edit figures

analysis/data/raw/.~lock.scenarios.xlsx#

-,jaccard,jaccard-Latitude-E6440,12.01.2021 12:05,file:///home/jaccard/.config/libreoffice/4;
\ No newline at end of file

analysis/paper/paper.Rmd

@@ -816,7 +816,7 @@ c_mean_mer = round((mer[1]+mer[2])/2)
 
 ```
 
-```{r}
+```{r , fig.width=7, fig.height=5.5}
 
 round_by = 10
 
@@ -842,16 +842,18 @@ a = df_all %>%
   geom_tile() +
   geom_hline(yintercept = ineq_curr, alpha=0.8, color="grey", linetype=2) +
   geom_line(data=df_scenario, aes(color=scenario, group=scenario)) +
-  annotate(geom="text", x=max(df_all$v_first)-5,y=ineq_curr+0.6,label = "current 10:10 ratio") +
+  annotate(geom="text", x=max(df_all$v_first)-5.7,y=ineq_curr+0.6,label = "current 10:10 ratio") +
   #scale_fill_viridis("Mean energy\navailable") +
   scale_fill_gradient("Mean energy\navailable",
                       low=wes_palette("Chevalier1")[3], 
                       high = wes_palette("Rushmore1")[4]) +
   scale_color_manual(values=wes_palette("Darjeeling1")) +
+  theme_minimal() +
   labs(x="Minimum energy requirement (GJ/aeu)", y="max inequality (10:10 ratio)")+
-  theme_ipsum()
+  theme(text=element_text(family="Liberation Sans Narrow"))# +
+  #theme_ipsum()
 
-#ggsave(here("figures", "figure5.pdf"))
+#ggsave(here("analysis", "figures", "figure5-test.pdf"))
 ```
 
 Based on this counterfactual distribution of the energy footprint using homogeneous supply technologies, we can now scale down energy consumption across European expenditure deciles to meet supply constraints and, where necessary, "squeeze" the distribution to not undershoot minimum demand constraints in any decile. This means that, based on the current empirical distribution, for each value combination of supply and minimum necessary demand, the maximum permissible inequality can be calculated as a 10:10 ratio (Figure \@ref(fig:figure5). [*Ref to formula*]