Download the employment data for a country and arrange it to the 64x64 SIOTS. Currently works only with product x product tables.

supplementary_add(data_table, supplementary_data,
  supplementary_names = NULL)

Arguments

data_table

A SIOT, a use table, a supply table, or a margins table.

supplementary_data

Supplementary data to be added. It must be a data.frame or tibble with a key column containing the indicator's name, and the column names must match with the data_table. Can be a vector or a data frame of several rows.

supplementary_names

Optional names for the new supplementary rows. Defaults to NULL.

Examples

de_io <- iotable_get() CO2 <- c( 0.2379, 0.5172, 0.0456, 0.1320, 0.0127, 0.0530) names ( CO2) <- c("agriculture_group", "industry_group","construction", "trade_group","business_services_group","other_services_group") CO2 <- cbind ( data.frame ( iotables_row = "CO2"),as.data.frame ( t(CO2))) de_coeff <- input_coefficient_matrix_create ( iotable_get() )
#> Warning: Parameter return_part='products' was not recognized, returned all data.
supplementary_add ( de_io, CO2)
#> iotables_row agriculture_group industry_group construction #> 1 agriculture_group 1131.0000 25480.0000 1.0000 #> 2 industry_group 7930.0000 304584.0000 64167.0000 #> 3 construction 426.0000 7334.0000 3875.0000 #> 4 trade_group 3559.0000 72717.0000 14190.0000 #> 5 business_services_group 3637.0000 96115.0000 31027.0000 #> 6 other_services_group 1552.0000 14986.0000 1747.0000 #> 7 total 18235.0000 521216.0000 115007.0000 #> 8 imports 2927.0000 156703.0000 13427.0000 #> 9 intermediate_consumption 22246.0000 684424.0000 129982.0000 #> 10 compensation_employees 9382.0000 296464.0000 78819.0000 #> 11 net_tax_production -2012.0000 1457.0000 963.0000 #> 12 consumption_fixed_capital 7871.0000 63769.0000 5860.0000 #> 13 os_mixed_income_net 6423.0000 33332.0000 29982.0000 #> 14 gva 21664.0000 395022.0000 115624.0000 #> 15 output 43910.0000 1079446.0000 245606.0000 #> 16 net_tax_products 1084.0000 6505.0000 1548.0000 #> 17 employment_wage_salary 483.0000 8032.0000 2896.0000 #> 18 employment_self_employed 613.0000 349.0000 340.0000 #> 19 employment_domestic_total 1096.0000 8381.0000 3236.0000 #> 20 CO2 0.2379 0.5172 0.0456 #> trade_group business_services_group other_services_group total #> 1 607.000 710.0000 762.000 28691 #> 2 41082.000 11981.0000 30360.000 460104 #> 3 5296.000 23457.0000 9155.000 49543 #> 4 74399.000 10835.0000 21008.000 196708 #> 5 65755.000 193176.0000 34223.000 423933 #> 6 11225.000 15058.0000 22070.000 66638 #> 7 198364.000 255217.0000 117578.000 1225617 #> 8 21943.000 13371.0000 13772.000 222143 #> 9 228656.000 277061.0000 143901.000 1486270 #> 10 214450.000 124810.0000 272975.000 996900 #> 11 2748.000 5946.0000 -8602.000 500 #> 12 41100.000 98610.0000 49260.000 266470 #> 13 53109.000 186060.0000 51384.000 360290 #> 14 311407.000 415426.0000 365017.000 1624160 #> 15 540063.000 692487.0000 508918.000 3110430 #> 16 8349.000 8473.0000 12551.000 38510 #> 17 7977.000 3653.0000 9555.000 32596 #> 18 1274.000 605.0000 651.000 3832 #> 19 9251.000 4258.0000 10206.000 36428 #> 20 0.132 0.0127 0.053 NA #> final_consumption_households final_consumption_government inventory_change #> 1 8500 16 -6 #> 2 197792 8588 7559 #> 3 3457 742 0 #> 4 269663 13492 0 #> 5 214757 10061 0 #> 6 119504 317251 0 #> 7 813673 350150 7553 #> 8 80187 2970 -4233 #> 9 1001060 356790 3580 #> 10 NA NA NA #> 11 NA NA NA #> 12 NA NA NA #> 13 NA NA NA #> 14 NA NA NA #> 15 1001060 356790 3580 #> 16 107200 3670 260 #> 17 NA NA NA #> 18 NA NA NA #> 19 NA NA NA #> 20 0 NA NA #> gross_capital_formation exports total_final_use #> 1 2975 3734 43910 #> 2 91692 313711 1079400 #> 3 191715 149 245606 #> 4 14155 46045 540063 #> 5 30124 13612 692487 #> 6 3483 2042 508918 #> 7 334144 379293 3110384 #> 8 41436 42597 385100 #> 9 404240 420730 3672624 #> 10 NA NA 996900 #> 11 NA NA 500 #> 12 NA NA 266470 #> 13 NA NA 360290 #> 14 NA NA 1624160 #> 15 404240 420730 NA #> 16 28660 -1160 177140 #> 17 NA NA 32596 #> 18 NA NA 3832 #> 19 NA NA 36428 #> 20 NA NA NA