Join many messy country tables on the ISO spine

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

Arguments

tables

A list of data frames.

by

A single country-column name present in every table, or a character vector giving the column for each table.

origin

countrycode origin scheme(s) for the key column(s) (default "country.name"; length 1 or one per table).

type

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

key

Which code system to join on, as in country_join(): "iso3c" (default), or "cowc"/"cown"/"gwn" for historical work that predates ISO 3166. Each table reports separately on the countries the alternate key cannot carry.

warn

Whether to report values that resolve to no country (default TRUE), per table, as country_join() does per side.

Value

A single tibble joined on key (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")
#> # A tibble: 2 × 7
#>   country.x     gdp iso3c country.y        pop country  area
#>   <chr>       <dbl> <chr> <chr>          <dbl> <chr>   <dbl>
#> 1 Czechia         1 CZE   Czech Republic    10 Czechia    79
#> 2 South Korea     2 KOR   Korea, Rep.       51 Korea     100