ratios_na_microdata

library(tidyverse)

national_accounts_df <-  tibble::tibble(country = c("ITA", "ITA", "ITA", "ITA", "ITA"), 
                                        variable = c("NFD41R", "NFD11P", "NFB3GR", "NFD42R", "NFD45R"), 
                                        sector = c("S14", "S14", "S14", "S14", "S14"), 
                                        year = c("2016", "2016", "2016", "2016", "2016"), 
                                        value = c(27593.2, 84325810, 227852.9, 119509.5, 1034.7))

estimates_from_microdata_df <- tibble::tibble(indicator = c("D11P", "B3GR+D41R+D42R+D45R"), 
                                              value = c(84328052, 894699.3))


compute_national_account_ratios <- function(estimates_from_microdata_df, national_accounts_table){
  
  national_accounts_table <- national_accounts_df

  national_accounts_table$variable <- stringr::str_remove(national_accounts_df$variable, "^NF" )
  
  national_accounts_table_wide <- national_accounts_table %>%
    pivot_wider(id_cols = "country", names_from = "variable", values_from = "value")
  
  national_accounts_estimates <- purrr::map_dbl(estimates_from_microdata_df[["indicator"]], 
             .f = function(formula_str, df){
               
               formula_expr <- parse(text = formula_str)
               
               # Evaluate the formula expression within the context of the data.frame
               new_column <- eval(formula_expr, envir = df)

               return(new_column)
               
               },
             national_accounts_table_wide)
  
  return(estimates_from_microdata_df[["value"]]/national_accounts_estimates)
  
}

compute_national_account_ratios(estimates_from_microdata_df, national_accounts_df)