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Reconcile and join two messy country tables

Source: R/join.R
country_join.Rd

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"),
  key = c("iso3c", "cowc", "cown", "gwn"),
  warn = TRUE
)

Arguments

x, y

Data frames to join.

by_x, by_y

The country columns in x and y (unquoted).

origin_x, origin_y

How to read each key (countrycode origin schemes).

type

Join type: "left" (default), "inner" or "full".

suffix

Suffix for clashing non-key columns (default c(".x", ".y")).

key

Which code system to join on. "iso3c" (default) is the package's spine and the right choice for anything contemporary. "cowc"/"cown" (Correlates of War) and "gwn" (Gleditsch-Ward) are the alternate spines historical work needs – see the section below.

warn

Whether to report values that resolve to no country (default TRUE). They join to nothing, so a silent reconciliation failure is the one thing this verb exists to prevent. Each side is reported separately.

Value

A tibble joined on a reconciled iso3c key.

Joining historical data

the second spine: ISO 3166 was first published in 1974 and never covered colonies, so iso3c cannot key anything before about 1970. Correlates of War and Gleditsch-Ward codes can, they run back to the nineteenth century, and historical_geometry() is keyed on gwn. Setting key switches the join onto one of those:

country_join(a, b, country, nation, key = "gwn")

The trade-off is real and worth stating: COW/GW codes cover states ISO never did, but they omit the dependencies and non-sovereign territories ISO does cover, so a modern dataset joined on gwn loses Hong Kong, Puerto Rico and the rest – which the join warns about. Use iso3c unless you are working before 1970.

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)
#> # A tibble: 2 × 5
#>   country       gdp iso3c nation           pop
#>   <chr>       <dbl> <chr> <chr>          <dbl>
#> 1 Czechia         1 CZE   Czech Republic    10
#> 2 South Korea     2 KOR   Korea, Rep.       51