R/employment_get.R

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

#' @title Get employment data
#'
#' @description Download the employment data for a country and arrange it to the 
#' 64x64 SIOTs.
#' 
#' @details Currently works only with product x product tables. 
#' @param geo The country code. 
#' @param year The year.  The average employment will be created for the given
#' year, starting with \code{2008}, when the NACE Rev 2 was introduced in 
#' employment statistics.
#' @param sex Defaults to \code{"Total"}. Enter \code{"Females"} or \code{"F"} for
#' female employment, \code{"Males"} or \code{"M"} for male employment.
#' @param age Defaults to \code{"Y_GE15"}, which is the Eurostat code for employment in 
#' all age groups starting from 15-years-old. Any Eurostat code can be used as a 
#' parameter. 
#' @param labelling Either \code{"iotables"} or the applicable short code, 
#' for product x product SIOTs \code{"prod_na"} and in the case of industry x 
#' industry SIOTs \code{"induse"}.
#' @param data_directory Defaults to \code{NULL}, if a valid directory, it will try to save the pre-processed 
#' data file here with labelling. 
#' @param force_download Defaults to \code{FALSE}. It will use the existing downloaded file
#' in the \code{data_directory} or the temporary directory, if it exists.
#' @importFrom dplyr filter select mutate left_join rename ungroup summarize group_by
#' @importFrom tidyr spread
#' @importFrom eurostat get_eurostat
#' @importFrom rlang .data
#' @source Eurostat statistic 
#' \href{https://ec.europa.eu/eurostat/web/products-datasets/-/lfsq_egan22d}{Employment 
#' by sex, age and detailed economic activity (from 2008 onwards, NACE Rev. 2 two digit level) - 1 000}
#' @return A data.frame with auxiliary metadata to conform the symmetric
#' input-output tables.
#' @family import functions
#' @examples
#' \dontrun{
#'  io_tables <- get_employment ( 
#'                geo = "CZ", 
#'                year = "2010",
#'                sex = "Total", 
#'                age = "Y_GE15",
#'                data_directory = NULL,
#'                force_download = TRUE
#'                )
#'  }
#' @export

employment_get <- function ( geo, 
                             year = "2010",
                             sex = "Total", 
                             age = "Y_GE15",
                             labelling = 'iotables', 
                             data_directory = NULL,
                             force_download = FALSE) {

  if ( ! labelling %in% c("iotables", 'prod_na', 'induse')) {
    stop("Labelling must be any of 'iotables', 'prod_na' [product x product] or 'induse' [industry x industry]")
  }
  
  ## Avoiding no visible binding for global variable 'data' ----------
  getdata <- function(...)
  {
    e <- new.env()
    name <- utils::data(..., envir = e)[1]
    e[[name]]
  }
  
  employment_metadata <- getdata("employment_metadata")
  
  save_employment_file <- paste0('employment_', tolower (age), '_',
                                 tolower(sex), '_', 
                                 geo, '_', year, '_avg.rds')
  
  ### Changing to Eurostat in case of GB/UK and GR/EL-------
  if ( geo %in% c("GB", "GR")) {
    if (geo == "GB") {
      warning ( "Switching GB to Eurostat abbreviation UK.")
      geo <- "UK"
    }
    if (geo == "GR") {
      warning ( "Switching GR to Eurostat abbreviation EL.")
      geo <- "EL"
    }
  }
  
  sex <- tolower(sex) 
  sex <- ifelse ( grepl("total", sex), "T", 
                        ifelse ( grepl("female", sex), "F", "M"))
  
  emp <- NULL
  
  ## Use data_directory if it exists--------------------------------  
  if ( !is.null(data_directory) )  {
    #pre-existing raw data file in the data directory
    emp_file_name <- file.path(data_directory, "lfsq_egan22d.rds") 
    
    if ( ! force_download ) {  #no new download and filtered version exists 
        if ( file.exists(file.path(data_directory, save_employment_file)) ) {
          message("Try to use pre-existing file ", save_employment_file )
          
          tryCatch({
            emp <- readRDS(file.path(data_directory, save_employment_file))
            }, error = function(cond) {
              message ( "Could not read file.", cond)
              return(NULL)
            }, finally ={ 
              return(emp)
              })
      
        } else {  #no filtered version exists, work with raw file
          
          tryCatch({
            #Read pre-existing unfiltered raw data file 
            emp <- readRDS(emp_file_name)
          }, error = function(cond) {
            message ( 'Could not read file ', emp_file_name, '\n', cond)
            return(NULL)
          }, finally ={ 
            message ( 'Read ', emp_file_name )
          })
        }
    }  #end case of no forced download
  }  #end case data_directory is not NULL
 
  ## Forced/new download--------------------------------  
  if (is.null(emp)) {
    
    message ( "Downloading employment data from the Eurostat database.")
    emp <- eurostat::get_eurostat ("lfsq_egan22d")
    
    if ( !is.null(data_directory) ) {
      #if !is.null emp_file_name is the general file name (without filtering
      # for the statistic and was created in the previous block including the 
      # directory name)

      tryCatch({
        #Read pre-existing unfiltered raw data file 
        saveRDS(emp, file = emp_file_name)
      }, error = function(cond) {
        message ( "Failed to save ", emp_file_name, '.')
      }, finally = { 
        message ( 'Saving raw employment data to ', emp_file_name )
      })
    }
  }
 
 ## Geo selection and exception handling--------------------------------  
 if ( geo %in% unique (emp$geo) ) {
    emp <- emp %>% dplyr::filter ( .data$geo == geo )
  } else {
    stop ("No employment data found with geo parameter = ", geo )
  }
  
  emp$year <- as.numeric(substr(as.character(emp$time), start = 1, stop = 4))
  
  ## Year selection and exception handling -------------------------------------  
  
  if ( year %in% unique ( emp$year ) ) {
    emp <- emp %>% filter ( .data$year == year )
  } else {
    stop ("No employment data found with the year parameter = ", year )
  }
  
  ## Age group selection and exception handling ---------------------------------  
  if ( age %in% unique (emp$age) ) {
    emp <- emp %>% filter (.data$age == age)
  } else {
    stop ("No employment data found with the age parameter = ", age )
  }
  
  ## Sex variable selection and exception handling-------------------------------- 
  if ( sex %in% unique (emp$sex) ) {
    emp <- emp %>% filter (.data$sex == sex)
  } else {
    stop ("No employment data found with sex parameter = ", sex )
  }
  
  ## Missing values changed to 0 ------------------------------------------------- 
  emp$values <- ifelse ( is.na(emp$values), 0, emp$values ) 
  
  ## Data processing for employment variables ------------------------------------ 
  employment <- emp %>%
    mutate (   nace_r2 = as.character(.data$nace_r2) ) %>%
    group_by ( .data$nace_r2, .data$year ) %>%
    summarize ( values = mean(.data$values)) %>%
    dplyr::rename ( emp_code = .data$nace_r2 ) %>%
    ungroup () %>%
    left_join ( employment_metadata, 
                       by = "emp_code") %>%  # iotables:::employment_metadata
    dplyr::group_by (  .data$code, .data$variable, .data$iotables_label ) %>%
    dplyr::summarize ( values = sum(.data$values)) 
  
  
  ## If data_directory exists, save results ------------------------------- 
  
  if ( ! is.null(data_directory) ) {
    message ( "Saving ", save_employment_file )
    saveRDS(employment, file = file.path(data_directory, 
                                         save_employment_file)
            )
  }
 
  
  ## If data_directory exists, save results-------------------------------- 
  
  emp_sex <- ifelse ( tolower(sex) == "t", "total", 
                      ifelse (tolower(sex) == "f", "female", "male" ))
  

  if ( labelling == "iotables" ) { # this is the Eurostat manual-tutorial type labelling format 
    prefix <- data.frame ( 
      iotables_row = paste0("employment_", emp_sex )
    )
    
    primary_employment_input <- employment %>%
      filter ( .data$variable == "prod_na" ) #does not matter which, not used
    
    ##No employment for imputed rent column-------------------------------- 
    
    imputed_rent <- data.frame ( 
      real_estate_imputed_a = 0
    )
    primary_employment_input <-  primary_employment_input %>% 
      dplyr::ungroup() %>%
      select ( .data$iotables_label, .data$values ) %>%
      tidyr::spread ( .data$iotables_label, .data$values )    #use iotables_label in this case
    
  } else if ( labelling == "prod_na" ){  ## this is the product x product labelling format 
    prefix <- data.frame ( 
      prod_na = paste0("employment_", emp_sex )
    )
    
    primary_employment_input <- employment %>%
      dplyr::filter ( .data$variable == "prod_na" )
    
    imputed_rent <- data.frame ( 
      CPA_L68A = 0
    )
    primary_employment_input <-  primary_employment_input %>% 
      dplyr::ungroup() %>%
      dplyr::select ( .data$code, .data$values ) %>%
      tidyr::spread ( .data$code, .data$values )     #use code for standard Eurostat library
    
  } else if (labelling == "induse" ) {  # this is the industry x industry labelling format
    prefix <- data.frame ( 
      induse = paste0("employment_", emp_sex )
    )
    
    primary_employment_input <- employment %>%
      dplyr::filter ( .data$variable == "induse" )
    
    imputed_rent <- data.frame ( 
      L68A = 0
    )
    primary_employment_input <-  primary_employment_input %>% 
      dplyr::ungroup() %>%
      dplyr::select ( .data$code, .data$values ) %>%
      tidyr::spread ( .data$code, .data$values )      #use code for standard Eurostat library
    
  } else {
    warning("No L68A was added.")
    return ( primary_employment_input )
  }
  
  return_employment <- cbind( prefix, primary_employment_input )
  return_employment <- cbind ( return_employment, imputed_rent )
  
  return_employment
}