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 run without extra dependencies and points to the rest.

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)

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)

Equal-area tile grids

Give every country the same visual weight so micro-states are visible.

tile_map(snap, 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)

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)

Labels

Centroid-anchored labels (names, ISO codes or flag emoji), with ggrepel collision avoidance when available.

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_cartesian(xlim = c(-25, 45), ylim = c(34, 72))

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)

# 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")

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 – 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).