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Aggregate PSUT matrices into regions

Source: R/aggregates.R
region_aggregates.Rd

Aggregates a data frame according to the regions given in a column of the data frame. The data frame (.sut_data) should contain metadata columns (including many_colname and few_colname) and be wide-by-matrices.

Usage

region_aggregates(
  .sut_data,
  many_colname = IEATools::iea_cols$country,
  few_colname = Recca::aggregate_cols$region,
  drop_na_few = FALSE,
  year = IEATools::iea_cols$year,
  method = IEATools::iea_cols$method,
  energy_type = IEATools::iea_cols$energy_type,
  last_stage = IEATools::iea_cols$last_stage,
  matrix_cols = c(R = Recca::psut_cols$R, U = Recca::psut_cols$U, U_feed =
    Recca::psut_cols$U_feed, U_eiou = Recca::psut_cols$U_eiou, r_eiou =
    Recca::psut_cols$r_eiou, V = Recca::psut_cols$V, Y = Recca::psut_cols$Y, S_units =
    Recca::psut_cols$S_units, Y_fu_details = Recca::psut_cols$Y_fu_details,
    U_eiou_fu_details = Recca::psut_cols$U_eiou_fu_details),
  matrix_names = Recca::psut_cols$matnames,
  matrix_values = Recca::psut_cols$matvals
)

Arguments

.sut_data

A wide-by-matrices matsindf-style data frame of PSUT matrices.

many_colname

The name of the column in .sut_data that contains the "many" descriptions, for example countries that need to be aggregated to continents. Default is IEATools::iea_cols$country.

few_colname

The of the column in .sut_data that contains the "few" descriptions, for example continents into which countries are to be aggregated. Default is Recca::aggregate_cols$region.

drop_na_few

A boolean that tells whether to ignore (not aggregate) rows with NA values in few_colname. See details. Default is FALSE.

year, method, energy_type, last_stage

See IEATools::iea_cols.

matrix_cols

Names of columns in .sut_data containing matrices. Default is a vector of names from Recca::psut_cols: R, U, U_feed, U_eiou, r_eiou, V, Y, S_units, Y_fu_details, and U_eiou_fu_details.

matrix_names, matrix_values

Internal column names. See Recca::psut_cols.

Value

An aggregated version of .sut_data wherein the many_colname column is replaced by few_colname as specified by aggregation_map.

Details

The argument drop_na_few controls what happens when an item many_colname does not have a corresponding value in few_colname. This condition can occur when, say, "WRLD" is a country. "WRLD" (as a country in many_colname) should not be aggregated to "World" (as a region in the few_colname). In those circumstances, a well-formed aggregation_map will leave NA in few_colname. Setting drop_na_few to TRUE (default is FALSE) will eliminate rows with NA in few_colname before doing the aggregation so those NA rows do not end up as NA in the outgoing data frame.

The default value for drop_na_few is FALSE, because setting to TRUE will result in data loss. You need to opt in to this behavior when you know it's what you want.

If all of few_colname entries are NA and drop_na_few is TRUE, a zero-row data frame of the same structure as .sut_data is returned.

This function works for both a data frame of PSUT matrices and a data frame of details matrices.

Examples

library(dplyr)
library(matsbyname)
library(tidyr)
mats_GBR <- UKEnergy2000mats %>%
  tidyr::pivot_wider(names_from = matrix.name, values_from = matrix)
# Add other countries, by duplicating and renaming GBR
mats <- dplyr::bind_rows(mats_GBR,
                         mats_GBR %>% dplyr::mutate(Country = "USA"),
                         mats_GBR %>% dplyr::mutate(Country = "FRA"))
# Establish the aggregation map.
agg_df <- list(EUR = c("GBR", "FRA"), AMR = "USA") %>%
  matsbyname::agg_map_to_agg_table(few_colname = "Continent", many_colname = "Country")
# Aggregate into continents
dplyr::left_join(mats, agg_df, by = "Country") %>%
  region_aggregates(many_colname = "Country", few_colname = "Continent")
#> # A tibble: 8 × 12
#>    Year EnergyType LastStage Country R             S_units  U_EIOU   U_feed  
#>   <dbl> <chr>      <chr>     <chr>   <list>        <list>   <list>   <list>  
#> 1  2000 E          Final     AMR     <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 2  2000 E          Final     EUR     <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 3  2000 E          Services  AMR     <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 4  2000 E          Services  EUR     <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 5  2000 E          Useful    AMR     <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 6  2000 E          Useful    EUR     <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 7  2000 X          Services  AMR     <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 8  2000 X          Services  EUR     <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> # ℹ 4 more variables: V <list>, Y <list>, U <list>, r_EIOU <list>