data-raw/no_longer_used/old_table_selection_function.R

In rOpenGov/iotables: Reproducible Input-Output Economics Analysis, Economic and Environmental Impact Assessment with Empirical Data

old_table_selection_function <- function(labelled_io_data, year_input, geo_input, unit_input, stk_flow_input) {
  ## This internal function used to be a part of iotable_get() and it is no longer in use.
  ## It is replaced by get_saved_table().
  
  selected_table <- which (   ## get the number of table to be selected
    labelled_io_data$year == year_input & 
      as.character(labelled_io_data$geo) == geo_input &
      labelled_io_data$unit == unit_input )
  
  if ( length( selected_table) == 0  )  {
    stop ( paste0("There is no available table for country ", geo_input, 
                  " in the year ", year_input, 
                  " with ", unit_input, " units.") )
  } else if (length( selected_table) == 3) { 
    selected_table <- which (   ##get the number of table to be selected
      labelled_io_data$year == year_input & 
        as.character(labelled_io_data$geo) == geo_input &
        labelled_io_data$unit == unit_input  &
        labelled_io_data$stk_flow == stk_flow_input
    )
  }  
  
  if (length(selected_table) != 1) {
    stop ( "The parameters geo=", geo, "; unit=",  unit_input, 
           "; stk_flow=", stk_flow_input, 
           "\ndo not select a unique table.")
  }
  
  iotable <- labelled_io_data$data[[selected_table]]
}


## old_table_selection_function in data-raw/old_table_selection_function.R

#' @keywords internal
not_in_use <- function() {
  tmp_iot <- iot %>%
    unnest(iot, cols = "data") %>%
    select ( any_of(c( "prod_na", "induse", "values")))
  
  ## How to find out if it has prod_na?
  
  labelling_information <- tmp_iot %>% 
    dplyr::distinct( across ( any_of(c("trows", "prod_na", "induse")))) %>%
    rename ( code = .data$prod_na ) %>%
    left_join ( metadata, by = 'code' ) %>%
    arrange ( .data$numeric_label )
  
  if ( labelling != "iotables") {
    left_join (
      labelling_information %>% 
        select ( prod_na = .data$code ) %>%
        distinct( .data$prod_na), 
      tmp_iot %>%
        pivot_wider ( names_from ="induse", values_from = "values"),
      by = "prod_na"
    )
  } else {
    
    stop ("this is not yet working")
    
    induse_labels <-  labelling_information %>% 
      select ( induse = .data$code, .data$iotables_label ) %>%
      distinct( across(all_of(c("induse", "iotables_label")))) %>%
      semi_join ( tmp_iot %>% select ( any_of("induse")) )
    
    prod_na_labels <-    labelling_information %>% 
      select ( prod_na = .data$code, .data$iotables_label ) %>%
      distinct( across(all_of(c("prod_na", "iotables_label")))) %>%
      semi_join ( tmp_iot %>% select (any_of("prod_na")), by ="prod_na")
    
    dpulicates <- prod_na_labels %>%
      left_join ( tmp_iot, by  = "prod_na") %>%
      add_count( .data$prod_na, .data$induse) %>%
      filter ( n > 1)
    
    rename ( iotables_row = .data$iotables_label ) %>%
      select (-.data$prod_na) %>%
      left_join ( induse_labels ) %>%
      select (-.data$induse ) %>%
      pivot_wider( names_from = "iotables_label", values_from = "values")
  } 
}