Vanguard Regions with Highest Life Expectancy

Convenience function returning regions classified as "vanguard" - those with the highest life expectancy and sustained improvement trends.

Usage

vanguard_regions(country = NULL, year = NULL, sex = NULL)

Arguments

country

Character vector. Filter to specific countries using ISO 2-letter codes (e.g., "FR", "DE", "ES"). Default NULL returns all countries.

year

Integer or vector. Filter to specific years. Default NULL returns all years (1992-2023).

sex

Character. Filter by sex: "Male", "Female", or "Total". Default NULL returns all.

Value

A tibble filtered to vanguard regions only.

See also

regional_life_expectancy(), laggard_regions()

Other regional: laggard_regions(), regional_life_expectancy(), regional_mortality_multiplier()

Examples

# Vanguard regions in 2019
vanguard_regions(year = 2019, sex = "Total")
#> # A tibble: 14 × 9
#>    region_code region_name              country_code  year sex   life_expectancy
#>    <chr>       <chr>                    <chr>        <int> <chr>           <dbl>
#>  1 CH01        Région lémanique         CH            2019 Total            84.6
#>  2 CH03        Nordwestschweiz          CH            2019 Total            84  
#>  3 CH06        Zentralschweiz           CH            2019 Total            84.4
#>  4 ES13        Cantabria                ES            2019 Total            83.9
#>  5 ES22        Comunidad Foral de Nava… ES            2019 Total            85  
#>  6 ES24        Aragón                   ES            2019 Total            84.4
#>  7 ES30        Comunidad de Madrid      ES            2019 Total            85.8
#>  8 ES51        Cataluña                 ES            2019 Total            84.3
#>  9 ES53        Illes Balears            ES            2019 Total            84.2
#> 10 FI20        Åland                    FI            2019 Total            83.9
#> 11 FRM0        Corse                    FR            2019 Total            84  
#> 12 ITI4        Lazio                    IT            2019 Total            83.8
#> 13 NO01        Oslo og Akershus (stati… NO            2019 Total            83.8
#> 14 SE11        Stockholm                SE            2019 Total            83.9
#> # ℹ 3 more variables: microlives_vs_eu_avg <dbl>, classification <chr>,
#> #   source_url <chr>