accelerate_espar: Accelerate espar

In gpw13/billionaiRe: Calculate the WHO Triple Billions

Accelerate espar

Description

@description

Usage

accelerate_espar(
  df,
  value_col = "value",
  ind_ids = billion_ind_codes("hep"),
  scenario_col = "scenario",
  start_year = 2018,
  baseline_year = 2018,
  end_year = 2025,
  default_scenario = "default",
  scenario_name = "acceleration",
  ...
)

Arguments

df	Data frame in long format, where 1 row corresponds to a specific country, year, and indicator.
value_col	Column name of column with indicator values.
ind_ids	Named vector of indicator codes for input indicators to the Billion. Although separate indicator codes can be used than the standard, they must be supplied as a named vector where the names correspond to the output of billion_ind_codes().
scenario_col	Column name of column with scenario identifiers. Useful for calculating contributions on data in long format rather than wide format.
start_year	Base year for contribution calculation, defaults to 2018.
baseline_year	Year from which the scenario is measured. Defaults to start_year
end_year	End year(s) for contribution calculation, defaults to 2019 to 2025.
default_scenario	name of the default scenario.
scenario_name	Name of the scenario. Defaults to scenario_percent_change_baseline_year
...	additional parameters to be passed to scenario function

Details

accelerate_espar() accelerate espar by aiming at the best value between the regional average (WHO regions) and the value last year of the last year with complete espar data (with categories and sub-categories).

Value

data frame with acceleration scenario binded to df. scenario_col is set to acceleration

See Also

HEP acceleration scenarios accelerate_cholera_campaign(), accelerate_detect(), accelerate_measles_routine(), accelerate_meningitis_campaign(), accelerate_polio_routine(), accelerate_yellow_fever_campaign()

gpw13/billionaiRe documentation built on Sept. 27, 2024, 10:05 p.m.