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Scenario to add a linear percentage point aimed at quantiles

Source: R/scenarios_target_specific_values.R
scenario_quantile.Rd

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()