edit fig.5

analysis/paper/paper.Rmd

@@ -964,24 +964,10 @@ for (min_energy in seq(from=mer[1], to=mer[2], by=0.25)) {
       bind_rows(df_energy_deciles %>%
                   scaled_quantiles(eu_q_rank, pae_energy_use_gj, min_energy, mean_energy))
     }
-    
   }
 }
 
-# thought it was much faster without the bind_rows, turns out that is not the bottleneck
-#n_quantiles = nrow(df_energy_deciles)
-#mea_range = seq(from=mea[1], to=mea[2], by=1)
-#mer_range = seq(from=mer[1], to=mer[2], by=1)
-#df_all2 = data.frame(eu_q_rank = rep(seq(1:n_quantiles),
-#                                     times=length(mer_range)*length(mea_range)),
-#                     v_mean = rep(mea_range, each=n_quantiles, times=length(mer_range)),
-#                     v_first = rep(mer_range, each=length(mea_range)*n_quantiles),
-#                     scaled = 0) %>%
-#  filter(v_first <= v_mean)
-
-
-
-#saveRDS(df_all, here("data/scenarios_fine.rds"))
+saveRDS(df_all, here("analysis/data/derived/scenarios_fine_new.rds"))
 ```
 
 ```{r , fig.width=7, fig.height=5.5, eval = FALSE}
@@ -1017,14 +1003,14 @@ a = df_all %>%
   scale_fill_gradient("Mean energy\navailable (GJ/ae)",
                       low=wes_palette("Chevalier1")[3], 
                       high = wes_palette("Rushmore1")[4]) +
-  #scale_color_manual(values=wes_palette("Darjeeling1")) +
+  scale_color_manual(values=wes_palette("Darjeeling1")) +
   theme_minimal() +
   labs(x="Minimum energy requirement (GJ/ae)", y="Maximum energy inequality (10:10 ratio)", color = "Scenario")+
   theme(text=element_text(family="Liberation Sans Narrow"),
         axis.text.x = element_text(size = 13),
         axis.text.y = element_text(size = 13))# +
   #theme_ipsum()
-
+a
 ggsave(here("analysis", "figures", "figure5-test.pdf"))
 ```