diff --git a/analysis/paper/si.Rmd b/analysis/paper/si.Rmd
index 7539ad3a34ad22d97458007e9b1a7748b368ed0d..002fe2873dc9489b7a886423a4a7108ba3941be0 100644
--- a/analysis/paper/si.Rmd
+++ b/analysis/paper/si.Rmd
@@ -272,7 +272,7 @@ Table S4 shows our mapping between the EUROSTAT HBS consumption categories and t
```{r tableS4}
-labels = read.csv(here("/analysis/preprocessing/Exiobase_T_labels_ixi_w_coicop_mapping_no_rent.csv")) %>%
+labels = read.csv(here("/analysis/preprocessing/Exiobase_T_labels_ixi_w_coicop_mapping.csv")) %>%
select(V2,coicop,five_sectors) %>%
unique()
diff --git a/analysis/preprocessing/Exiobase_T_labels_ixi_w_coicop_mapping_no_rent.csv b/analysis/preprocessing/Exiobase_T_labels_ixi_w_coicop_mapping.csv
similarity index 100%
rename from analysis/preprocessing/Exiobase_T_labels_ixi_w_coicop_mapping_no_rent.csv
rename to analysis/preprocessing/Exiobase_T_labels_ixi_w_coicop_mapping.csv
diff --git a/analysis/preprocessing/full_code.Rmd b/analysis/preprocessing/full_code.Rmd
index c4d7e846416b95279ba56d924919a16f017dd981..16dcc3cc7aa5d49805d86f100081b41f5f0ab28b 100644
--- a/analysis/preprocessing/full_code.Rmd
+++ b/analysis/preprocessing/full_code.Rmd
@@ -3196,9 +3196,9 @@ results_formatted = results_recombined %>%
#write_rds(results_formated, here("/results_formated.rds"))
-write.csv(results_formatted, paste0(data_dir_income_stratified_footprints, "/results_formatted_method1_ixi_pps_hh_no_rent.csv"))
+write.csv(results_formatted, paste0(data_dir_income_stratified_footprints, "/results_formatted_method1_ixi.csv"))
-write_rds(results_formatted, paste0(data_dir_income_stratified_footprints, "/results_formatted_method1_ixi_pps_hh_no_rent.rds"))
+write_rds(results_formatted, paste0(data_dir_income_stratified_footprints, "/results_formatted_method1_ixi.rds"))
#write.csv(results_formatted, paste0(data_dir_income_stratified_footprints, "/results_formatted_method1_ixi_pps_ae.csv"))
@@ -7554,7 +7554,7 @@ write.csv(results_formatted_with_direct_FD_fp, paste0(data_dir_income_stratified
```
-# European exp deciles
+# European expenditure deciles
```{r european-expenditure-deciles, eval = FALSE}
@@ -7574,11 +7574,11 @@ pacman::p_load(tidyverse,
target_eu_ntiles = 10
-source(here("code", "helper_functions.R"))
-
+#source(here("code", "helper_functions.R"))
# 1) load MRIO result file
-dat_results_raw = read_rds(here("data", "results_formatted_method1_ixi_pps_hh_no_rent.rds")) %>%
+dat_results_raw = read_rds(here("analysis", "preprocessing", "income-stratified-footprints",
+ "results_formatted_method1_ixi.rds")) %>%
ungroup() %>%
mutate(year= strtoi(year)) %>%
rename(iso2 = geo)
@@ -7591,7 +7591,8 @@ country_codes = ISOcodes::ISO_3166_1 %>%
mutate(iso2 = if_else(iso2=="GB", "UK", iso2))
# 2) load Eurostat household data - should show how I get to 'total_private_households.csv' - see code chunk in SI - move to here.
-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) %>%
@@ -7601,7 +7602,9 @@ 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") %>%
@@ -7701,8 +7704,8 @@ df_adult_e_p_hh %>%
df_adult_e_p_hh %>%
filter(iso2 %in% complete_countries, year<=2015, year>=2005) %>%
- mutate(quint = parse_number(quintile)) %>%
- write_csv(here("data", "adult_eq_per_household.csv"))
+ mutate(quint = parse_number(quintile)) #%>%
+ #write_csv(here("data", "adult_eq_per_household.csv"))
# calculate EU expenditure tiles based on loaded mrio result file and adult equivalents.
@@ -7886,7 +7889,7 @@ 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, ".csv")))
+ here(paste0("analysis/data/derived/mrio_results_eu_ntile_mapped_n_", target_eu_ntiles, "_check.csv")))
df_mapped_result_ntiles =
df_mapped_result_2005_ntiles %>% mutate(year=2005) %>%
@@ -7896,26 +7899,9 @@ df_mapped_result_ntiles =
write_csv(df_mapped_result_ntiles,
here(paste0("data/eu_ntiles_n_", target_eu_ntiles, ".csv")))
-######
-
-knitr::opts_chunk$set(echo = FALSE, message=FALSE)
-
-if (!require("pacman")) install.packages("pacman")
-pacman::p_load(tidyverse,
- janitor,
- here,
- wbstats,
- ISOcodes,
- viridis,
- imputeTS,
- hrbrthemes,
- wesanderson,
- glue)
-
-target_eu_ntiles = 10
-
-source(here("code", "helper_functions.R"))
+###### pxp version
+# 1) load MRIO result file
dat_results_raw = read_rds(here("data", "results_formatted_method1_pxp_pps_hh_no_rent.rds")) %>%
ungroup() %>%
mutate(year= strtoi(year)) %>%
@@ -8229,25 +8215,7 @@ df_mapped_result_ntiles =
write_csv(df_mapped_result_ntiles,
here(paste0("data/eu_ntiles_n_", target_eu_ntiles, "_pxp.csv")))
-######
-
-knitr::opts_chunk$set(echo = FALSE, message=FALSE)
-
-if (!require("pacman")) install.packages("pacman")
-pacman::p_load(tidyverse,
- janitor,
- here,
- wbstats,
- ISOcodes,
- viridis,
- imputeTS,
- hrbrthemes,
- wesanderson,
- glue)
-
-target_eu_ntiles = 10
-
-source(here("code", "helper_functions.R"))
+###### 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")) %>%