si.Rmd

  group_by(v_mean, v_first) %>%
  summarise(ratio = last(scaled)/first(scaled)) %>%
  mutate(bin_ratio = if_else((ratio*100)%%round_by > round_by*0.5, 
                             ratio*100+(round_by-(ratio*100)%%round_by),
                             ratio*100-(ratio*100)%%round_by)) %>%
  group_by(bin_ratio, v_first) %>%
  summarise(v_mean = mean(v_mean)) %>%
  mutate(bin_ratio = bin_ratio*0.01)

df_scenario = df_all %>%
  filter(v_mean %in% df_scenario_info$fe_gj_pc) %>%
  left_join(df_scenario_info, by=c("v_mean"="fe_gj_pc")) %>%
  filter(!(scenario == "DLE"))

library(wesanderson)

a = df_all %>%
  ggplot(aes(x=v_first, y=bin_ratio, fill=v_mean)) +
  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)-5.7,y=ineq_curr+0.6,label = "Current (2015) 10:10 ratio") +
  #scale_fill_viridis("Mean energy\navailable") +
  scale_fill_gradient("Mean energy\navailable (GJ/cap)",
                      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/cap)", 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)) +
  xlim(0,50) +
  scale_y_continuous(breaks = c(2.5,5,7.5,10,12.5)) # +
  #theme_ipsum()
a
ggsave(here("analysis", "figures", "figureSxx.pdf"))
```

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# References 

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