edit si

analysis/paper/.~lock.si_test.docx#

-,jaccard,jaccard-Latitude-E6440,28.01.2021 18:37,file:///home/jaccard/.config/libreoffice/4;
+,jaccard,jaccard-Latitude-E6440,28.01.2021 18:52,file:///home/jaccard/.config/libreoffice/4;
\ No newline at end of file

analysis/paper/si_test.Rmd

@@ -173,7 +173,8 @@ hbs = data.frame(quintile, pps_hh, pm_sector1, pm_sector2) %>%
   mutate(pps_hh_sector1 = pps_hh*(pm_sector1/1000),
          sector_1_shares = pps_hh_sector1/sum(pps_hh_sector1),
          pps_hh_sector2 = pps_hh*(pm_sector2/1000),
-         sector_2_shares = pps_hh_sector2/sum(pps_hh_sector2))
+         sector_2_shares = pps_hh_sector2/sum(pps_hh_sector2)) %>%
+  mutate_if(is.numeric, round, digits = 2)
 
 #knitr::kable(hbs, caption = "Table S1: HBS structure with calculations of quintile shares per sector.", 
 #             escape = F, 
@@ -225,7 +226,8 @@ eemrio = data.frame(q_share_of_sector, eemrio_hh_fd) %>%
          q2_eemrio = q2*eemrio_hh_fd,
          q3_eemrio = q3*eemrio_hh_fd,
          q4_eemrio = q4*eemrio_hh_fd,
-         q5_eemrio = q5*eemrio_hh_fd)
+         q5_eemrio = q5*eemrio_hh_fd) %>%
+  mutate_if(is.numeric, round, digits = 2)
 
 #knitr::kable(eemrio, caption = "Table S2: HBS income quintile shares per sector 
 #             multiplied by EE-MRIO household final demand expenditure vector to 
@@ -288,7 +290,8 @@ footprint = data.frame(eemrio,TIV) %>%
          q3_footprint = q3_eemrio*TIV,
          q4_footprint = q4_eemrio*TIV,
          q5_footprint = q5_eemrio*TIV,
-         total_footprint = eemrio_hh_fd*TIV)
+         total_footprint = eemrio_hh_fd*TIV) %>%
+  mutate_if(is.numeric, round, digits = 2)
 
 #knitr::kable(footprint, caption = "Table S3: Calculation of EE-MRIO household 
 #             footprint decomposed by income quintile, through multiplication of 
@@ -516,47 +519,11 @@ year_2005 = c("x",
               "x",
               "x",
               "")
-  
-year_1999 = c("x",
-              "x",
-              "",
-              "",
-              "",
-              "x",
-              "x",
-              "",
-              "x",
-              "x",
-              "x",
-              "x",
-              "",
-              "",
-              "x",
-              "x",
-              "",
-              "x",
-              "",
-              "",
-              "",
-              "",
-              "x",
-              "",
-              "",
-              "x",
-              "",
-              "",
-              "x",
-              "",
-              "",
-              "",
-              "x",
-              "")
 
 country_year_coverage = data.frame(geo,
                                    year_2015,
                                    year_2010,
-                                   year_2005,
-                                   year_1999)
+                                   year_2005)
 
 #knitr::kable(country_year_coverage, caption = "Table S5: Country and year 
 #             coverage between EXIOBASE and the EUROSTAT HBS. Rows with black 
@@ -579,8 +546,7 @@ flextable(country_year_coverage) %>%
   set_header_labels(geo = "geo",
                     year_2015 = "2015",
                     year_2010 = "2010",
-                    year_2005 = "2005",
-                    year_1999 = "1999") %>%
+                    year_2005 = "2005") %>%
   set_caption("Country and year 
              coverage between EXIOBASE and the EUROSTAT HBS. Rows with black 
              text show countries that are represented in EXIOBASE, and an 'x' 
@@ -588,7 +554,7 @@ flextable(country_year_coverage) %>%
              Rows with red text show countries where EUROSTAT HBS data exists, 
              but who are not represented individually in EXIOBASE (they are in 
              'rest-of-world' categories)") %>%
-  fit_to_width(max_width = 7.5)
+  fit_to_width(max_width = 7.5) 
 
 ```
 
@@ -639,7 +605,8 @@ hbs_bp_pps = hbs_bp %>%
          pps_hh_sector1_bp,
          pps_hh_sector2,
          bp_share_in_pp_sector_2,
-         pps_hh_sector2_bp)
+         pps_hh_sector2_bp) %>%
+  mutate_if(is.numeric, round, digits = 2)
 
 #knitr::kable(hbs_bp_pps, caption = "Table S6: Same as Table S1 but now 
 #             with base price shares of purchaser price per sector, and 
@@ -689,7 +656,8 @@ hbs_bp_shares = hbs_bp %>%
          pps_hh_sector2_bp,
          sector_2_shares_bp) %>%
   mutate(pm_sector1_bp = (pps_hh_sector1_bp/(pps_hh_sector1_bp + pps_hh_sector2_bp))*1000,
-         pm_sector2_bp = (pps_hh_sector2_bp/(pps_hh_sector1_bp + pps_hh_sector2_bp))*1000)
+         pm_sector2_bp = (pps_hh_sector2_bp/(pps_hh_sector1_bp + pps_hh_sector2_bp))*1000) %>%
+  mutate_if(is.numeric, round, digits = 2)
 
 #knitr::kable(hbs_bp_shares, caption = "Table S7: Quintile shares per sector 
 #             in base price, with new 'pps hh' per quintile in base price and 
@@ -745,7 +713,8 @@ eemrio_bp = data.frame(q_share_of_sector_bp, eemrio_hh_fd) %>%
          q2_eemrio = q2*eemrio_hh_fd,
          q3_eemrio = q3*eemrio_hh_fd,
          q4_eemrio = q4*eemrio_hh_fd,
-         q5_eemrio = q5*eemrio_hh_fd)
+         q5_eemrio = q5*eemrio_hh_fd) %>%
+  mutate_if(is.numeric, round, digits = 2)
 
 #knitr::kable(eemrio_bp, caption = "Table S8: Identical to Table S2.",
 #             booktabs = TRUE,
@@ -833,7 +802,8 @@ hbs_bp_pps = hbs_bp %>%
          pps_hh_sector1_bp,
          pps_hh_sector2,
          bp_share_in_pp_sector_2,
-         pps_hh_sector2_bp)
+         pps_hh_sector2_bp) %>%
+  mutate_if(is.numeric, round, digits = 2)
 
 #knitr::kable(hbs_bp_pps, caption = "Table S9: Same as Table S1 but now 
 #             with base price shares of purchaser price per sector, and 
@@ -879,7 +849,8 @@ hbs_bp_shares = hbs_bp %>%
          pps_hh_sector1_bp,
          pps_hh_sector2_bp) %>%
   mutate(pm_sector1_bp = (pps_hh_sector1_bp/(pps_hh_sector1_bp + pps_hh_sector2_bp))*1000,
-         pm_sector2_bp = (pps_hh_sector2_bp/(pps_hh_sector1_bp + pps_hh_sector2_bp))*1000)
+         pm_sector2_bp = (pps_hh_sector2_bp/(pps_hh_sector1_bp + pps_hh_sector2_bp))*1000) %>%
+  mutate_if(is.numeric, round, digits = 2)
 
 #knitr::kable(hbs_bp_shares, caption = "Table S10: Income quintile shares per sector 
 #             in base price, with new 'pps hh' per income quintile in base price and 
@@ -942,7 +913,8 @@ hbs_alt_method_fd = hbs_alt_method %>%
          mean_expenditure_share,
          eemrio_hh_fd,
          hh_sector1,
-         hh_sector2)
+         hh_sector2) %>%
+  mutate_if(is.numeric, round, digits = 2)
 
 #knitr::kable(hbs_alt_method_fd, caption = "Table S11: Same as Table S9 but 
 #             with total EE-MRIO household final demand in purchaser price 
@@ -986,7 +958,8 @@ eemrio_alt_method = hbs_alt_method %>%
   spread(quintile,value) %>%
   mutate(sector = dplyr::recode(sector,
                                 "hh_sector1" = "1",
-                                "hh_sector2" = "2"))
+                                "hh_sector2" = "2")) %>%
+  mutate_if(is.numeric, round, digits = 2)
 
 #knitr::kable(eemrio_alt_method, caption = "Table S12: EE-MRIO 
 #             household final demand per quintile and sector.", 
@@ -1027,7 +1000,8 @@ footprint_alt_method = data.frame(eemrio_alt_method, TIV) %>%
            q2_footprint +
            q3_footprint +
            q4_footprint +
-           q5_footprint)
+           q5_footprint) %>%
+  mutate_if(is.numeric, round, digits = 2)
 
 #knitr::kable(footprint_alt_method, caption = "Table S13: EE-MRIO household 
 #             final demand per quintile and sector multiplied by the TIV to