From 2fbff3f5c5f04f8d900f5271325441b9579f8a79 Mon Sep 17 00:00:00 2001
From: jaccard <jaccard@pik-potsdam.de>
Date: Tue, 15 Dec 2020 19:17:18 -0800
Subject: [PATCH] edit full code
---
analysis/preprocessing/full_code.Rmd | 20 ++++++++++++--------
1 file changed, 12 insertions(+), 8 deletions(-)
diff --git a/analysis/preprocessing/full_code.Rmd b/analysis/preprocessing/full_code.Rmd
index 16dcc3c..3ffae21 100644
--- a/analysis/preprocessing/full_code.Rmd
+++ b/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) %>%
--
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