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Chop the R and Y matrices and swim downstream/upstream

Source: R/chop.R
chop-doc.Rd

Chopping the resource (R) or final demand (Y) matrices involves isolating products and industries then swimming downstream/upstream to identify an energy conversion chain (ECC) associated with each resource or final demand category. These functions perform those calculations.

Usage

chop_Y(
  .sut_data = NULL,
  calc_pfd_aggs = TRUE,
  p_industries = NULL,
  fd_sectors = NULL,
  piece = "all",
  notation = RCLabels::notations_list,
  pattern_type = c("exact", "leading", "trailing", "anywhere", "literal"),
  prepositions = RCLabels::prepositions_list,
  unnest = FALSE,
  method = c("solve", "QR", "SVD"),
  tol_invert = .Machine$double.eps,
  tol_chop_sum = 1e-04,
  R = Recca::psut_cols$R,
  U = Recca::psut_cols$U,
  U_feed = Recca::psut_cols$U_feed,
  V = Recca::psut_cols$V,
  Y = Recca::psut_cols$Y,
  S_units = Recca::psut_cols$S_units,
  chop_df = Recca::aggregate_cols$chop_df,
  product_sector = Recca::aggregate_cols$product_sector,
  aggregate_primary = Recca::aggregate_cols$aggregate_primary,
  net_aggregate_demand = Recca::aggregate_cols$net_aggregate_demand,
  gross_aggregate_demand = Recca::aggregate_cols$gross_aggregate_demand,
  .prime = "_prime",
  R_colname = Recca::psut_cols$R,
  U_colname = Recca::psut_cols$U,
  U_feed_colname = Recca::psut_cols$U_feed,
  U_eiou_colname = Recca::psut_cols$U_eiou,
  r_eiou_colname = Recca::psut_cols$r_eiou,
  V_colname = Recca::psut_cols$V,
  Y_colname = Recca::psut_cols$Y,
  S_units_colname = Recca::psut_cols$S_units,
  R_prime_colname = paste0(R_colname, .prime),
  U_prime_colname = paste0(U_colname, .prime),
  U_feed_prime_colname = paste0(U_feed_colname, .prime),
  U_eiou_prime_colname = paste0(U_eiou_colname, .prime),
  r_eiou_prime_colname = paste0(r_eiou_colname, .prime),
  V_prime_colname = paste0(V_colname, .prime),
  Y_prime_colname = paste0(Y_colname, .prime),
  S_units_prime_colname = paste0(S_units_colname, .prime)
)

chop_R(
  .sut_data = NULL,
  calc_pfd_aggs = TRUE,
  p_industries = NULL,
  fd_sectors = NULL,
  piece = "all",
  notation = RCLabels::notations_list,
  pattern_type = c("exact", "leading", "trailing", "anywhere", "literal"),
  prepositions = RCLabels::prepositions_list,
  unnest = FALSE,
  method = c("solve", "QR", "SVD"),
  tol_invert = .Machine$double.eps,
  tol_chop_sum = 1e-04,
  R = Recca::psut_cols$R,
  U = Recca::psut_cols$U,
  U_feed = Recca::psut_cols$U_feed,
  V = Recca::psut_cols$V,
  Y = Recca::psut_cols$Y,
  S_units = Recca::psut_cols$S_units,
  chop_df = Recca::aggregate_cols$chop_df,
  product_sector = Recca::aggregate_cols$product_sector,
  aggregate_primary = Recca::aggregate_cols$aggregate_primary,
  net_aggregate_demand = Recca::aggregate_cols$net_aggregate_demand,
  gross_aggregate_demand = Recca::aggregate_cols$gross_aggregate_demand,
  .prime = "_prime",
  R_colname = Recca::psut_cols$R,
  U_colname = Recca::psut_cols$U,
  U_feed_colname = Recca::psut_cols$U_feed,
  U_eiou_colname = Recca::psut_cols$U_eiou,
  r_eiou_colname = Recca::psut_cols$r_eiou,
  V_colname = Recca::psut_cols$V,
  Y_colname = Recca::psut_cols$Y,
  S_units_colname = Recca::psut_cols$S_units,
  R_prime_colname = paste0(R_colname, .prime),
  U_prime_colname = paste0(U_colname, .prime),
  U_feed_prime_colname = paste0(U_feed_colname, .prime),
  U_eiou_prime_colname = paste0(U_eiou_colname, .prime),
  r_eiou_prime_colname = paste0(r_eiou_colname, .prime),
  V_prime_colname = paste0(V_colname, .prime),
  Y_prime_colname = paste0(Y_colname, .prime),
  S_units_prime_colname = paste0(S_units_colname, .prime)
)

Arguments

.sut_data

A data frame or list of physical supply-use table matrices. Default is NULL.

calc_pfd_aggs

A boolean that tells whether (TRUE) or not (FALSE) to include primary and final demand aggregates to the nested data frame.

p_industries

A vector of names of industries to be aggregated as "primary" and used if aggregations are requested. If .sut_data is a data frame, p_industries should be the name of a column in the data frame. If .sut_data is NULL, p_industries can be a single vector of industry names. These industries in p_industries will appear in rows of the resource (R) and make (V) matrices and columns of the final demand matrix (Y). Entries in Y_p will be subtracted from entries in R_p + V_p to obtain the total primary energy aggregate, where *_p is the primary part of those matrices. The function find_p_industry_names() might be helpful to find primary industry names if they can be identified by prefixes. This argument is passed to primary_aggregates(). Default is NULL.

fd_sectors

A vector of names of sectors in final demand and used if aggregations are requested. Names should include columns in the Y and U_EIOU matrices to cover both net (in Y) and gross (in Y and U_EIOU) final demand. This argument is passed to finaldemand_aggregates(). Default is NULL.

piece, notation, pattern_type, prepositions

Arguments passed to both primary_aggregates() and finaldemand_aggregates() and, ultimately, to matsbyname::select_rowcol_piece_byname() for the purpose of selecting rows and columns for primary and final demand aggregations. See matsbyname::select_rowcol_piece_byname() for details.

unnest

A boolean that tells whether to unnest the outgoing data. When TRUE, creates a new column called product_sector and columns of primary and final demand aggregates. Default is FALSE.

method

One of "solve", "QR", or "SVD". Default is "solve". See details.

tol_invert

The tolerance for detecting linear dependencies in the columns inverted matrices. Default is .Machine$double.eps.

tol_chop_sum

The allowable deviation from 0 for the difference between the sum of the chopped ECCs and the original ECC. Default is 1e-4.

R, U, U_feed, V, Y, S_units

Matrices that describe the energy conversion chain (ECC). See Recca::psut_cols for default values.

chop_df, aggregate_primary, net_aggregate_demand, gross_aggregate_demand

Names of output columns. See Recca::aggregate_cols.

product_sector

The name of the output column that contains the product, industry, or sector for which footprint aggregates are given. Default is Recca::aggregate_cols$product_sector.

.prime

A string that denotes new matrices. This string is used as a suffix that is appended to many variable names. Default is "_prime".

R_colname, U_colname, U_feed_colname, U_eiou_colname, r_eiou_colname, V_colname, Y_colname, S_units_colname

Names of input matrices in .sut_data. See Recca::psut_cols for default values.

R_prime_colname, U_prime_colname, U_feed_prime_colname, U_eiou_prime_colname, r_eiou_prime_colname, V_prime_colname, Y_prime_colname, S_units_prime_colname

Names of output matrices in the return value. Default values are constructed from Recca::psut_cols values suffixed with the value of the .prime argument.

Value

Chopped R and Y energy conversion chains with optional primary and final demand aggregates.

Details

Chopping R involves calculating an ECC for each industry row and each product column in the R matrix. This calculation is accomplished for each description of an energy conversion chain (ECC) by the following algorithm:

  1. Calculate IO matrices with calc_io_mats(). (Do this step prior to calling this function.)

  2. Identify each industry and each product from rows and columns of the R matrix.

  3. For each industry and product independently, perform a downstream swim with new_R_ps() to obtain the ECC induced by that industry or product only.

  4. Optionally (but included by default with calc_pfd_aggs = TRUE), calculate primary and final demand aggregates using primary_aggregates() and finaldemand_aggregates(). Both functions are called with by = "Total", yielding total primary and final demand aggregates.

  5. Add the chopped ECCs to the right side of .sut_data as a nested data frame. If calculated, add the primary and final demand aggregates as columns in the nested data frame.

Chopping Y involves calculating an ECC for each individual product row and sector column of final demand in the Y matrix. This calculation is accomplished for each description of an ECC by the following algorithm:

  1. Calculate io matrices with calc_io_mats(). (Do this step prior to calling this function.)

  2. Identify each product and sector from rows and columns of the Y matrix.

  3. For each product and sector independently, perform an upstream swim with new_Y() to obtain the ECC requirements to supply that product or sector only.

  4. Optionally (but included by default), calculate primary and final demand aggregates using primary_aggregates() and finaldemand_aggregates(). Both functions are called with by = "Total", yielding total primary and final demand aggregates.

  5. Add the chopped ECCs to the right side of .sut_data as a nested data frame. If calculated, add the primary and final demand aggregates as columns in the nested data frame.

Use the unnest argument to define how the aggregate data are added to the right side of .sut_data when .sut_data is a matsindf data frame.

Note that the nested data frame includes columns for the ECC matrices for each isolated product or sector. Optionally, the nested data frame includes primary and final demand aggregates for the chopped ECCs. The names of the columns in the data frame are taken from the *_prime_colname arguments.

chop_R() and chop_Y() involve downstream and upstream swims performed by the new_R_ps() and new_Y() functions. Both involve matrix inverses. The method arguments specify how the matrix inversion is accomplished. The tol argument specifies the tolerance for detecting linearities in the matrix to be inverted. See the documentation at matsbyname::invert_byname() for details.

Both tol and method should be a single values and apply to all rows of .sut_data.

Before chopping and swimming are performed, the original R or Y matrix is used for an downstream or upstream swim (respectively). An error will be emitted if we are unable to reproduce the other ECC matrices (U, U_feed, U_EIOU, V, and Y in the case of a downstream swim when chopping R; R, U, U_feed, U_EIOU, and V in the case of an upstream swim when chopping Y) to within machine precision.

When the R and Y matrices are chopped by rows or columns, the sum of the ECCs created from the chopped rows or columns should equal the original ECC. Internally, these functions check for sum consistency and emits an error if inconsistencies are found.

Examples

p_industries <- c("Resources - Crude", "Resources - NG")
fd_sectors <- c("Residential", "Transport", "Oil fields")
psut_mats <- UKEnergy2000mats %>%
  tidyr::pivot_wider(names_from = matrix.name, values_from = matrix)
psut_mats %>%
  chop_Y(p_industries = p_industries, fd_sectors = fd_sectors)
#> # A tibble: 4 × 13
#>   Country  Year Energy.type Last.stage R             U        U_EIOU   U_feed  
#>   <chr>   <dbl> <chr>       <chr>      <list>        <list>   <list>   <list>  
#> 1 GBR      2000 E           Final      <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 2 GBR      2000 E           Services   <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 3 GBR      2000 E           Useful     <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 4 GBR      2000 X           Services   <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> # ℹ 5 more variables: V <list>, Y <list>, r_EIOU <list>, S_units <list>,
#> #   Chopped.ECCs <list>
psut_mats %>%
  chop_Y(p_industries = p_industries, fd_sectors = fd_sectors, unnest = TRUE)
#> # A tibble: 24 × 24
#>    Country  Year Energy.type Last.stage R             U        U_EIOU   U_feed  
#>    <chr>   <dbl> <chr>       <chr>      <list>        <list>   <list>   <list>  
#>  1 GBR      2000 E           Final      <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#>  2 GBR      2000 E           Final      <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#>  3 GBR      2000 E           Final      <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#>  4 GBR      2000 E           Final      <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#>  5 GBR      2000 E           Final      <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#>  6 GBR      2000 E           Final      <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#>  7 GBR      2000 E           Services   <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#>  8 GBR      2000 E           Services   <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#>  9 GBR      2000 E           Services   <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 10 GBR      2000 E           Services   <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> # ℹ 14 more rows
#> # ℹ 16 more variables: V <list>, Y <list>, r_EIOU <list>, S_units <list>,
#> #   Product.Industry.Sector <chr>, R_prime <named list>, U_prime <named list>,
#> #   U_feed_prime <named list>, U_EIOU_prime <named list>,
#> #   r_EIOU_prime <named list>, V_prime <named list>, Y_prime <named list>,
#> #   S_units_prime <named list>, EX.p <dbl>, EX.fd_gross <dbl>, EX.fd_net <dbl>
psut_mats_2 <- psut_mats %>%
  # Slice to avoid the services rows on which NA values are obtained due to unit homogeneity.
  dplyr::filter(Last.stage != "Services")
# Calculate aggregates
psut_mats_2 %>%
  chop_R(p_industries = p_industries, fd_sectors = fd_sectors)
#> # A tibble: 2 × 13
#>   Country  Year Energy.type Last.stage R             U        U_EIOU   U_feed  
#>   <chr>   <dbl> <chr>       <chr>      <list>        <list>   <list>   <list>  
#> 1 GBR      2000 E           Final      <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 2 GBR      2000 E           Useful     <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> # ℹ 5 more variables: V <list>, Y <list>, r_EIOU <list>, S_units <list>,
#> #   Chopped.ECCs <list>
psut_mats_2 %>%
  chop_R(p_industries = p_industries, fd_sectors = fd_sectors, unnest = TRUE)
#> # A tibble: 8 × 24
#>   Country  Year Energy.type Last.stage R             U        U_EIOU   U_feed  
#>   <chr>   <dbl> <chr>       <chr>      <list>        <list>   <list>   <list>  
#> 1 GBR      2000 E           Final      <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 2 GBR      2000 E           Final      <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 3 GBR      2000 E           Final      <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 4 GBR      2000 E           Final      <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 5 GBR      2000 E           Useful     <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 6 GBR      2000 E           Useful     <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 7 GBR      2000 E           Useful     <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 8 GBR      2000 E           Useful     <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> # ℹ 16 more variables: V <list>, Y <list>, r_EIOU <list>, S_units <list>,
#> #   Product.Industry.Sector <chr>, R_prime <named list>, U_prime <named list>,
#> #   U_feed_prime <named list>, U_EIOU_prime <named list>,
#> #   r_EIOU_prime <named list>, V_prime <named list>, Y_prime <named list>,
#> #   S_units_prime <named list>, EX.p <dbl>, EX.fd_gross <dbl>, EX.fd_net <dbl>