diff --git a/analysis/preprocessing/full_code.Rmd b/analysis/preprocessing/full_code.Rmd
index 188a9a6d1cb88a0bb1112679713c08afed9dd68e..866aa9a4901484377f7c3b767a77c05e51ed857f 100644
--- a/analysis/preprocessing/full_code.Rmd
+++ b/analysis/preprocessing/full_code.Rmd
@@ -7502,8 +7502,11 @@ country_codes = ISOcodes::ISO_3166_1 %>%
mutate(iso2 = if_else(iso2=="GR", "EL", iso2)) %>%
mutate(iso2 = if_else(iso2=="GB", "UK", iso2))
+# read in total private households data from EUROSTAT and Norway, merge, and write .csv
+## set 4 digits per value
options(digits=4)
+## EUROSTAT total private households
total_private_households_Eurostat = read.csv(here("/analysis/preprocessing/income-stratified-footprints/lfst_hhnhtych_1_Data.csv")) %>%
filter(!(GEO %in% c("European Union - 27 countries (from 2020)",
"Euro area - 19 countries (from 2015)",
@@ -7549,6 +7552,7 @@ total_private_households_Eurostat = read.csv(here("/analysis/preprocessing/incom
total_private_households = as.numeric(total_private_households),
total_private_households = total_private_households*1000)
+## Norway total private households
total_private_households_Norway = read.csv(here("/analysis/preprocessing/income-stratified-footprints/Privathusholdninger.csv")) %>%
gather(year, total_private_households, Private.households.2005:Private.households.2019) %>%
mutate(geo = dplyr::recode(region,
@@ -7571,20 +7575,23 @@ total_private_households_Norway = read.csv(here("/analysis/preprocessing/income-
"Private.households.2019" = 2019)) %>%
select(year,geo,total_private_households)
+## merge EUROSTAT and Norway total private households data
total_private_households = rbind(total_private_households_Eurostat,
total_private_households_Norway) %>%
mutate(geo = as.character(geo),
year = as.numeric(year),
total_private_households = as.numeric(total_private_households))
+## write .csv file with all total private households data
write_csv(total_private_households, here("/analysis/preprocessing/income-stratified-footprints/total_private_households.csv"))
# the original 'total_private_households_original.csv' is in 'income-stratified-footprints-in-europe/data' - if something goes wrong
# with the main paper results (pulling from the mrio results file written later) then the problem is here, when I added
# the as.character mutation in the total_private_households_Eurostat
+## return to 2 digits per value
options(digits=2)
-# 2) load Eurostat household data - should show how I get to 'total_private_households.csv' - see code chunk in SI - move to here.
+# 2) load merged total private households data
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)) %>%
@@ -7595,7 +7602,7 @@ hh_data = read_csv(here("analysis", "preprocessing", "income-stratified-footprin
left_join(country_codes, by="iso2") %>%
select(-total_private_households)
-#3) Eurostat mean expenditures per household income quintile per household and per adult equivalent
+#3) load EUROSTAT mean expenditures per household income quintile per household and per adult equivalent (written and saved in the previous, income-stratified-footprints code chunk)
df_expenditure_long = read_csv(here("analysis",
"preprocessing",
"income-stratified-footprints",
@@ -7611,15 +7618,6 @@ df_expenditure_long = read_csv(here("analysis",
select(-mean_expenditure) %>%
ungroup()
-
-# df_expenditure_2005 = df_expenditure_long %>%
-# filter(year == 2010) %>%
-# mutate(year = 2005,
-# imputed = TRUE)
-#
-# df_expenditure_long = df_expenditure_long %>%
-# bind_rows(df_expenditure_2005)
-
## Calculate adult equivalents per household
df_adult_e_p_hh = df_expenditure_long %>%
rename(iso2 = geo) %>%
@@ -7633,7 +7631,7 @@ df_adult_e_p_hh = df_expenditure_long %>%
quint = parse_number(quintile))
-# add quintile population data
+## add quintile population data
mrio_results_with_adult_eq_all = dat_results_raw %>%
filter(year %in% c(2005, 2010, 2015)) %>%
left_join(hh_data, by=c("iso2", "year")) %>%
@@ -7647,8 +7645,7 @@ mrio_results_with_adult_eq_all = dat_results_raw %>%
select(-c(hh_quintile, adult_e_p_hh))
-#### ONLY COUNTRIES THAT HAVE DATA FOR 2005, 2010, and 2015
-## TODO: maybe make a bit less dirty =)
+## for the european expenditure deciles we use only countries with data in 2005, 2010 and 2015. This excludes Luxembourg and Italy.
complete_countries = mrio_results_with_adult_eq_all %>%
group_by(year, iso2) %>%
summarise(co2_kg = sum(co2_kg)) %>%
@@ -7660,47 +7657,9 @@ complete_countries = mrio_results_with_adult_eq_all %>%
select(iso2) %>%
pull()
-
-hh_data %>%
- filter(iso2 %in% complete_countries, year<=2015, year>=2005) %>%
-ggplot(aes(x=year, y=hh*0.000001)) +
- geom_line() +
- geom_point(data=hh_data %>%
- filter(iso2 %in% complete_countries,
- year<=2015,
- year>=2005,
- imputed), color="red") +
- theme_ipsum() +
- scale_x_continuous(labels = scales::label_number(accuracy = 1, big.mark = "")) +
- labs(x="", y="Total number of households (mio)") +
- facet_wrap(~iso3, scales="free_y", ncol = 4) +
- theme(legend.position = "bottom",
- axis.text.x = element_text(angle = 90))
-
-#ggsave(here("figures", "household_size.png"), plot = p, width = 8, height = 14)
-
-pal <- wes_palette("Cavalcanti1", 5, type = "discrete")
-pal = pal[c(1,2,5)]
-
-df_adult_e_p_hh %>%
- filter(iso2 %in% complete_countries, year<=2015, year>=2005) %>%
- mutate(quint = parse_number(quintile)) %>%
- ggplot(aes(x=quint, y=adult_e_p_hh, color=factor(year))) +
- geom_line(alpha=0.5) +
- theme_ipsum() +
- scale_color_manual(name = "Year", values = pal) +
- labs(x="", y="Adult equivivalents per household") +
- facet_wrap(~iso3, ncol = 4) +
- theme(legend.position = "bottom",
- axis.text.x = element_text(angle = 90))
-
-#ggsave(here("figures", "adult_eq_per_household.png"), plot = p, width = 8, height = 14)
-
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))
# calculate EU expenditure tiles based on loaded mrio result file and adult equivalents.
# returns country quintiles mapped to EU ntile rank and EU ntile boundaries
@@ -7885,15 +7844,7 @@ df_mapped_result_data = df_mapped_result_2005_data %>%
write_csv(df_mapped_result_data,
here(paste0("analysis/data/derived/mrio_results_eu_ntile_mapped_n_", target_eu_ntiles, ".csv")))
-#df_mapped_result_ntiles =
-# df_mapped_result_2005_ntiles %>% mutate(year=2005) %>%
-# bind_rows(df_mapped_result_2010_ntiles %>% mutate(year=2010)) %>%
-# bind_rows(df_mapped_result_2015_ntiles %>% mutate(year=2015))
-
-#write_csv(df_mapped_result_ntiles,
-# here(paste0("data/eu_ntiles_n_", target_eu_ntiles, ".csv")))
-
-###### pxp version
+###### using EXIOBASE product-by-product version
# 1) load MRIO result file
dat_results_raw = read_rds(here("analysis", "preprocessing", "income-stratified-footprints",
@@ -7909,6 +7860,95 @@ country_codes = ISOcodes::ISO_3166_1 %>%
mutate(iso2 = if_else(iso2=="GR", "EL", iso2)) %>%
mutate(iso2 = if_else(iso2=="GB", "UK", iso2))
+# read in total private households data from EUROSTAT and Norway, merge, and write .csv
+## set 4 digits per value
+options(digits=4)
+
+## EUROSTAT total private households
+total_private_households_Eurostat = read.csv(here("/analysis/preprocessing/income-stratified-footprints/lfst_hhnhtych_1_Data.csv")) %>%
+ filter(!(GEO %in% c("European Union - 27 countries (from 2020)",
+ "Euro area - 19 countries (from 2015)",
+ "European Union - 28 countries (2013-2020)",
+ "European Union - 15 countries (1995-2004)"))) %>%
+ mutate(geo = dplyr::recode(GEO,
+ "Belgium" = "BE",
+ "Bulgaria" = "BG",
+ "Czechia" = "CZ",
+ "Denmark" = "DK",
+ "Germany (until 1990 former territory of the FRG)" = "DE",
+ "Estonia" = "EE",
+ "Ireland" = "IE",
+ "Greece" = "EL",
+ "Spain" = "ES",
+ "France" = "FR",
+ "Croatia" = "HR",
+ "Italy" = "IT",
+ "Cyprus" = "CY",
+ "Latvia" = "LV",
+ "Lithuania" = "LT",
+ "Luxembourg" = "LU",
+ "Hungary" = "HU",
+ "Malta" = "MT",
+ "Netherlands" = "NL",
+ "Austria" = "AT",
+ "Poland" = "PL",
+ "Portugal" = "PT",
+ "Romania" = "RO",
+ "Slovenia" = "SI",
+ "Slovakia" = "SK",
+ "Finland" = "FI",
+ "Sweden" = "SE",
+ "United Kingdom" = "UK",
+ "Montenegro" = "ME",
+ "North Macedonia" = "MK",
+ "Serbia" = "RS",
+ "Turkey" = "TR")) %>%
+ select(TIME,geo,Value) %>%
+ rename(year = TIME, total_private_households = Value) %>%
+ mutate(total_private_households = as.character(total_private_households),
+ total_private_households = parse_number(total_private_households),
+ total_private_households = as.numeric(total_private_households),
+ total_private_households = total_private_households*1000)
+
+## Norway total private households
+total_private_households_Norway = read.csv(here("/analysis/preprocessing/income-stratified-footprints/Privathusholdninger.csv")) %>%
+ gather(year, total_private_households, Private.households.2005:Private.households.2019) %>%
+ mutate(geo = dplyr::recode(region,
+ "0 The whole country" = "NO"),
+ year = dplyr::recode(year,
+ "Private.households.2005" = 2005,
+ "Private.households.2006" = 2006,
+ "Private.households.2007" = 2007,
+ "Private.households.2008" = 2008,
+ "Private.households.2009" = 2009,
+ "Private.households.2010" = 2010,
+ "Private.households.2011" = 2011,
+ "Private.households.2012" = 2012,
+ "Private.households.2013" = 2013,
+ "Private.households.2014" = 2014,
+ "Private.households.2015" = 2015,
+ "Private.households.2016" = 2016,
+ "Private.households.2017" = 2017,
+ "Private.households.2018" = 2018,
+ "Private.households.2019" = 2019)) %>%
+ select(year,geo,total_private_households)
+
+## merge EUROSTAT and Norway total private households data
+total_private_households = rbind(total_private_households_Eurostat,
+ total_private_households_Norway) %>%
+ mutate(geo = as.character(geo),
+ year = as.numeric(year),
+ total_private_households = as.numeric(total_private_households))
+
+## write .csv file with all total private households data
+write_csv(total_private_households, here("/analysis/preprocessing/income-stratified-footprints/total_private_households.csv"))
+# the original 'total_private_households_original.csv' is in 'income-stratified-footprints-in-europe/data' - if something goes wrong
+# with the main paper results (pulling from the mrio results file written later) then the problem is here, when I added
+# the as.character mutation in the total_private_households_Eurostat
+
+## return to 2 digits per value
+options(digits=2)
+
# 2) load Eurostat household data
hh_data = read_csv(here("analysis", "preprocessing", "income-stratified-footprints",
"total_private_households.csv")) %>%
@@ -7934,14 +7974,6 @@ df_expenditure_long = read_csv(here("analysis", "preprocessing", "income-stratif
select(-mean_expenditure) %>%
ungroup()
-# df_expenditure_2005 = df_expenditure_long %>%
-# filter(year == 2010) %>%
-# mutate(year = 2005,
-# imputed = TRUE)
-#
-# df_expenditure_long = df_expenditure_long %>%
-# bind_rows(df_expenditure_2005)
-
## Calculate adult equivalents per household
df_adult_e_p_hh = df_expenditure_long %>%
rename(iso2 = geo) %>%
@@ -7954,7 +7986,6 @@ df_adult_e_p_hh = df_expenditure_long %>%
mutate(iso3 = if_else(iso2 == "XK", "XKX", iso3),
quint = parse_number(quintile))
-
# add quintile population data
mrio_results_with_adult_eq_all = dat_results_raw %>%
filter(year %in% c(2005, 2010, 2015)) %>%
@@ -7970,7 +8001,6 @@ mrio_results_with_adult_eq_all = dat_results_raw %>%
#### ONLY COUNTRIES THAT HAVE DATA FOR 2005, 2010, and 2015
-## TODO: maybe make a bit less dirty =)
complete_countries = mrio_results_with_adult_eq_all %>%
group_by(year, iso2) %>%
summarise(co2_kg = sum(co2_kg)) %>%
@@ -7982,45 +8012,9 @@ complete_countries = mrio_results_with_adult_eq_all %>%
select(iso2) %>%
pull()
-hh_data %>%
- filter(iso2 %in% complete_countries, year<=2015, year>=2005) %>%
-ggplot(aes(x=year, y=hh*0.000001)) +
- geom_line() +
- geom_point(data=hh_data %>%
- filter(iso2 %in% complete_countries,
- year<=2015,
- year>=2005,
- imputed), color="red") +
- theme_ipsum() +
- scale_x_continuous(labels = scales::label_number(accuracy = 1, big.mark = "")) +
- labs(x="", y="Total number of households (mio)") +
- facet_wrap(~iso3, scales="free_y", ncol = 4) +
- theme(legend.position = "bottom",
- axis.text.x = element_text(angle = 90))
-
-#ggsave(here("figures", "household_size.png"), plot = p, width = 8, height = 14)
-
-pal <- wes_palette("Cavalcanti1", 5, type = "discrete")
-pal = pal[c(1,2,5)]
-
df_adult_e_p_hh %>%
filter(iso2 %in% complete_countries, year<=2015, year>=2005) %>%
- mutate(quint = parse_number(quintile)) %>%
- ggplot(aes(x=quint, y=adult_e_p_hh, color=factor(year))) +
- geom_line(alpha=0.5) +
- theme_ipsum() +
- scale_color_manual(name = "Year", values = pal) +
- labs(x="", y="Adult equivivalents per household") +
- facet_wrap(~iso3, ncol = 4) +
- theme(legend.position = "bottom",
- axis.text.x = element_text(angle = 90))
-
-#ggsave(here("figures", "adult_eq_per_household.png"), plot = p, width = 8, height = 14)
-
-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))
# calculate EU expenditure tiles based on loaded mrio result file and adult equivalents.
# returns country quintiles mapped to EU ntile rank and EU ntile boundaries
@@ -8192,26 +8186,13 @@ df_mapped_result_2010 = map_mrio_results_to_eu_ntiles(2010, target_eu_ntiles)
df_mapped_result_2010_data = df_mapped_result_2010$df_mapped_data
df_mapped_result_2010_ntiles = df_mapped_result_2010$df_ntile_boundaries
-#df_mapped_result_2015 = map_mrio_results_to_eu_ntiles(2015, target_eu_ntiles)
-#df_mapped_result_2015_data = df_mapped_result_2015$df_mapped_data
-#df_mapped_result_2015_ntiles = df_mapped_result_2015$df_ntile_boundaries
-
df_mapped_result_data = df_mapped_result_2005_data %>%
- bind_rows(df_mapped_result_2010_data) #%>%
- #bind_rows(df_mapped_result_2015_data)
+ bind_rows(df_mapped_result_2010_data)
write_csv(df_mapped_result_data,
here(paste0("analysis/data/derived/si/mrio_results_eu_ntile_mapped_n_", target_eu_ntiles, "_pxp.csv")))
-#df_mapped_result_ntiles =
-# df_mapped_result_2005_ntiles %>% mutate(year=2005) %>%
-# bind_rows(df_mapped_result_2010_ntiles %>% mutate(year=2010)) #%>%
- #bind_rows(df_mapped_result_2015_ntiles %>% mutate(year=2015))
-
-#write_csv(df_mapped_result_ntiles,
-# here(paste0("data/eu_ntiles_n_", target_eu_ntiles, "_pxp.csv")))
-
-###### alternative method, ixi version
+###### alternative method, EXOIBASE industry-by-industry version
# 1) load MRIO result file
dat_results_raw = read_rds(here("analysis", "preprocessing", "income-stratified-footprints",
@@ -8227,6 +8208,95 @@ country_codes = ISOcodes::ISO_3166_1 %>%
mutate(iso2 = if_else(iso2=="GR", "EL", iso2)) %>%
mutate(iso2 = if_else(iso2=="GB", "UK", iso2))
+# read in total private households data from EUROSTAT and Norway, merge, and write .csv
+## set 4 digits per value
+options(digits=4)
+
+## EUROSTAT total private households
+total_private_households_Eurostat = read.csv(here("/analysis/preprocessing/income-stratified-footprints/lfst_hhnhtych_1_Data.csv")) %>%
+ filter(!(GEO %in% c("European Union - 27 countries (from 2020)",
+ "Euro area - 19 countries (from 2015)",
+ "European Union - 28 countries (2013-2020)",
+ "European Union - 15 countries (1995-2004)"))) %>%
+ mutate(geo = dplyr::recode(GEO,
+ "Belgium" = "BE",
+ "Bulgaria" = "BG",
+ "Czechia" = "CZ",
+ "Denmark" = "DK",
+ "Germany (until 1990 former territory of the FRG)" = "DE",
+ "Estonia" = "EE",
+ "Ireland" = "IE",
+ "Greece" = "EL",
+ "Spain" = "ES",
+ "France" = "FR",
+ "Croatia" = "HR",
+ "Italy" = "IT",
+ "Cyprus" = "CY",
+ "Latvia" = "LV",
+ "Lithuania" = "LT",
+ "Luxembourg" = "LU",
+ "Hungary" = "HU",
+ "Malta" = "MT",
+ "Netherlands" = "NL",
+ "Austria" = "AT",
+ "Poland" = "PL",
+ "Portugal" = "PT",
+ "Romania" = "RO",
+ "Slovenia" = "SI",
+ "Slovakia" = "SK",
+ "Finland" = "FI",
+ "Sweden" = "SE",
+ "United Kingdom" = "UK",
+ "Montenegro" = "ME",
+ "North Macedonia" = "MK",
+ "Serbia" = "RS",
+ "Turkey" = "TR")) %>%
+ select(TIME,geo,Value) %>%
+ rename(year = TIME, total_private_households = Value) %>%
+ mutate(total_private_households = as.character(total_private_households),
+ total_private_households = parse_number(total_private_households),
+ total_private_households = as.numeric(total_private_households),
+ total_private_households = total_private_households*1000)
+
+## Norway total private households
+total_private_households_Norway = read.csv(here("/analysis/preprocessing/income-stratified-footprints/Privathusholdninger.csv")) %>%
+ gather(year, total_private_households, Private.households.2005:Private.households.2019) %>%
+ mutate(geo = dplyr::recode(region,
+ "0 The whole country" = "NO"),
+ year = dplyr::recode(year,
+ "Private.households.2005" = 2005,
+ "Private.households.2006" = 2006,
+ "Private.households.2007" = 2007,
+ "Private.households.2008" = 2008,
+ "Private.households.2009" = 2009,
+ "Private.households.2010" = 2010,
+ "Private.households.2011" = 2011,
+ "Private.households.2012" = 2012,
+ "Private.households.2013" = 2013,
+ "Private.households.2014" = 2014,
+ "Private.households.2015" = 2015,
+ "Private.households.2016" = 2016,
+ "Private.households.2017" = 2017,
+ "Private.households.2018" = 2018,
+ "Private.households.2019" = 2019)) %>%
+ select(year,geo,total_private_households)
+
+## merge EUROSTAT and Norway total private households data
+total_private_households = rbind(total_private_households_Eurostat,
+ total_private_households_Norway) %>%
+ mutate(geo = as.character(geo),
+ year = as.numeric(year),
+ total_private_households = as.numeric(total_private_households))
+
+## write .csv file with all total private households data
+write_csv(total_private_households, here("/analysis/preprocessing/income-stratified-footprints/total_private_households.csv"))
+# the original 'total_private_households_original.csv' is in 'income-stratified-footprints-in-europe/data' - if something goes wrong
+# with the main paper results (pulling from the mrio results file written later) then the problem is here, when I added
+# the as.character mutation in the total_private_households_Eurostat
+
+## return to 2 digits per value
+options(digits=2)
+
# 2) load Eurostat household data
hh_data = read_csv(here("analysis", "preprocessing", "income-stratified-footprints",
"total_private_households.csv")) %>%
@@ -8252,14 +8322,6 @@ df_expenditure_long = read_csv(here("analysis", "preprocessing", "income-stratif
select(-mean_expenditure) %>%
ungroup()
-# df_expenditure_2005 = df_expenditure_long %>%
-# filter(year == 2010) %>%
-# mutate(year = 2005,
-# imputed = TRUE)
-#
-# df_expenditure_long = df_expenditure_long %>%
-# bind_rows(df_expenditure_2005)
-
## Calculate adult equivalents per household
df_adult_e_p_hh = df_expenditure_long %>%
rename(iso2 = geo) %>%
@@ -8272,7 +8334,6 @@ df_adult_e_p_hh = df_expenditure_long %>%
mutate(iso3 = if_else(iso2 == "XK", "XKX", iso3),
quint = parse_number(quintile))
-
# add quintile population data
mrio_results_with_adult_eq_all = dat_results_raw %>%
filter(year %in% c(2005, 2010, 2015)) %>%
@@ -8286,9 +8347,7 @@ mrio_results_with_adult_eq_all = dat_results_raw %>%
mutate(ae_quintile = hh_quintile * adult_e_p_hh) %>%
select(-c(hh_quintile, adult_e_p_hh))
-
#### ONLY COUNTRIES THAT HAVE DATA FOR 2005, 2010, and 2015
-## TODO: maybe make a bit less dirty =)
complete_countries = mrio_results_with_adult_eq_all %>%
group_by(year, iso2) %>%
summarise(co2_kg = sum(co2_kg)) %>%
@@ -8300,45 +8359,9 @@ complete_countries = mrio_results_with_adult_eq_all %>%
select(iso2) %>%
pull()
-hh_data %>%
- filter(iso2 %in% complete_countries, year<=2015, year>=2005) %>%
-ggplot(aes(x=year, y=hh*0.000001)) +
- geom_line() +
- geom_point(data=hh_data %>%
- filter(iso2 %in% complete_countries,
- year<=2015,
- year>=2005,
- imputed), color="red") +
- theme_ipsum() +
- scale_x_continuous(labels = scales::label_number(accuracy = 1, big.mark = "")) +
- labs(x="", y="Total number of households (mio)") +
- facet_wrap(~iso3, scales="free_y", ncol = 4) +
- theme(legend.position = "bottom",
- axis.text.x = element_text(angle = 90))
-
-#ggsave(here("figures", "household_size.png"), plot = p, width = 8, height = 14)
-
-pal <- wes_palette("Cavalcanti1", 5, type = "discrete")
-pal = pal[c(1,2,5)]
-
-df_adult_e_p_hh %>%
- filter(iso2 %in% complete_countries, year<=2015, year>=2005) %>%
- mutate(quint = parse_number(quintile)) %>%
- ggplot(aes(x=quint, y=adult_e_p_hh, color=factor(year))) +
- geom_line(alpha=0.5) +
- theme_ipsum() +
- scale_color_manual(name = "Year", values = pal) +
- labs(x="", y="Adult equivivalents per household") +
- facet_wrap(~iso3, ncol = 4) +
- theme(legend.position = "bottom",
- axis.text.x = element_text(angle = 90))
-
-#ggsave(here("figures", "adult_eq_per_household.png"), plot = p, width = 8, height = 14)
-
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))
# calculate EU expenditure tiles based on loaded mrio result file and adult equivalents.
# returns country quintiles mapped to EU ntile rank and EU ntile boundaries
@@ -8521,14 +8544,6 @@ df_mapped_result_data = df_mapped_result_2005_data %>%
write_csv(df_mapped_result_data,
here(paste0("analysis/data/derived/si/mrio_results_eu_ntile_mapped_n_", target_eu_ntiles, "_method2_ixi.csv")))
-#df_mapped_result_ntiles =
-# df_mapped_result_2005_ntiles %>% mutate(year=2005) %>%
-# bind_rows(df_mapped_result_2010_ntiles %>% mutate(year=2010)) %>%
-# bind_rows(df_mapped_result_2015_ntiles %>% mutate(year=2015))
-
-#write_csv(df_mapped_result_ntiles,
-# here(paste0("data/eu_ntiles_n_", target_eu_ntiles, "_method2_ixi.csv")))
-
```
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