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Convert a data.table from a regional mapping to another.

Source: R/convert_region.R
convert_region.Rd

convert_region returns a list containing a data.table where values are converted from one regional mapping to another. The conversion is done by downscaling values at country-level (if necessary) and updscaling them into the final regional mapping. The list also contain an optional data.table about the value coperture for the operator sumby. The required format of the input data is described in Details.

Usage

convert_region(
  .x,
  from_reg = NULL,
  to_reg,
  agg_operator = "sum",
  agg_weight = witchtools::default_weights[["gdp"]],
  agg_missing = "NA",
  regions = witchtools::region_mappings,
  info = FALSE
)

Arguments

.x

a well-formatted data.table.

from_reg

initial regional mapping name or a data.table with the mapping.

to_reg

final regional mapping name or a data.table with the mapping.

agg_operator

aggregation operator (See Details for the list of possible values)

agg_weight

aggregation weight data.table (See Details for the list of possible values)

agg_missing

tells how to deal with missing values ("NA" or "zero")

regions

optional list of region mappings (see Details for format)

info

logical indicating whether to include information, only required for the agg_operator "sumby".

Value

a list containing a converted data.table and information about the coperture if available.

Details

The input data.table should contain a column "value" and a column named by the initial regional mapping from_reg, while the other columns are are considered as id columns. The resulting data.table have a column named by the final regional mapping to_reg.

The regional mapping for from_reg and to_reg can be provided in a named list through the parameter regions. Regional mappings are 2-columns data.table with a column named 'iso3' (for country ISO3) and another one named as the regional mapping (for region name). The name in the list should also be the regional mapping name.

See also

convert_table, convert_gdx.

Other conversion functions: convert_duckdb(), convert_gdx(), convert_sqlite(), convert_table(), convert_time_period()

Examples

# Aggregate country-level GDP into the 17 WITCH regions
gdp_iso3 <- data.table::copy(default_weights[["gdp"]])
data.table::setnames(gdp_iso3, "weight", "value")

convert_region(gdp_iso3, to_reg = "witch17")
#>         witch17      value
#>          <char>      <num>
#>  1:        laca  3811.0076
#>  2:       sasia  2616.5509
#>  3:         ssa  3281.0841
#>  4:      europe 22137.4325
#>  5:        mena  7253.0736
#>  6:          te  8156.9139
#>  7:     oceania  1501.8456
#>  8:      brazil  2969.7366
#>  9:      seasia  4873.5174
#> 10:      canada  1746.1213
#> 11:       china 22961.7494
#> 12:   indonesia  3118.3992
#> 13:       india  8377.0372
#> 14:      jpnkor  7243.6577
#> 15:      mexico  2300.2179
#> 16:         usa 19910.1964
#> 17: southafrica   749.2962