edit full code

analysis/preprocessing/full_code.Rmd

@@ -7902,7 +7902,8 @@ write_csv(df_mapped_result_ntiles,
 ###### pxp version
 
 # 1) load MRIO result file
-dat_results_raw = read_rds(here("data", "results_formatted_method1_pxp_pps_hh_no_rent.rds")) %>%
+dat_results_raw = read_rds(here("analysis", "preprocessing", "income-stratified-footprints",
+                            "results_formatted_method1_pxp_pps_hh_no_rent.rds")) %>%
   ungroup() %>%
   mutate(year= strtoi(year)) %>%
   rename(iso2 = geo)
@@ -7915,7 +7916,8 @@ country_codes = ISOcodes::ISO_3166_1 %>%
   mutate(iso2 = if_else(iso2=="GB", "UK", iso2))
 
 # 2) load Eurostat household data
-hh_data = read_csv(here("data", "total_private_households.csv")) %>%
+hh_data = read_csv(here("analysis", "preprocessing", "income-stratified-footprints",
+                        "total_private_households.csv")) %>%
   mutate(imputed = if_else(is.na(total_private_households), TRUE, FALSE)) %>%
   rename(iso2 = geo) %>%
   group_by(iso2) %>%
@@ -7925,7 +7927,7 @@ hh_data = read_csv(here("data", "total_private_households.csv")) %>%
   select(-total_private_households)
 
 #3) Eurostat mean expenditures per household income quintile per household and per adult equivalent
-df_expenditure_long = read_csv(here("data",
+df_expenditure_long = read_csv(here("analysis", "preprocessing", "income-stratified-footprints",
                                     "mean_expenditure_by_quintile_long.csv"),
                                na = ":") %>%
   #filter(year >=2010, geo != "IT") %>%
@@ -8205,7 +8207,7 @@ df_mapped_result_data = df_mapped_result_2005_data %>%
   #bind_rows(df_mapped_result_2015_data)
 
 write_csv(df_mapped_result_data, 
-          here(paste0("data/mrio_results_eu_ntile_mapped_n_", target_eu_ntiles, "_pxp.csv")))
+          here(paste0("analysyis/data/derived/si/mrio_results_eu_ntile_mapped_n_", target_eu_ntiles, "_pxp_check.csv")))
 
 df_mapped_result_ntiles = 
   df_mapped_result_2005_ntiles %>% mutate(year=2005) %>%
@@ -8218,7 +8220,8 @@ write_csv(df_mapped_result_ntiles,
 ###### alternative method, ixi version
 
 # 1) load MRIO result file
-dat_results_raw = read_rds(here("data", "results_formatted_method2_ixi_pps_hh_no_rent.rds")) %>%
+dat_results_raw = read_rds(here("analysis", "preprocessing", "income-stratified-footprints",
+                                "results_formatted_method2_ixi_pps_hh_no_rent.rds")) %>%
   ungroup() %>%
   mutate(year= strtoi(year)) %>%
   rename(iso2 = geo)
@@ -8231,7 +8234,8 @@ country_codes = ISOcodes::ISO_3166_1 %>%
   mutate(iso2 = if_else(iso2=="GB", "UK", iso2))
 
 # 2) load Eurostat household data
-hh_data = read_csv(here("data", "total_private_households.csv")) %>%
+hh_data = read_csv(here("analysis", "preprocessing", "income-stratified-footprints",
+                        "total_private_households.csv")) %>%
   mutate(imputed = if_else(is.na(total_private_households), TRUE, FALSE)) %>%
   rename(iso2 = geo) %>%
   group_by(iso2) %>%
@@ -8241,7 +8245,7 @@ hh_data = read_csv(here("data", "total_private_households.csv")) %>%
   select(-total_private_households)
 
 #3) Eurostat mean expenditures per household income quintile per household and per adult equivalent
-df_expenditure_long = read_csv(here("data",
+df_expenditure_long = read_csv(here("analysis", "preprocessing", "income-stratified-footprints",
                                     "mean_expenditure_by_quintile_long.csv"),
                                na = ":") %>%
   #filter(year >=2010, geo != "IT") %>%
@@ -8521,7 +8525,7 @@ df_mapped_result_data = df_mapped_result_2005_data %>%
   bind_rows(df_mapped_result_2015_data)
 
 write_csv(df_mapped_result_data, 
-          here(paste0("data/mrio_results_eu_ntile_mapped_n_", target_eu_ntiles, "_method2_ixi.csv")))
+          here(paste0("analysis/data/derived/si/mrio_results_eu_ntile_mapped_n_", target_eu_ntiles, "_method2_ixi_check.csv")))
 
 df_mapped_result_ntiles = 
   df_mapped_result_2005_ntiles %>% mutate(year=2005) %>%