Beyond the choropleth

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“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. The bundled grid covers 239 countries – see ?world_tiles for the ten it omits.

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")

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).

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.