total   n_shown n_missing 
#>       215       210         5
cov$missing_iso3c
#> [1] "GIB" "HKG" "MAC" "TUV" "VGB"

Equal-area tile grids

Give every country the same visual weight so micro-states are visible. The bundled grid covers 239 countries – see ?world_tiles for the ten it omits.

tile_map(snap, gdp_per_capita)

Equal-area tile grid: one identically sized tile per country, shaded by GDP per capita.

Flow maps

Great-circle arcs between country pairs from an origin–destination table.

od <- data.frame(
  from   = c("China", "Germany", "Brazil", "Nigeria"),
  to     = c("United States", "France", "Argentina", "India"),
  weight = c(500, 200, 90, 60)
)
flow_map(od, from, to, weight)

Flow map: great-circle arcs joining four origin-destination country pairs, width by volume.

Small multiples

facet_map() splits one choropleth into per-group panels, the static counterpart to animate_world(), for print and side-by-side comparison:

world_poly <- attach_geometry(snap, geometry = "polygon") |>
  dplyr::filter(!is.na(continent))
facet_map(world_poly, gdp_per_capita, continent, style = "quantile", ncol = 3)

Small multiples: one GDP per capita choropleth panel per continent.

Labels

Centroid-anchored labels (names, ISO codes or flag emoji), with ggrepel collision avoidance when available. Zoom with coord_quickmap() rather than coord_cartesian() – both replace the map’s coordinate system, but only the former keeps the latitude-dependent aspect ratio that stops Europe coming out stretched sideways.

mapdf <- attach_geometry(
  dplyr::filter(snap, continent == "Europe"), geometry = "polygon"
)
world_map(mapdf, gdp_per_capita) +
  geom_country_labels(repel = FALSE, size = 2.5) +
  ggplot2::coord_quickmap(xlim = c(-25, 45), ylim = c(34, 72))

Choropleth of Europe with ISO codes labelled at country centroids.

Maps that need optional packages

The remaining displays follow the same one-call pattern but require optional packages, so they are shown here as code:

# Bivariate choropleth (two variables at once): needs `biscale` + `sf`
world_data(2020, c(gdp = "NY.GDP.PCAP.KD", life = "SP.DYN.LE00.IN"),
           geometry = "sf") |>
  bivariate_map(gdp, life)

# Area-honest cartogram: needs `cartogram` + `sf`
world_data(2020, c(pop = "SP.POP.TOTL"), geometry = "sf") |>
  cartogram_map(pop, type = "dorling")

# The same Dorling cartogram as a first-class verb, with its tuning exposed
world_data(2020, c(pop = "SP.POP.TOTL"), geometry = "sf") |>
  dorling_map(pop, k = 4)

# The fast flow-based cartogram (Gastner-Seguy-More): needs `cartogramR`
world_data(2020, c(pop = "SP.POP.TOTL"), geometry = "sf") |>
  cartogram_map(pop, type = "flow")

# Animated choropleth over a year panel: needs `gganimate`
world_data(2000:2020, c(gdp = "NY.GDP.PCAP.KD")) |>
  animate_world(gdp)

# Interactive choropleth: needs `leaflet`, `ggiraph` or `plotly`
world_data(2020) |>
  interactive_map(gdp_per_capita, engine = "plotly")

Value-by-alpha: the cartogram’s non-distorting cousin

A cartogram equalises a denominator by deforming geometry. value_by_alpha_map() does it by spending opacity instead, so the world stays recognisable: colour carries the value, opacity carries population, and a rate computed over a handful of people fades toward the background rather than shouting.

mapdf <- attach_geometry(snap, geometry = "polygon")
value_by_alpha_map(mapdf, gdp_per_capita, population)

Value-by-alpha map of GDP per capita weighted by population.

It needs no optional packages. The Honest maps vignette covers when to reach for it rather than for cartogram_map().

Country adjacency and distance

Two lightweight spatial helpers that aren’t choropleths at all. distance_between() answers “how far apart” from the bundled country_meta centroids, with no sf or network required:

distance_between("France", "Germany")
#> [1] 802.3524

country_borders() / neighbors() answer “who borders whom”, built from polygon topology, so they need sf:

neighbors("France")
#> # A tibble: 8 × 3
#>   iso3c neighbor neighbor_country
#>   <chr> <chr>    <chr>           
#> 1 FRA   SUR      Suriname        
#> 2 FRA   LUX      Luxembourg      
#> 3 FRA   ITA      Italy           
#> 4 FRA   BRA      Brazil          
#> 5 FRA   DEU      Germany         
#> 6 FRA   CHE      Switzerland     
#> 7 FRA   BEL      Belgium         
#> 8 FRA   ESP      Spain

Each degrades gracefully: if the optional package is missing you get a clear, actionable message (and animate_world() falls back to a faceted small-multiple).