A compact international sample of period life tables for selected countries in 2023. The dataset is intended for recent and simple examples involving central death rates, one-year death probabilities, survival probabilities, and life-table calculations.
Format
A tibble with 3,939 rows and 13 variables:
- country
Country name.
- country_code
Numeric ISO country code.
- continent
Continent.
- region
Geographic region.
- year
Calendar year.
- sex
Sex category:
"male","female", or"both".- x
Integer actuarial age, from 0 to 100.
- mx
Central death rate at age
x.- qx
One-year death probability between ages
xandx + 1.- px
One-year survival probability between ages
xandx + 1.- lx
Number of survivors at exact age
x, based onl0 = 100000.- dx
Expected number of deaths between ages
xandx + 1.- source
Data source.
Details
The dataset is derived from central death rates mx. Death probabilities
qx were computed using the annual approximation
$$
q_x = \frac{m_x}{1 + (1 - a_x)m_x},
$$
with \(a_x = 0.5\). The last available age is closed by setting
qx = 1. Survivors lx and expected deaths dx are then
reconstructed recursively from l0 = 100000.
This dataset is a selected-country sample, not a complete world mortality database.
Examples
data(mortality_world_sample_2023)
mortality_world_sample_2023 |>
dplyr::filter(country == "Colombia", sex == "both") |>
dplyr::select(x, mx, qx, px, lx, dx)
#> # A tibble: 101 × 6
#> x mx qx px lx dx
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 0.00992 0.00987 0.990 100000 987.
#> 2 1 0.000720 0.000719 0.999 99013. 71.2
#> 3 2 0.000573 0.000573 0.999 98942. 56.7
#> 4 3 0.000460 0.000460 1.000 98885. 45.5
#> 5 4 0.000375 0.000375 1.000 98840. 37.0
#> 6 5 0.000311 0.000311 1.000 98802. 30.7
#> 7 6 0.000266 0.000266 1.000 98772. 26.2
#> 8 7 0.000237 0.000237 1.000 98746. 23.4
#> 9 8 0.000223 0.000223 1.000 98722. 22.0
#> 10 9 0.000222 0.000222 1.000 98700. 21.9
#> # ℹ 91 more rows