scenario_quantile: Scenario to add a linear percentage point aimed at quantiles

In gpw13/billionaiRe: Calculate the WHO Triple Billions

Scenario to add a linear percentage point aimed at quantiles

Description

scenario_quantile aims to reach the mean quantile average annual change (ARC) in which a country is at quantile_year. The target is based on the ARC between quantile_year and baseline_quantile_year. If ARC is under the mean of the quantile, it will aim at the mean, and at the higher limit of the quantile if above the mean.

Usage

scenario_quantile(
  df,
  n = 5,
  value_col = "value",
  start_year = 2018,
  end_year = 2025,
  quantile_year = start_year,
  baseline_quantile_year = start_year - 5,
  baseline_year = start_year,
  scenario_name = glue::glue("quantile_{n}"),
  scenario_col = "scenario",
  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,
  ind_ids = billion_ind_codes("all"),
  default_scenario = "default"
)

Arguments

df	Data frame in long format, where 1 row corresponds to a specific country, year, and indicator.
n	number of quantile to create (5 for quintile, 4 for quartiles, etc.)
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
quantile_year	year at which the the quantiles ARC should be calculated.
baseline_quantile_year	baseline year at which the quantiles ARC should be calculated.
baseline_year	Year from which the scenario is measured. Defaults to start_year
scenario_name	Name of the scenario. Defaults to scenario_percent_change_baseline_year
scenario_col	Column name of column with scenario identifiers. Useful for calculating contributions on data in long format rather than wide format.
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.
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().
default_scenario	name of the default scenario to be used.

Details

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

See Also

Comparing scenarios scenario_bau(), scenario_best_in_region(), scenario_best_of()

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