# 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. 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).