Accelerate hwf by first dividing countries into two groups:
For countries with a 2018 value greater than or equal to the 2018 global median, business as usual is returned.
For countries with a 2018 value less than the 2018 global median, the average of the top 5 rate of change within all countries.
Usage
accelerate_hwf(
df,
ind_ids = billion_ind_codes("uhc"),
scenario_col = "scenario",
value_col = "value",
start_year = 2018,
default_scenario = "default",
bau_scenario = "historical",
scenario_name = "acceleration",
...
)Arguments
- df
Data frame in long format, where 1 row corresponds to a specific country, year, and indicator.
- 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.
- value_col
Column name of column with indicator values.
- start_year
Base year for contribution calculation, defaults to 2018.
- default_scenario
name of the default scenario.
- bau_scenario
name of scenario to be used for business as usual. Default is
historical.- scenario_name
name of scenario
- ...
additional parameters to be passed to scenario function
See also
UHC acceleration scenarios
accelerate_anc4(),
accelerate_art(),
accelerate_beds(),
accelerate_bp(),
accelerate_doctors(),
accelerate_dtp3(),
accelerate_fh(),
accelerate_fpg(),
accelerate_fp(),
accelerate_itn(),
accelerate_nurses(),
accelerate_pneumo(),
accelerate_tb(),
accelerate_uhc_sanitation(),
accelerate_uhc_tobacco()