Scenario to add a linear point change

scenario_linear_change() to add a linear_value percentage point change to baseline_value from a baseline_year. It provides values for scenarios stated as "Increase INDICATOR by XX% points".

scenario_linear_change_col() wraps around scenario_linear_change() to provide linear values from a column specified in linear_value rather than a single value.

The calculation is done by taking the baseline_year value_col and adding the linear_value times the number of years between the baseline year and the current year. For instance, if baseline_year is 2018, linear_value is 2, and baseline_year value_col is 10, then 2019 value_col will be 12, 2020 14, etc.

It differs from scenario_aroc() percent_change in two ways: it is not compounded and it adds percentage points and not percentage of values.

upper_limit and lower_limit allow to trim values when they are exceeding the bounds after calculations. If values were already exceeding the bounds before calculations, they are kept.

Usage

scenario_linear_change(
  df,
  linear_value,
  value_col = "value",
  start_year = 2018,
  end_year = 2025,
  baseline_year = start_year,
  target_year = end_year,
  scenario_name = glue::glue("linear_change"),
  scenario_col = "scenario",
  trim = TRUE,
  small_is_best = FALSE,
  keep_better_values = FALSE,
  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"
)

scenario_linear_change_col(
  df,
  linear_value_col,
  value_col = "value",
  start_year = 2018,
  end_year = 2025,
  baseline_year = start_year,
  target_year = end_year,
  scenario_col = "scenario",
  scenario_name = glue::glue("linear_change"),
  trim = TRUE,
  small_is_best = FALSE,
  keep_better_values = FALSE,
  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.
linear_value: vector indicating the increase to apply.
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_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: numeric indicating the upper bound of the data after calculation. If value_col is already higher before calculation it will be kept
lower_limit: numeric indicating the lower bound of the data after calculation. If value_col is already lower before calculation it will be kept
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.
linear_value_col: name of column with linear values

Usage

Arguments

See also