scenario_covid_rapid_return() creates a scenario where there is a rapid
return to the pre-pandemic situation after a dip due to COVID-19.
Usage
scenario_covid_rapid_return(
df,
start_year = 2018,
covid_year = 2020,
recovery_year = 2022,
end_year = 2025,
value_col = "value",
scenario_col = "scenario",
scenario_name = "covid_rapid_return",
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.
- start_year
Base year for contribution calculation, defaults to 2018.
- covid_year
(integer) year where the values are impacted by COVID.
- recovery_year
integer year from which the AROC will be applied. Default to 2022.
- end_year
End year(s) for contribution calculation, defaults to 2019 to 2025.
- value_col
Column name of column with indicator values. This column will be used to return the results.
- scenario_col
Column name of column with scenario identifiers.
- 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.
- ...
additional parameters to be passed to
scenario_dip_recover_iso3()- scenario
name of scenario column to be created
Details
In details, the AROC between the start_year and covid_year - 1 is
applied to the last reported value to recovery_year onward. If there are
missing values between covid_year and recovery_year, the last value from
covid_year is carried forward. This applies only to countries where the
indicator value for covid_year is reported or estimated. Otherwise,
the value is carried with scenario_bau.