A convenience function to create PSUT matrices in a data frame

Starting from human and animal muscle work data frames, this function bundles several functions to create a data frame of PSUT matrices. The bundled functions are:

specify_energy_type_method(),
specify_product(),
specify_ktoe() or specify_TJ(), depending on the value of output_unit,
MWTools::specify_primary_production(),
specify_useful_products(),
specify_fu_machines(),
specify_last_stages(),
MWTools::add_row_col_meta(),
MWTools::collapse_to_psut(),
calc_S_units(), and
calc_U_feed_U_eiou_r_eiou().

Usage

prep_psut(
  .hmw_df,
  .amw_df,
  matrix_class = c("matrix", "Matrix"),
  output_unit = c("TJ", "ktoe"),
  unit = IEATools::iea_cols$unit,
  R = IEATools::psut_cols$R,
  U = IEATools::psut_cols$U,
  V = IEATools::psut_cols$V,
  Y = IEATools::psut_cols$Y,
  s_units = IEATools::psut_cols$s_units,
  U_feed = IEATools::psut_cols$U_feed,
  U_eiou = IEATools::psut_cols$U_eiou,
  r_eiou = IEATools::psut_cols$r_eiou
)

Arguments

.hmw_df: A data frame produced by calc_hmw_pfu().
.amw_df: A data frame produced by calc_amw_pfu().
matrix_class: The type of matrix to be created, one of "matrix" or "Matrix". Default is "matrix".
output_unit: A string of length one that specifies the output unit. One of "TJ" or "ktoe" for terajoules or kilotons of oil equivalent.
unit, R, U, V, Y, s_units, U_feed, U_eiou, r_eiou: Column names. See IEATools::psut_cols.

Value

A data frame of muscle work PSUT matrices.

Details

Default values are assumed for function arguments.

The "Unit" column is deleted after the "S_units" column is created.

Examples

ilo_working_hours_data <- read.csv(file = MWTools::ilo_working_hours_test_data_path())
ilo_employment_data <- read.csv(file = MWTools::ilo_employment_test_data_path())
hmw_data <- prepareRawILOData(ilo_working_hours_data = ilo_working_hours_data,
                              ilo_employment_data = ilo_employment_data)
hmw_df <- hmw_data %>%
  calc_hmw_pfu() %>%
  # Keep only a few years for speed.
  dplyr::filter(Year %in% 2000:2002)
amw_df <- amw_test_data_path() %>%
  read.csv() %>%
  calc_amw_pfu() %>%
  # Keep only a few years for speed.
  dplyr::filter(Year %in% 2000:2002)
prep_psut(hmw_df, amw_df)
#> # A tibble: 12 × 13
#>    Country  Year Method Energy.type Last.stage R             U        V       
#>    <chr>   <dbl> <chr>  <chr>       <chr>      <list>        <list>   <list>  
#>  1 CHNM     2000 PCM    E           Final      <dbl [1 × 1]> <dbl[…]> <dbl[…]>
#>  2 CHNM     2000 PCM    E           Useful     <dbl [1 × 1]> <dbl[…]> <dbl[…]>
#>  3 CHNM     2001 PCM    E           Final      <dbl [1 × 1]> <dbl[…]> <dbl[…]>
#>  4 CHNM     2001 PCM    E           Useful     <dbl [1 × 1]> <dbl[…]> <dbl[…]>
#>  5 CHNM     2002 PCM    E           Final      <dbl [1 × 1]> <dbl[…]> <dbl[…]>
#>  6 CHNM     2002 PCM    E           Useful     <dbl [1 × 1]> <dbl[…]> <dbl[…]>
#>  7 GBR      2000 PCM    E           Final      <dbl [1 × 1]> <dbl[…]> <dbl[…]>
#>  8 GBR      2000 PCM    E           Useful     <dbl [1 × 1]> <dbl[…]> <dbl[…]>
#>  9 GBR      2001 PCM    E           Final      <dbl [1 × 1]> <dbl[…]> <dbl[…]>
#> 10 GBR      2001 PCM    E           Useful     <dbl [1 × 1]> <dbl[…]> <dbl[…]>
#> 11 GBR      2002 PCM    E           Final      <dbl [1 × 1]> <dbl[…]> <dbl[…]>
#> 12 GBR      2002 PCM    E           Useful     <dbl [1 × 1]> <dbl[…]> <dbl[…]>
#> # ℹ 5 more variables: Y <list>, S_units <list>, U_feed <list>, U_EIOU <list>,
#> #   r_EIOU <list>