View source: R/accelerate_hpop.R
| accelerate_pm25 | R Documentation |
Accelerate pm25 by picking the best value between business as usual, and
linear change of -2% * 2018 value_col per year.
accelerate_pm25(
df,
ind_ids = billion_ind_codes("hpop"),
scenario_col = "scenario",
value_col = "value",
default_scenario = "default",
bau_scenario = "historical",
scenario_name = "acceleration",
...
)
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
|
scenario_col |
Column name of column with scenario identifiers. Useful for calculating contributions on data in long format rather than wide format. |
value_col |
Name of the column containing indicator value in |
default_scenario |
name of the default scenario. |
bau_scenario |
name of scenario to be used for business as usual.
Default is |
scenario_name |
name of scenario |
... |
additional parameters to be passed to scenario function |
Runs:
scenario_bau(df, small_is_best = TRUE,...)
scenario_linear_change(df, linear_value = df$value_col[df$"year" == 2018] * -0.02, small_is_best = TRUE,...)
Then picks the best result between the two scenarios.
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_road(),
accelerate_stunting(),
accelerate_suicide(),
accelerate_transfats(),
accelerate_wasting(),
accelerate_water_rural(),
accelerate_water_urban(),
accelerate_water()
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