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Transform Raw Indicator Values for HEP Billion

Source: R/transform_hep.R
transform_hep_data.Rd

transform_hep_data() applies transformations on HEP Billion indicators so that transformed indicator values can be used within Billions calculations. Details on the specific transformations applied can be found within the Billions methods report.

Usage

transform_hep_data(
  df,
  scenario_col = NULL,
  value_col = "value",
  transform_glue = "transform_{value_col}",
  source = "WUENIC/IVB/WHO Technical Programme",
  ind_ids = billion_ind_codes("hep", include_calculated = TRUE),
  extrapolate_to = 2025,
  recycle = FALSE,
  ...
)

Arguments

df

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

scenario_col

Column name of column with scenario identifiers. Useful for calculating contributions on data in long format rather than wide format.

value_col

Column name of column with indicator values.

transform_glue

Glue expression to be passed to glue::glue(). Defaults to 'transform_{value_col}' which will create new column names by prefixing transform_ to the original name.

source

Source to use for prevent data that is flat extrapolated that has more than one unique value.

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().

extrapolate_to

Year to extrapolate Prevent data to, defaults to 2025

recycle

Boolean to indicate if data should be recycled

...

additional parameters to to pass to recycle_data

Value

Data frame in long format.

Details

Currently, this function only changes Prevent campaign data by calculating the total sum of campaigns for each year for use in Prevent calculations. For more details on the HEP Billion calculation process and how this function ties in with the rest, see the vignette:

vignette("hep", package = "billionaiRe")

See also

HEP functions add_hep_populations(), calculate_hep_billion(), calculate_hep_components(), hep_df, transform_prev_cmpgn_data(), transform_prev_routine_data()