visualization functions for comparing the deviation of our extracted area data...

visualization functions for comparing the deviation of our extracted area data with FAOSTAT and EUROSTAT is fiished and works (look for ggarrange function in script)

Grassland/Grassland.R

@@ -372,8 +372,8 @@ Fun_ggplot_area_deviation = function(dat,lut_vec,author_vec,vec_source){
 }
 
 # Plotting - leandro extraction
-Fun_ggplot_area_deviation(arable_area_cifos, lut_vec="arable", author_vec = "WJS", vec_source = "EUROSTAT")
-Fun_ggplot_area_deviation(grass_area_cifos,lut_vec="grass", author_vec = "WJS" , vec_source = "EUROSTAT")
+gg_leandro_eurostat_arable=Fun_ggplot_area_deviation(arable_area_cifos, lut_vec="arable", author_vec = "Leandro", vec_source = "EUROSTAT")
+gg_leandro_eurostat_grass=Fun_ggplot_area_deviation(grass_area_cifos,lut_vec="grass", author_vec = "Leandro" , vec_source = "EUROSTAT")
 
 # Comparing the own extraction from R script 'Zones_AEZ.R' -----------------
 Fun_area_join_WJSextract = function(dat,vec_lut){
@@ -403,29 +403,44 @@ arable_area_cifos_WJex =  Fun_area_join_WJSextract(dat = permanent_area %>% bind
                                               vec_lut = c("arable"))
 
 # Plotting - WJS extraction
-Fun_ggplot_area_deviation(grass_area_cifos_WJex, lut_vec="arable", author_vec = "WJS",vec_source = "EUROSTAT")
-Fun_ggplot_area_deviation(arable_area_cifos_WJex,lut_vec="grass", author_vec = "WJS", vec_source = "EUROSTAT")
+gg_WJS_eurostat_arable=Fun_ggplot_area_deviation(grass_area_cifos_WJex, lut_vec="arable", author_vec = "WJS",vec_source = "EUROSTAT")
+gg_WJS_eurostat_grass=Fun_ggplot_area_deviation(arable_area_cifos_WJex,lut_vec="grass", author_vec = "WJS", vec_source = "EUROSTAT")
 
 
 # comparing with FAO area ---------------------------------------------------------------------
+EU_cntr_Cifos = c("Austria","Belgium","Bulgaria","Croatia","Cyprus", "Czechia","Denmark",
+                  "Estonia","Finland","France","Germany","Greece","Hungary","Ireland",
+                  "Italy","Latvia","Lithuania","Luxembourg","Malta","Netherlands","Poland",
+                  "Portugal","Romania","Slovakia","Slovenia","Spain","Sweden",'United Kingdom')
+
 fao_arable_area =
   read_csv("INput_data/area_harvested_FAO_arablecrops.csv") %>% 
-  clean_names() %>% type.convert()  %>% 
-  left_join(country_map, by = c("area_code_fao" = "ADM0_CODE_GAUL")) #%>% 
-  filter(., iso3_SPAM  %in% EU_iso3) %>% 
-  rename("country" = "name_cntr_CIFOS2021",
+  clean_names() %>% type.convert()  %>%
+  mutate(area = recode(area, "United Kingdom of Great Britain and Northern Ireland"= "United Kingdom")) %>% 
+  filter(., area  %in% EU_cntr_Cifos) %>% 
+  rename("country" = "area",
          "area_ha"="value") %>%
   group_by(year_code, country) %>%
   summarise(area_ha = mean(area_ha,na.rm=T)) %>% 
+  ungroup() %>% 
   group_by(country) %>%
   summarise(area_ha = sum(area_ha,na.rm=T)) 
 
-grass_area_cifos_WJex_fao = Fun_area_join_WJSextract(dat = fao_arable_area, vec_lut = c("arable"))
+# Applying the function with FAO arable area data to the extracted datasets of Leandro and Wolfram 
+arable_area_cifos_WJex_fao = Fun_area_join_WJSextract(dat = fao_arable_area, vec_lut = c("arable"))
+arable_area_cifos_fao =  Fun_area_join(dat = fao_arable_area, vec_lut = c("arable"))
+
+# Plotting the two extraction types from Leandro and Wolfram 
+gg_WJS_fao_arable = Fun_ggplot_area_deviation(arable_area_cifos_WJex_fao, lut_vec="arable", author_vec = "WJS",vec_source = "FAOSTAT - 10 year average")
+gg_leandro_fao_arable = Fun_ggplot_area_deviation(arable_area_cifos_fao, lut_vec="arable", author_vec = "Leandro",vec_source = "FAOSTAT - 10 year average")
 
+# Conclusion: Leandros extraction is way to low (average around -60% of the FAOSTAT reference)
+# Arable land
+ggarrange(gg_leandro_eurostat_arable, gg_leandro_fao_arable,
+          gg_WJS_eurostat_arable, gg_WJS_fao_arable)
 
-Comb_Zon_EU28 %>%
-  left_join(fao_arable_area, by=c("country_cifos"="country")) %>% 
-  mutate(diff_perc = 100-(100/area_ha*area_ha_cifos))
+# Grass area 
+ggarrange(gg_leandro_eurostat_grass, gg_WJS_eurostat_grass, nrow = 2)
 
 # EU28 - Subsetting -------------------------------------------------------
 EU_iso3 = c("AUT","BEL", "BGR", "HRV", "CZE", "DNK", "EST","FIN", "FRA", "DEU", "GRC", "HUN", "IRL", "ITA","LVA",