Package 'countryatlas'

Description

Aggregate a country-level value to a coarser grouping, optionally with population-weighted means.

Usage

aggregate_regions(data, value, by = "region", fun = "sum", weight = NULL)

Arguments

Value

A tibble of by plus the aggregated value.

Examples

df <- data.frame(iso3c = c("USA", "CAN", "BRA"),
                 region = c("North America", "North America", "Latin America"),
                 gdp = c(21, 1.7, 1.4))
aggregate_regions(df, gdp, fun = "sum")

Description

Given a panel from world_data(2000:2020, ...), animate the choropleth over year via the optional gganimate package, or fall back to a faceted small-multiple when it is not installed.

Usage

animate_world(data, fill, time = year, projection = "equal_earth", ...)

Arguments

Value

A gganim object (if gganimate is available) or a faceted ggplot.

Examples

## Not run: 
world_data(2000:2005, c(gdp = "NY.GDP.PCAP.KD")) |>
  animate_world(gdp)

## End(Not run)

Description

Hand countryatlas's curated, ISO-reconciled, WDI-joined spatial table to ggsql so it can be charted with ⁠DRAW spatial⁠ – the bridge that lets ggsql draw maps of your override-corrected data instead of its static bundled world. sf geometry is WKB-encoded so ggsql can decode it.

Usage

as_ggsql_source(
  data,
  name = "countryatlas_world",
  format = c("duckdb", "parquet", "arrow"),
  con = NULL,
  path = NULL,
  geometry_col = "geometry"
)

Arguments

Value

Depending on format: a DuckDB connection (with the table written), a Parquet file path, or a nanoarrow array stream.

Examples

## Not run: 
# Curate in R, render in the database:
src <- world_data(2020, geometry = "sf") |> as_ggsql_source(format = "duckdb")
ggsql::ggsql_execute(src, world_query(gdp_per_capita))

## End(Not run)

Description

The bridge between a one-row-per-country table (e.g. from country_data()) and plotting: bolts polygon or sf geometry onto your data, keyed on iso3c.

Usage

attach_geometry(
  data,
  by = "iso3c",
  geometry = c("polygon", "sf"),
  scale = "small",
  region = NULL,
  projection = "equal_earth",
  recenter = NULL
)

Arguments

Value

For "polygon", a tibble with long/lat/group plus your columns. For "sf", an sf object.

Examples

df <- data.frame(iso3c = c("USA", "CAN"), value = c(1, 2))
if (requireNamespace("maps", quietly = TRUE)) {
  attach_geometry(df, geometry = "polygon")
}

Description

What is missing, before you trust the map: which countries are unmatched, the NA rate per indicator, and which World Bank regions / income groups are under-covered – so a half-empty map is caught before it is published.

Usage

audit_coverage(data, indicator = NULL, by = c("region", "income", "continent"))

Arguments

Value

A list with elements unmatched, na_rates and by_group.

Examples

audit_coverage(countryatlas::world_snapshot$countries)

Description

A 2-D bivariate choropleth with a built-in 2-D legend (via the optional biscale package), e.g. GDP per capita x life expectancy in one map.

Usage

bivariate_map(
  data,
  fill_x,
  fill_y,
  palette = "GrPink",
  dim = 3,
  projection = "equal_earth"
)

Arguments

Value

A ggplot object (the map; combine with biscale::bi_legend() for a standalone legend).

Examples

## Not run: 
world_data(2020, c(gdp = "NY.GDP.PCAP.KD", life = "SP.DYN.LE00.IN"),
           geometry = "sf") |>
  bivariate_map(gdp, life)

## End(Not run)

Description

Plots sized circles at country centroids – the right idiom for totals (population, total emissions, total GDP), which a choropleth misrepresents because big values hide in small countries and vice versa.

Usage

bubble_map(
  data,
  size,
  color = NULL,
  projection = "equal_earth",
  backend = c("polygon", "sf"),
  max_size = 18,
  alpha = 0.7
)

Arguments

Value

A ggplot object.

Examples

snap <- countryatlas::world_snapshot$countries
if (requireNamespace("maps", quietly = TRUE)) {
  bubble_map(snap, population)
}

Description

Resizes countries by weight (population, GDP, ...) via the optional cartogram package, defeating the "big empty countries dominate the eye" bias of world choropleths.

Usage

cartogram_map(
  data,
  weight,
  type = c("contiguous", "dorling", "noncontiguous"),
  fill = NULL,
  projection = "equal_earth"
)

Arguments

Value

A ggplot object.

Examples

## Not run: 
world_data(2020, c(pop = "SP.POP.TOTL"), geometry = "sf") |>
  cartogram_map(pop, type = "dorling")

## End(Not run)

Description

A report on what will and will not match before you trust the map: the input, its iso3c, whether it matched, and a suggestion (the closest known country name by string distance) for misses. Surfaced automatically by join_world().

Usage

check_country_match(
  x,
  origin = "country.name",
  custom_match = wdj_overrides(),
  suggest = TRUE
)

Arguments

Value

A tibble with columns input, iso3c, matched, suggestion.

Examples

check_country_match(c("USA", "Cote d'Ivoire", "Yugoslavia", "Wakanda"))

Description

Forget memoised World Bank fetches, both in-session and (optionally) on disk.

Usage

clear_wdi_cache(disk = FALSE)

Arguments

Value

Invisibly TRUE.

Examples

## Not run: 
clear_wdi_cache()

## End(Not run)

Description

A friendly-name to WDI-code lookup so indicator = common_indicators$population beats memorising "SP.POP.TOTL".

Usage

common_indicators

Format

A tibble with columns name (friendly name), code (WDI indicator code) and description.

Source

World Bank indicator catalogue.

Description

Completes a panel so every country has every year, optionally filling missing values by carry-forward ("locf") or linear interpolation ("linear") so animations do not flicker on missing years.

Usage

complete_years(
  data,
  years = NULL,
  value = NULL,
  method = c("none", "locf", "linear")
)

Arguments

Value

A completed (and optionally filled) panel tibble.

Examples

df <- data.frame(iso3c = "USA", year = c(2000L, 2002L), gdp = c(1, 3))
complete_years(df, 2000:2002, method = "linear")

Description

A discoverable wrapper around countrycode::countrycode() exposing the full set of schemes with first-class shortcuts for the high-value ones: flag emoji, currency, top-level domain, continent/region and research codes (Correlates of War, Polity, Gleditsch-Ward, V-Dem, IMF, FAO, FIPS, GAUL).

Usage

convert_country(
  x,
  to = "iso3c",
  from = "country.name",
  custom_match = wdj_overrides(),
  warn = TRUE
)

Arguments

Value

A vector of converted codes.

Examples

convert_country(c("Japan", "Brazil"), to = "flag")
convert_country("Germany", to = "currency")
convert_country(c("USA", "France"), to = "continent")

Description

The whole countrycode::codelist reshaped into a tidy, pipeable lookup you can filter() / join() directly – one row per country.

Usage

country_codes(codes = NULL)

Arguments

Value

A tibble, one row per country.

Examples

country_codes()
country_codes(c("iso2c", "continent", "currency"))

Description

The analysis counterpart to world_data(): no polygons, one tidy row per country (iso3c, iso2c, country, classifications and the requested indicators). This is what you actually join() / mutate() / summarise() / rank() on; attach geometry only at draw time with attach_geometry().

Usage

country_data(
  year,
  indicator = NULL,
  latest = FALSE,
  panel = FALSE,
  classify = c("income", "continent", "region"),
  cache = TRUE,
  language = "en",
  parallel = TRUE
)

Arguments

Value

A tibble, one row per country (or per country-year for a panel).

Examples

country_data(2020, c(co2 = "EN.ATM.CO2E.KT"))

Description

Answers the constant question "is this country in the EU / OECD / G7 / G20 / BRICS / ...?" from a curated, dated membership table (point-in-time membership is genuinely fiddly, so it is shipped and maintained, not guessed). See country_groups_tbl.

Usage

country_groups(group = NULL)

Arguments

Value

A tibble of group, iso3c, country.

Examples

country_groups("EU")
country_groups(c("G7", "BRICS"))

Description

A curated, dated membership table for the common country groups.

Usage

country_groups_tbl

Format

A tibble with columns group, iso3c, country.

Source

Curated from official membership lists (point-in-time; see the package NEWS for the reference date).

Description

The generic two-table version of the package's whole reason for being: join any two data frames that each key on country names or codes, by reconciling both sides to iso3c first. Tables keyed on "Czech Republic" vs "Czechia", or "South Korea" vs "Korea, Rep.", just work.

Usage

country_join(
  x,
  y,
  by_x,
  by_y,
  origin_x = "country.name",
  origin_y = "country.name",
  type = c("left", "inner", "full"),
  suffix = c(".x", ".y")
)

Arguments

Value

A tibble joined on a reconciled iso3c key.

Examples

a <- data.frame(country = c("Czechia", "South Korea"), gdp = c(1, 2))
b <- data.frame(nation = c("Czech Republic", "Korea, Rep."), pop = c(10, 51))
country_join(a, b, country, nation)

Description

The many-table generalisation of country_join(): reduce-join a list of data frames that each key on country names or codes, reconciling every one to iso3c first.

Usage

country_join_all(
  tables,
  by,
  origin = "country.name",
  type = c("full", "left", "inner")
)

Arguments

Value

A single tibble joined on iso3c (clashing non-key columns get dplyr's default .x/.y suffixes).

Examples

a <- data.frame(country = c("Czechia", "South Korea"), gdp = c(1, 2))
b <- data.frame(country = c("Czech Republic", "Korea, Rep."), pop = c(10, 51))
d <- data.frame(country = c("Czechia", "Korea"), area = c(79, 100))
country_join_all(list(a, b, d), by = "country")

Description

One row per country with the facts people constantly need and currently scrape together by hand.

Usage

country_meta

Format

A tibble with one row per country and columns including iso3c, iso2c, country, continent, region, un_region, capital, capital_lat, capital_lon, centroid_lat, centroid_lon, area_km2, currency, tld, landlocked, flag.

Source

Assembled from countrycode, WDI metadata and Natural Earth geometry.

Description

Facet a choropleth into small multiples (one panel per group or per year) – the static counterpart to animate_world(), for print and side-by-side comparison. Builds a world_map() and facets it on facet.

Usage

facet_map(data, fill, facet, ncol = NULL, ...)

Arguments

Value

A faceted ggplot object.

Examples

snap <- countryatlas::world_snapshot$countries
if (requireNamespace("maps", quietly = TRUE)) {
  mapdf <- attach_geometry(snap, geometry = "polygon")
  facet_map(mapdf, gdp_per_capita, continent, style = "quantile")
}

Description

Draws great-circle arcs between country pairs from an origin-destination table (trade, migration, flights, remittances), resolving both endpoints to centroids automatically.

Usage

flow_map(data, from, to, weight = NULL, origin = "country.name", n = 50)

Arguments

Value

A ggplot object.

Examples

od <- data.frame(from = c("China", "Germany"),
                 to = c("United States", "France"),
                 value = c(500, 200))
if (requireNamespace("maps", quietly = TRUE)) {
  flow_map(od, from, to, value)
}

Description

A ggplot2 layer that places labels (names, ISO codes or flag emoji) at country centroids, with optional ggrepel collision avoidance. Designed for the polygon backend produced by world_data() / join_world().

Usage

geom_country_labels(mapping = NULL, repel = TRUE, flag = FALSE, size = 3, ...)

Arguments

Value

A ggplot2 layer.

Examples

library(ggplot2)
snap <- countryatlas::world_snapshot$countries
if (requireNamespace("maps", quietly = TRUE)) {
  mapdf <- attach_geometry(snap, geometry = "polygon")
  world_map(mapdf, gdp_per_capita) + geom_country_labels()
}

Description

The world as a globe (orthographic projection) centred on lon/lat – the honest answer to "the whole world on a rectangle exaggerates the poles". Takes the same fill / style options as world_map(). The default "sf" backend gives the cleanest limb; the "polygon" backend draws the globe with ggplot2::coord_map() and needs only maps + mapproj (no sf).

Usage

globe_map(
  data,
  fill,
  lon = 0,
  lat = 20,
  backend = c("sf", "polygon"),
  style = c("continuous", "binned", "quantile", "jenks", "categorical"),
  palette = NULL,
  n_bins = 5,
  borders = TRUE,
  title = NULL,
  legend = NULL,
  na_label = "No data"
)

Arguments

Value

A ggplot object.

Examples

## Not run: 
world_data(2020, geometry = "sf") |>
  globe_map(gdp_per_capita, lon = 10, lat = 30)
# No sf required:
globe_map(world_snapshot$countries, continent, backend = "polygon",
          style = "categorical")

## End(Not run)

Description

Adds a growth-rate column to a panel: either the period-over-period change ("yoy") or the compound annual growth rate from the first observed year ("cagr"), computed per country.

Usage

growth_rate(data, value, type = c("yoy", "cagr"), suffix = "_growth")

Arguments

Value

data with a growth-rate column added (a proportion, so 0.03 = 3%).

Examples

df <- data.frame(iso3c = "USA", year = 2000:2002, gdp = c(100, 110, 121))
growth_rate(df, gdp)

Description

A vectorised membership predicate built on country_groups().

Usage

in_group(x, group, origin = "country.name")

Arguments

Value

A logical vector the same length as x.

Examples

in_group(c("France", "United States", "Japan"), "EU")

Description

Rescales a value column so the chosen base year equals to (100 by default), per country – the standard way to compare trajectories that start at very different levels.

Usage

index_to(data, value, base_year, to = 100, suffix = "_index")

Arguments

Value

data with an index column added.

Examples

df <- data.frame(iso3c = "USA", year = 2000:2002, gdp = c(50, 55, 60))
index_to(df, gdp, base_year = 2000)

Description

An interactive choropleth with hover and zoom, for dashboards and R Markdown / Quarto. Engines are all optional Suggests.

Usage

interactive_map(
  data,
  fill,
  tooltip = NULL,
  engine = c("plotly", "ggiraph", "leaflet", "ggsql"),
  ...
)

Arguments

Value

An interactive widget.

Examples

## Not run: 
world_data(2020) |> interactive_map(gdp_per_capita)
world_data(2020, geometry = "sf") |>
  interactive_map(gdp_per_capita, engine = "ggsql", transform = "log10")

## End(Not run)

Description

Auto-detects the country column, standardises it to ISO codes (via standardize_country()), attaches geometry and returns a plot-ready frame – the function that fulfils the package's promise for your own data. Pipe the result straight into world_map().

Usage

join_world(
  data,
  country_col = NULL,
  origin = "country.name",
  geometry = c("polygon", "sf", "none"),
  scale = "small",
  region = NULL,
  projection = "equal_earth",
  recenter = NULL,
  warn = TRUE
)

Arguments

Value

A plot-ready frame: polygon tibble, sf object, or (for geometry = "none") the standardised table.

Examples

rates <- data.frame(country = c("United States", "Brazil", "Kenya"),
                    vaccination_pct = c(0.7, 0.8, 0.6))

if (requireNamespace("maps", quietly = TRUE)) {
  joined <- join_world(rates, country)
}

Description

Point-in-polygon lookup: given longitude / latitude vectors (or an sf POINT object), return the iso3c of the country each point falls in – the bridge for getting point data (events, stations, observations) onto the country spine so it can be joined, aggregated and mapped like everything else.

Usage

locate_country(
  lon = NULL,
  lat = NULL,
  points = NULL,
  scale = "small",
  add = "country"
)

Arguments

Value

A tibble with one row per point: iso3c plus any add columns (NA for points that fall in no country, e.g. open ocean).

Examples

## Not run: 
locate_country(lon = c(2.35, -74.0), lat = c(48.85, 40.7))  # Paris, NYC

## End(Not run)

Description

Removes the "is this map just a population map?" footgun by dividing a value column by population. If no population column is supplied, SP.POP.TOTL is pulled automatically for the relevant countries and years.

Usage

per_capita(data, value, pop = NULL, suffix = "_per_capita", cache = TRUE)

Arguments

Value

data with a new per-capita column.

Examples

df <- data.frame(iso3c = c("USA", "CHN"), year = 2020L,
                 co2 = c(5e6, 1e7), pop = c(331e6, 1402e6))
per_capita(df, co2, pop)

Description

Adds rank, percentile and z_score for a value column, optionally within a group (region, year, ...), for "top 10" tables and labelling.

Usage

rank_countries(data, value, within = NULL, desc = TRUE)

Arguments

Value

data with rank, percentile and z_score columns added.

Examples

df <- data.frame(iso3c = c("USA", "CHN", "IND"), gdp = c(21, 17, 3))
rank_countries(df, gdp)

Description

The "act on it" companion to check_country_match(): replaces unmatched country names with their closest known country name (by string distance), but only when the match is confident enough, and reports what it changed. Pipe the result into standardize_country() / join_world().

Usage

repair_country_names(
  x,
  threshold = 0.2,
  origin = "country.name",
  verbose = TRUE
)

Arguments

Value

A character vector the same length as x, with confident misses replaced by the closest known country name (others left unchanged). The applied substitutions are attached as the attribute "repairs".

Examples

repair_country_names(c("United States", "Brzil", "Germny"))

Description

Adds a column giving each country's value as a share of the (year's) world total – e.g. share of global emissions or GDP. Operates within year when a panel is supplied.

Usage

share_of_world(data, value, suffix = "_share")

Arguments

Value

data with a share column added (a proportion in ⁠[0, 1]⁠).

Examples

df <- data.frame(iso3c = c("USA", "CHN"), co2 = c(5, 10))
share_of_world(df, co2)

Description

Reduce the vertex count of an sf object via the optional rmapshaper package (falling back to sf::st_simplify()), for fast web/plotting.

Usage

simplify_geometry(x, keep = 0.05, ...)

Arguments

Value

A simplified sf object.

Examples

## Not run: 
world_geometry(geometry = "sf") |> simplify_geometry(keep = 0.1)

## End(Not run)

Description

An animated GIF of the world rotating on its axis: a sequence of orthographic globe_map() frames at evenly spaced central longitudes, assembled into a looping animation with the optional gifski (preferred) or magick package. Embeds directly in R Markdown / Quarto / a README.

Usage

spin_globe(
  data,
  fill,
  lat = 20,
  n_frames = 60,
  fps = 15,
  backend = c("polygon", "sf"),
  width = 480,
  height = 480,
  file = NULL,
  ...
)

Arguments

Value

The path to the written GIF, invisibly.

Examples

## Not run: 
# No sf required:
spin_globe(world_snapshot$countries, continent, backend = "polygon",
           style = "categorical")

## End(Not run)

Description

The package's mission, exposed for your data: take a data frame keyed on messy country names (or codes) and attach standardised ISO codes plus useful classifications, reconciling spellings via countrycode::countrycode() and the curated wdj_overrides() table. The result joins cleanly to anything else keyed on iso3c.

Usage

standardize_country(
  data,
  country_col,
  origin = "country.name",
  add = c("iso3c", "iso2c", "continent", "region"),
  custom_match = wdj_overrides(),
  warn = TRUE
)

Arguments

Value

data with the requested columns added (and existing same-named columns overwritten).

Examples

df <- data.frame(nation = c("U.S.", "S. Korea", "Czechia"), value = 1:3)
standardize_country(df, nation)

Description

Strips axes, panel grid and background so the map is the focus. Used by all the package's plotting functions and exported for reuse.

Usage

theme_world_map(base_size = 12, base_family = "")

Arguments

Value

A ggplot2 theme object.

Examples

library(ggplot2)
ggplot() + theme_world_map()

Description

A statebins-style equal-area tile grid of the world (one square per country) so tiny states are actually visible. Uses the bundled world_tiles layout (and geofacet when available for small multiples).

Usage

tile_map(data, fill, label = TRUE)

Arguments

Value

A ggplot object.

Examples

tile_map(countryatlas::world_snapshot$countries, gdp_per_capita)

Description

A tidy, pipeable wrapper on WDI::WDIsearch() for discovering indicator codes.

Usage

wdi_search(pattern, field = c("name", "indicator"), cache = NULL)

Arguments

Value

A tibble of matching indicator codes and names.

Examples

wdi_search("CO2 emissions")

Description

A documented custom_match table for entities that map backends (ggplot2::map_data() and Natural Earth) get wrong or leave without an ISO code. Earlier versions of the package deleted these regions; now they are matched instead, so they stop silently disappearing from maps.

country_overrides() is the preferred name as of the package's rename to countryatlas; wdj_overrides() is kept as a backward-compatible alias.

Usage

wdj_overrides(extra = NULL)

country_overrides(extra = NULL)

Arguments

Details

The table maps a country/region name (as spelled by the geometry backends) to an ISO 3166-1 alpha-3 code. Pass the result as the custom_match argument to standardize_country(), world_data() and friends. Every downstream code (iso2c, continent, region, flag, ...) is derived from this iso3c, so a single override is enough.

Value

A named character vector suitable for countrycode(custom_match=).

Examples

wdj_overrides()
wdj_overrides(c(Somaliland = "SOM"))
country_overrides()

Description

The package's headline function, generalised but backward-compatible. Returns a tibble that already stitches together map geometry, World Bank indicators and the countrycode crosswalk, keyed on the ISO spine – ready to pipe into world_map() or ggplot2.

Usage

world_data(
  year,
  indicator = c(gdp_per_capita = "NY.GDP.PCAP.KD"),
  geometry = c("polygon", "sf", "none"),
  scale = c("small", "medium", "large"),
  region = NULL,
  classify = c("income", "continent", "region"),
  projection = "equal_earth",
  recenter = NULL,
  latest = FALSE,
  cache = TRUE,
  language = "en",
  parallel = TRUE,
  overrides = wdj_overrides()
)

Arguments

Details

world_data(2020) keeps its original behaviour (polygon backend, GDP per capita). Everything else is opt-in: any indicator(s), a span of years (a panel), an sf backend with real projections, and region subsetting.

Value

A tibble (polygon backend), sf object (sf backend) or country-level tibble (geometry = "none").

Examples

world_data(2020)
world_data(2020, indicator = c(life_exp = "SP.DYN.LE00.IN"),
           geometry = "none")

Description

Sometimes you just want the canvas: country polygons, label-ready centroids, coastlines, internal borders, a graticule or an ocean rectangle – already projected, region-subset and antimeridian-safe. This is the building block the plotting functions sit on, exposed for power users.

Usage

world_geometry(
  what = c("countries", "centroids", "coastline", "borders", "graticule", "ocean"),
  geometry = c("polygon", "sf"),
  scale = "small",
  region = NULL,
  projection = "equal_earth",
  recenter = NULL
)

Arguments

Value

A tibble (polygon backend) or sf object (sf backend).

Examples

if (requireNamespace("maps", quietly = TRUE)) {
  head(world_geometry("countries", geometry = "polygon"))
}

Description

Encapsulates the choropleth boilerplate and goes beyond a single style. Auto-detects the polygon vs sf backend, applies theme_world_map(), and – for sf – a real projection via ggplot2::coord_sf(). Binned / quantile / jenks styles are offered because a continuous fill on a skewed indicator hides almost all the variation; binning is the honest default for choropleths.

Usage

world_map(
  data,
  fill,
  style = c("continuous", "binned", "quantile", "jenks", "categorical"),
  projection = "equal_earth",
  palette = NULL,
  n_bins = 5,
  borders = TRUE,
  title = NULL,
  legend = NULL,
  na_label = "No data",
  recenter = NULL
)

Arguments

Value

A ggplot object.

Examples

snap <- countryatlas::world_snapshot$countries
if (requireNamespace("maps", quietly = TRUE)) {
  mapdf <- attach_geometry(snap, geometry = "polygon")
  world_map(mapdf, gdp_per_capita, style = "quantile")
}

Description

Build a ggsql query string that draws a choropleth from a registered countryatlas source – the same idea as world_map(), but the map is rendered in the database (DuckDB) and returned as a web-ready Vega-Lite widget, so the geometry never has to come back into R. Pure string builder with no dependencies; pair it with as_ggsql_source() + ggsql::ggsql_execute(), or drop the string into a {ggsql} chunk.

Usage

world_query(
  fill,
  source = "countryatlas_world",
  projection = "equal_earth",
  palette = "viridis",
  transform = NULL,
  title = NULL,
  draw = "spatial"
)

Arguments

Value

A ggsql_query string (prints as the formatted query).

Examples

world_query(gdp_per_capita, projection = "equal_earth",
            palette = "magma", transform = "log10",
            title = "GDP per capita")

Description

A small, lazy-loaded snapshot of a curated indicator set for one recent year, as both a country-level tibble and a low-resolution sf object. It lets every example, test and vignette run offline and deterministically, without the World Bank API.

Usage

world_snapshot

Format

A list with two elements:

countries

A tibble, one row per country, with iso3c, iso2c, country, classifications and curated indicators (gdp_per_capita, population, life_expectancy, co2_per_capita).

sf

A low-resolution sf object with the same per-country columns and a geometry column (Natural Earth 110m). Present only if sf was available when the package was built.

year

The reference year.

Source

World Bank via WDI; geometry from Natural Earth via rnaturalearth.

Description

A statebins-style equal-area tile layout: one square per country, positioned on a row/col grid derived from country centroids. Used by tile_map().

Usage

world_tiles

Format

A tibble with columns iso3c, country, row, col.

Source

Derived from Natural Earth country centroids.