Scenario dip and recover

scenario_dip_recover() creates a scenario where there is a rapid return to the previous situation after a dip. The same annual rate of change (AROC) as before dip is applied from the recovery_year.

Usage

scenario_dip_recover(
  df,
  start_year = 2018,
  dip_year = 2020,
  recovery_year = 2021,
  progressive_recovery = FALSE,
  aroc_type = c("lastest_year", "average_years_in_range"),
  aroc_start_year = start_year,
  end_year = 2025,
  value_col = "value",
  scenario_col = "scenario",
  scenario_name = "dip_recover",
  ind_ids = billion_ind_codes("all"),
  default_scenario = "default",
  source = sprintf("WHO DDI, %s", format(Sys.Date(), "%B %Y")),
  ...
)

scenario_dip_recover_iso3(
  df,
  iso3,
  start_year = 2018,
  dip_year = 2020,
  recovery_year = 2021,
  progressive_recovery = FALSE,
  aroc_type = c("lastest_year", "average_years_in_range"),
  aroc_start_year = start_year,
  end_year = 2025,
  value_col = "value",
  scenario_col = "scenario",
  scenario_name = "dip_recover",
  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"),
  source = sprintf("WHO DDI, %s", format(Sys.Date(), "%B %Y")),
  default_scenario = "default"
)

scenario_dip_recover_iso3_ind(
  df,
  iso3,
  ind,
  dip_year = 2020,
  recovery_year = 2021,
  progressive_recovery = FALSE,
  aroc_type = c("lastest_year", "average_years_in_range"),
  aroc_start_year = start_year,
  baseline_year = 2018,
  last_year = NULL,
  start_year = 2018,
  end_year = 2025,
  value_col = "value",
  scenario_col = "scenario",
  scenario_name = "dip_recover",
  ind_ids = billion_ind_codes("all"),
  default_scenario = "default",
  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,
  source = sprintf("WHO DDI, %s", format(Sys.Date(), "%B %Y"))
)

Arguments

df

Data frame in long format, where 1 row corresponds to a specific country, year, and indicator.

start_year

Base year for contribution calculation, defaults to 2018.

dip_year

(integer) year where the dip appends

recovery_year

(integer) year from which the AROC will be applied

progressive_recovery

(logical) TRUE if the recovery after dip should be progressive.

aroc_type

(character) name of the type of AROC to be used. Can be either:

lastest_year: only the latest reported/estimated year between start_year and dip_year is used to calculate
average_years_in_range:

aroc_start_year

(integer) year

end_year

End year(s) for contribution calculation, defaults to 2019 to 2025.

value_col

Column name of column with indicator values.

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 scenario

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.

source

Source to provide for calculated average service coverage and single measure.

...

additional parameters to be passed to scenario_dip_recover_iso3()

iso3

(character) ISO3 code of country to scenario

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

upper limit at which the indicator should be caped.

lower_limit

lower_limit limit at which the indicator should be caped.

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

(character) name of the indicator on which to calculate the scenario

baseline_year

(integer) identify baseline year on which the AROC should be calculated.

last_year

(integer) identify last year where values were reported or estimated between start_year and end_year.

scenario

name of scenario column to be created

Value

a data frame with scenario values in value_col with a scenario column.

Details

scenario_dip_recover_iso3() applies scenario_dip_recover() to a specific iso3.

scenario_dip_recover_iso3_ind() applies scenario_dip_recover() to a specific ind and iso3 combination.

Two types of AROC are supported:

lastest_year: the AROC is calculated the values between the start_year and the last reported or estimated value before dip_year is applied to the last reported value to recovery_year onward.
average_years_in_range: all AROC between the aroc_start_year and the last reported or estimated value before dip_year are calculated. The average AROC for that period is then applied to the last reported value to recovery_year onward.

If there are missing values between dip_year and recovery_year, the last value from dip_year is carried forward. This applies only to countries where the indicator value for dip_year is reported or estimated. Otherwise, the value is carried with scenario_bau.

If progressive_recovery is TRUE, then the recovery is spread between the years between recovery_year and end_year. For instance, if recovery_year is 2021 and end_year 2025, then 2021 will have 0% of AROC, 2022 25%, 2023 50%, 2024 75%, and 2025 100%.

Usage

Arguments

Value

Details

See also