R/world_population_data.R
world_population_data.RdWorld population details from United Nations
world_population_data()a data.frame
res = world_population_data()
colnames(res)
#> [1] "gender" "variant" "region_type"
#> [4] "notes" "region_code" "data_category"
#> [7] "parent_region_code" "year" "age_group"
#> [10] "population"
dplyr::glimpse(res)
#> Rows: 760,410
#> Columns: 10
#> $ gender <chr> "male", "male", "male", "male", "male", "male", "ma…
#> $ variant <chr> "Estimates", "Estimates", "Estimates", "Estimates",…
#> $ region_type <chr> "WORLD", "WORLD", "WORLD", "WORLD", "WORLD", "WORLD…
#> $ notes <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
#> $ region_code <dbl> 900, 900, 900, 900, 900, 900, 900, 900, 900, 900, 9…
#> $ data_category <chr> "World", "World", "World", "World", "World", "World…
#> $ parent_region_code <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ year <dbl> 1950, 1950, 1950, 1950, 1950, 1950, 1950, 1950, 195…
#> $ age_group <chr> "0-4", "5-9", "10-14", "15-19", "20-24", "25-29", "…
#> $ population <dbl> 172419832, 138298389, 133685702, 122155285, 1132065…
head(res)
#> # A tibble: 6 × 10
#> gender variant region_type notes region_code data_category parent_region_co…
#> <chr> <chr> <chr> <chr> <dbl> <chr> <dbl>
#> 1 male Estimates WORLD NA 900 World 0
#> 2 male Estimates WORLD NA 900 World 0
#> 3 male Estimates WORLD NA 900 World 0
#> 4 male Estimates WORLD NA 900 World 0
#> 5 male Estimates WORLD NA 900 World 0
#> 6 male Estimates WORLD NA 900 World 0
#> # … with 3 more variables: year <dbl>, age_group <chr>, population <dbl>