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
- year
A single year or a range (e.g.
2000:2020, yielding a panel keyed oniso3c+year). Minimum 1960.- indicator
A named character vector of WDI codes. Names drive column names, e.g.
c(gdp = "NY.GDP.PCAP.KD", pop = "SP.POP.TOTL"). Defaults toc(gdp_per_capita = "NY.GDP.PCAP.KD").- geometry
"polygon"(default; reproduces the classic output),"sf"(Natural Earth, forgeom_sf()and real projections) or"none".- scale
Natural Earth resolution for the
sfbackend.- region
Optional subset: a continent, group name,
iso3cvector or bounding box.- classify
Which classifications to add (any of
"income","continent","region").- projection, recenter
Projection options for the
sfbackend.- latest
If
TRUE, use the most recent non-NAvalue per country for a single-year request.- cache
Whether to use the memoised / on-disk WDI cache.
- language
WDI language code (default
"en").- parallel
Whether to fetch multiple indicators in parallel.
- overrides
Name -> iso3c overrides for geometry matching (default
wdj_overrides()).
Value
A tibble (polygon backend), sf object (sf backend) or country-level
tibble (geometry = "none").
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.
Examples
# \donttest{
world_data(2020)
#> # A tibble: 99,338 × 13
#> long lat group order subregion iso3c iso2c country continent region income
#> <dbl> <dbl> <dbl> <int> <chr> <chr> <chr> <chr> <chr> <chr> <fct>
#> 1 -69.9 12.5 1 1 NA ABW AW Aruba Americas Latin… High …
#> 2 -69.9 12.4 1 2 NA ABW AW Aruba Americas Latin… High …
#> 3 -69.9 12.4 1 3 NA ABW AW Aruba Americas Latin… High …
#> 4 -70.0 12.5 1 4 NA ABW AW Aruba Americas Latin… High …
#> 5 -70.1 12.5 1 5 NA ABW AW Aruba Americas Latin… High …
#> 6 -70.1 12.6 1 6 NA ABW AW Aruba Americas Latin… High …
#> 7 -70.0 12.6 1 7 NA ABW AW Aruba Americas Latin… High …
#> 8 -70.0 12.6 1 8 NA ABW AW Aruba Americas Latin… High …
#> 9 -69.9 12.5 1 9 NA ABW AW Aruba Americas Latin… High …
#> 10 -69.9 12.5 1 10 NA ABW AW Aruba Americas Latin… High …
#> # ℹ 99,328 more rows
#> # ℹ 2 more variables: gdp_per_capita <dbl>, gdp_per_capita_2015 <dbl>
world_data(2020, indicator = c(life_exp = "SP.DYN.LE00.IN"),
geometry = "none")
#> # A tibble: 216 × 7
#> iso3c iso2c country continent region income life_exp
#> <chr> <chr> <chr> <chr> <chr> <fct> <dbl>
#> 1 AFG AF Afghanistan Asia Middle East, North… Low i… 61.5
#> 2 ALB AL Albania Europe Europe & Central A… Upper… 77.8
#> 3 DZA DZ Algeria Africa Middle East, North… Upper… 73.3
#> 4 ASM AS American Samoa Oceania East Asia & Pacific High … 72.7
#> 5 AND AD Andorra Europe Europe & Central A… High … 79.4
#> 6 AGO AO Angola Africa Sub-Saharan Africa Lower… 63.1
#> 7 ATG AG Antigua and Barbuda Americas Latin America & Ca… High … 77.2
#> 8 ARG AR Argentina Americas Latin America & Ca… Upper… 75.9
#> 9 ARM AM Armenia Asia Europe & Central A… Upper… 73.4
#> 10 ABW AW Aruba Americas Latin America & Ca… High … 75.4
#> # ℹ 206 more rows
# }