HPOP Billion calculation

To calculate the HPOP Billion, there are a series of functions made available through the billionaiRe package:

• transform_hpop_data() to transform raw values into normalized values used within the calculations.
• add_hpop_populations() to get relevant population groups for each country and indicator.
• calculate_hpop_contributions() to calculate indicator level changes and contributions to the Billion.
• calculate_hpop_billion() to calculate indicator level changes, country-level Billion, adjusting for double counting, and all contributions.

Run in sequence, these can calculate the entire HPOP Billion, or they can be run separately to produce different outputs as required. Details on the inputs of each function are available in their individual documentation, but below you can see the quick and easy Billions calculation done using the sample fake HPOP data provided in the package, hpop_df.

library(billionaiRe)

hpop_df %>%
  transform_hpop_data() %>%
  add_hpop_populations() %>%
  calculate_hpop_billion() %>%
  dplyr::filter(stringr::str_detect(ind, "hpop_healthier"))
#> # A tibble: 6 × 10
#>   iso3   year ind            value type  trans…¹ popul…² contr…³ contr…⁴ contr…⁵
#>   <chr> <dbl> <chr>          <dbl> <chr>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
#> 1 AFG    2023 hpop_healthie…    NA NA         NA  4.45e7  2.72e7    61.2      NA
#> 2 AFG    2023 hpop_healthie…    NA NA         NA  4.45e7 -3.84e7   -86.3      NA
#> 3 AFG    2023 hpop_healthier    NA NA         NA  4.45e7 -1.12e7   -25.1      NA
#> 4 AFG    2023 hpop_healthie…    NA NA         NA  4.45e7  3.20e7    71.9      NA
#> 5 AFG    2023 hpop_healthie…    NA NA         NA  4.45e7 -7.46e7  -168.       NA
#> 6 AFG    2023 hpop_healthie…    NA NA         NA  4.45e7 -4.26e7   -95.7      NA
#> # … with abbreviated variable names ¹​transform_value, ²​population,
#> #   ³​contribution, ⁴​contribution_percent, ⁵​contribution_percent_total_pop