HEP Billion Calculation

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

• transform_hep_data() to transform raw values into normalized values used within the calculations. For now, this is primarily calculating the total prevent numerators and denominators for campaign and routine data.
• calculate_hep_components() to calculate component indicators (Prevent coverages), the HEP index, and levels for all components.
• calculate_hep_billion() to calculate the change for the three HEP components (DNR, Prepare, and Prevent), their contribution to the Billion, and overall HEPI change and contribution.

Run in sequence, these can calculate the entire HEP 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 HEP data provided in the package, hep_df.

library(billionaiRe)

hep_df %>%
  transform_hep_data() %>%
  calculate_hep_components() %>%
  calculate_hep_billion(end_year = 2023) %>%
  dplyr::filter(ind %in% c("prevent",
                           "espar",
                           "detect_respond",
                           "hep_idx"),
                year == 2023)
#> # A tibble: 4 × 12
#>   iso3   year ind       value type  source trans…¹ use_d…² use_c…³ level contr…⁴
#>   <chr> <dbl> <chr>     <dbl> <chr> <chr>    <dbl> <lgl>   <lgl>   <dbl>   <dbl>
#> 1 AFG    2023 espar      51.2 Proj… NA        51.2 NA      NA          3  5.00e6
#> 2 AFG    2023 detect_r…  91   Proj… NA        91   NA      NA          5  2.23e6
#> 3 AFG    2023 prevent    NA   proj… Unite…   100   NA      NA          5  0     
#> 4 AFG    2023 hep_idx    NA   proj… WHO D…    80.7 NA      NA          4  7.22e6
#> # … with 1 more variable: contribution_percent <dbl>, and abbreviated variable
#> #   names ¹​transform_value, ²​use_dash, ³​use_calc, ⁴​contribution