Accelerate road by picking the best results between business as usual, and -50 percent from 2020 to 2030.
Usage
accelerate_road(
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,...)
, andscenario_percent_baseline(df, percent_change = -50, baseline_year = 2020, target_year = 2030, small_is_best = TRUE, ...)
Then picks the best result between the two 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_stunting()
,
accelerate_suicide()
,
accelerate_transfats()
,
accelerate_wasting()
,
accelerate_water_rural()
,
accelerate_water_urban()
,
accelerate_water()