scenario_best_in_region: Scenario to add a linear percentage point aimed at regional...

In gpw13/billionaiRe: Calculate the WHO Triple Billions

Scenario to add a linear percentage point aimed at regional values

Description

scenario_best_in_region aims to reach the mean regional average annual change in which a country is at quantile_year. The target is based on the ARC between quantile_year and quantile_year - 5. If ARC is under the mean of the region, it will aim at the mean, and at the best value of the quantile if above the mean. small_is_best can be used to indicate is lower value is best or not.

Usage

scenario_best_in_region(
  df,
  value_col = "value",
  start_year = 2018,
  end_year = 2025,
  baseline_year = 2018,
  target_year = 2013,
  scenario_col = "scenario",
  scenario_name = "best_in_region",
  ind_ids = billion_ind_codes("all"),
  trim = TRUE,
  small_is_best = FALSE,
  keep_better_values = TRUE,
  upper_limit = 100,
  lower_limit = 0,
  trim_years = TRUE,
  start_year_trim = start_year,
  end_year_trim = end_year,
  default_scenario = "default"
)

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.
start_year	Start year for scenario, defaults to 2018.
end_year	End year for scenario, defaults to 2025
baseline_year	Year from which the scenario is measured. Defaults to start_year
target_year	Year by which the scenario should eventually be achieved. Defaults to end_year
scenario_col	Column name of column with scenario identifiers. Useful for calculating contributions on data in long format rather than wide format.
scenario_name	Name of the scenario. Defaults to scenario_percent_change_baseline_year
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().
trim	logical to indicate if the data should be trimmed between upper_limit and lower_limit.
small_is_best	Logical to identify if a lower value is better than a higher one (e.g. lower obesity in a positive public health outcome, so obesity rate should have small_is_best = TRUE).
keep_better_values	logical to indicate if "better" values should be kept from value_col if they are present. Follows the direction set in small_is_best. For instance, if small_is_best is TRUE, then value_col lower than col will be kept.
upper_limit	limit at which the indicator should be caped. Can take any of "guess", or any numeric. guess (default) will take 100 as the limit if percent_change is positive, and 0 if negative.
lower_limit	limit at which the indicator should be caped. Can take any of "guess", or 0 to 100. guess (default) will take 0 as the limit if percent_change is positive, and 100 if negative.
trim_years	logical to indicate if years before start_year_trim and after end_year_trim should be removed
start_year_trim	(integer) year to start trimming from.
end_year_trim	(integer) year to end trimming.
default_scenario	name of the default scenario to be used.

Details

Calculates the regional target, then wraps around scenario_linear_change_col to aim at the target.

See Also

Comparing scenarios scenario_bau(), scenario_best_of(), scenario_quantile()

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