Accelerate suicide by picking the best results between business as usual, halt downwards trend, and -33.333% points between 2015 and 2030.
Usage
accelerate_suicide(
df,
ind_ids = billion_ind_codes("hpop"),
scenario_col = "scenario",
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.
- 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
Details
Runs:
scenario_bau(df, small_is_best = TRUE,...),scenario_percent_baseline(df, percent_change = -33.333, baseline_year = 2015, target_year = 2030, small_is_best = TRUE, ...)scenario_halt_rise(df, small_is_best = TRUE,...)
Then picks the best result between the three scenarios.
See also
HPOP acceleration scenarios
accelerate_adult_obese(),
accelerate_alcohol(),
accelerate_child_obese(),
accelerate_child_viol(),
accelerate_devontrack(),
accelerate_fuel(),
accelerate_hpop_sanitation_rural(),
accelerate_hpop_sanitation_urban(),
accelerate_hpop_sanitation(),
accelerate_hpop_tobacco(),
accelerate_ipv(),
accelerate_overweight(),
accelerate_pm25(),
accelerate_road(),
accelerate_stunting(),
accelerate_transfats(),
accelerate_wasting(),
accelerate_water_rural(),
accelerate_water_urban(),
accelerate_water()