Scenario to change by a fixed percentage from a baseline value by target year

This scenario allows to change a value by a fixed percentage to a provided year from a provided baseline year. It provides values for scenarios stated as "Reduce INDICATOR by XX% by YEAR"

Usage

scenario_percent_baseline(
  df,
  percent_change,
  value_col = "value",
  start_year = 2018,
  end_year = 2025,
  baseline_year = start_year,
  target_year = end_year,
  scenario_col = "scenario",
  scenario_name = glue::glue("{percent_change}_{baseline_year}"),
  trim = TRUE,
  small_is_best = FALSE,
  keep_better_values = FALSE,
  upper_limit = "guess",
  lower_limit = "guess",
  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.

percent_change

Numeric with the percentage change in points that is to be achieved from value_col in baseline_year by target_year. Should be expressed a percentage point and not a fraction of 100 (e.g. 6% increase = 6, and not 0.06). For an increase, use a positive numeric, and a negative one for a decrease.

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

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.

Value

Dataframe with scenario rows

Details

The percent_change parameter is understood as a percentage change, and not a percentage point change, as this is usually what intended by those formulations. If it is indeed the percentage change that is required, please use scenario_aroc. For instance, to calculate the scenario "reduce the 2018 value (90%) by 30% by 2025", will results to a 2025 value of 63% and not 60%.

The returned scenario is a portion of the straight line drawn from the baseline_year value to the target_year. Only values for years between start_year and end_year will be returned.

See also

Basic scenarios scenario_aroc(), scenario_bau(), scenario_best_of(), scenario_linear_change()