R/get_saved_table.R

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

#' @title Get an input-output table from a downloaded bulk file
#' @importFrom dplyr distinct across left_join rename select any_of
#' @importFrom tidyr pivot_wider unnest
#' @importFrom rlang .data
#' @keywords internal
get_saved_table <- function (labelled_io_data, 
                             geo, 
                             year, 
                             unit,  
                             stk_flow) {
  
  if ( nchar(geo) == 2 & geo == tolower(geo)) { 
    geo <- toupper (geo)
  }
  
  iot <- find_saved_table(labelled_io_data, geo=geo, year=year, unit=unit, stk_flow=stk_flow )
  
  iot %>% unnest(.data$data)
}

#' @rdname get_saved_table
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter
#' @importFrom rlang .data
#' @keywords internal
find_saved_table <- function(labelled_io_data, geo, unit, year, stk_flow) {
  geo_input <- geo
  unit_input <- unit
  year_input <- year
  stk_flow_input <- stk_flow
  
  assert_that( all( c("geo", "unit", "year", "stk_flow", "data") %in% names(labelled_io_data) ), 
               msg = "The columns 'geo', 'year', 'unit', 'stk_flow' columns and the nested 'data' column must be present in labelled_io_data.")
  
  assert_that( geo_input %in% labelled_io_data$geo, 
               msg = glue("The labelled_io_data$geo column does not contain geo='{geo_input}'."))
  
  subset_labelled_io_data <- labelled_io_data %>% filter ( .data$geo == geo_input )
  
  assert_that( unit_input %in% subset_labelled_io_data$unit, 
               msg = glue("The labelled_io_data$unit column does not contain unit='{unit_input}' (for geo='{geo_input}')."))
  
  subset_labelled_io_data  <- subset_labelled_io_data  %>% filter ( .data$unit == unit_input )
  
  assert_that( stk_flow_input %in% subset_labelled_io_data$stk_flow, 
               msg = glue("The labelled_io_data$stk_flow column does not contain {stk_flow_input} (for geo='{geo_input}')."))
  
  subset_labelled_io_data  <- subset_labelled_io_data  %>% filter ( .data$stk_flow == stk_flow_input )
  
  assert_that( year_input %in% subset_labelled_io_data$year, 
               msg = glue("The labelled_io_data$year column does not contain year='{year_input'} (for geo='{geo_input}', unit='{unit_input}', stk_flow='{stk_flow_input}'.)"))
  
  subset_labelled_io_data  %>% filter ( .data$year == year_input )
}

#' @rdname get_saved_table
#' @importFrom lubridate year
#' @importFrom dplyr mutate
#' @keywords internal
get_package_iots <- function( source_input ) {
  
  croatia_tables <- c("croatia_2010_1700", "croatia_2010_1800", "croatia_2010_1900")
  uk_tables <- c("uk_2010_siot", "uk_2010_coeff", "uk_2010_inverse")
  
  ## Read from file or internal dataset ----
  if ( source_input == "germany_1995" ) {
    germany_1995 <- getdata("germany_1995") 
    labelled_io_data <- germany_1995    # use germany example 
    labelled_io_data$year <- 1990
    
  } else if ( source_input == "croatia_2010_1700"  ) { 
    croatia_2010_1700 <- getdata("croatia_2010_1700")
    labelled_io_data <- croatia_2010_1700 %>%
      mutate ( year = lubridate::year(.data$time))
    
  } else if ( source_input == "croatia_2010_1800" )  {
    croatia_2010_1800 <- getdata("croatia_2010_1800")
    labelled_io_data <- croatia_2010_1800 %>%
      mutate ( year = lubridate::year (.data$time))
    
  } else if ( source_input == "croatia_2010_1900" )  {
    croatia_2010_1900 <- getdata("croatia_2010_1900")
    labelled_io_data <- croatia_2010_1900 %>%
      mutate ( year = lubridate::year(.data$time))
    
  } else if ( source_input %in% uk_tables) {
    labelled_io_data <-  getdata("uk_2010_data") %>%
      mutate ( year = 2010 )
    
    if ( source %in% uk_tables ) {
      if ( source == "uk_2010_siot") {
        labelled_io_data <- labelled_io_data %>%
          filter ( .data$indicator == 'Input-Output table (domestic use, basic prices, product by product)')
      }
      
      if ( source == "uk_2010_use") {
        labelled_io_data <- labelled_io_data %>%
          filter ( .data$indicator == 'Domestic use table at basic prices (product by industry)')
      }
      
      if ( source == "uk_2010_imports") {
        labelled_io_data <- labelled_io_data %>%
          filter ( .data$indicator == 'Imports use table at basic prices (product by product)')
      }
      
      if ( source == "uk_2010_coeff") {
        labelled_io_data <- labelled_io_data %>%
          filter ( .data$indicator == 'Matrix of coefficients (product by product)')
      }
      
      if ( source == "uk_2010_inverse") {
        labelled_io_data <- labelled_io_data %>%
          filter ( .data$indicator == 'Leontief Inverse (product by product)')
      }
    }
    }
  labelled_io_data
}