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Ingram Jaccard authoredIngram Jaccard authored
title: "figures"
output:
bookdown::word_document2:
fig_caption: yesknitr::opts_chunk$set(
collapse = TRUE,
warning = FALSE,
message = FALSE,
echo = FALSE,
comment = "#>",
fig.path = "../figures/",
dpi = 300
)
if (!require("pacman")) install.packages("pacman")
pacman::p_load(tidyverse,
janitor,
here,
wbstats,
ISOcodes,
viridis,
hrbrthemes,
wesanderson,
glue,
ggridges,
patchwork,
kableExtra,
readxl,
flextable)
pal <- wes_palette("Cavalcanti1", 5, type = "discrete")
extrafont::loadfonts()
library(here)# load data wrangling functions
source(here("analysis", "R", "wrangler_functions.R"))
## load result data for EU deciles
eu_q_count = 10
# summary countries aggregated by country quintiles and eu ntile
dat_country_summary_by_cquint_and_euntile = get_country_summary_by_cquint_and_euntile(eu_q_count)
# pivot to long format for plotting and attach readable indicator names
cols_ex = c("year", "iso2", "quint", "eu_q_rank")
pdat_country_summary_by_cquint_and_euntile =
pivot_results_longer_adorn(dat_country_summary_by_cquint_and_euntile, cols_ex)
# summary of countries by EU quantile without sectoral resolution
dat_country_summary_by_eu_ntile = get_country_summary_by_eu_ntile(eu_q_count)
# pivot to long format for plotting and attach readable indicator names
cols_ex = c("year", "iso2", "eu_q_rank")
pdat_country_summary_by_eu_ntile =
pivot_results_longer_adorn(dat_country_summary_by_eu_ntile, cols_ex)
# summary of countries by country quintile with aggregate sectoral resolution
dat_sector_summary_by_country_quintile = get_sector_summary_by_country_quintile(eu_q_count)
# pivot to long format for plotting and attach readable indicator names
cols_ex = c("year", "iso2", "quint", "eu_q_rank", "sector_agg_id")
pdat_sector_summary_by_country_quintile =
pivot_results_longer_adorn(dat_sector_summary_by_country_quintile, cols_ex)
# summary of eu ntile with aggregate sectoral resolution
dat_sector_summary_by_eu_ntile = get_sector_summary_by_eu_ntile(eu_q_count)
# pivot to long format for plotting and attach readable indicator names
cols_ex = c("year", "eu_q_rank", "sector_agg_id")
pdat_sector_summary_by_eu_ntile =
pivot_results_longer_adorn(dat_sector_summary_by_eu_ntile, cols_ex)
Fig. 1
p1 = pdat_country_summary_by_eu_ntile %>%
filter(year == 2015,
indicator == "total_fd_me") %>%
group_by(eu_q_rank) %>%
summarise(value = sum(value)*0.000001,
eu_ntile_name = first(eu_ntile_name)) %>%
ggplot(aes(x=eu_ntile_name, y=value)) +
geom_col(position = position_dodge(), fill=pal[1]) +
#theme_ipsum() +
theme_minimal() +
theme(text=element_text(family="Liberation Sans Narrow")) +
labs(x="", y="Expenditure (trn€)") +
theme(axis.text.x = element_text(angle = 90)) +
scale_x_discrete(labels = c("D01","D02","D03","D04","D05","D06","D07","D08","D09","D10"))
p2 = pdat_country_summary_by_eu_ntile %>%
filter(year == 2015,
indicator == "total_energy_use_tj") %>%
group_by(eu_q_rank) %>%
summarise(value = sum(value)*0.000001,
eu_ntile_name = first(eu_ntile_name)) %>%
ggplot(aes(x=eu_ntile_name, y=value)) +
geom_col(position = position_dodge(), fill=pal[1]) +
#theme_ipsum() +
theme_minimal() +
theme(text=element_text(family="Liberation Sans Narrow")) +
labs(x="", y="Energy footprint (EJ)") +
theme(axis.text.x = element_text(angle = 90)) +
scale_x_discrete(labels = c("D01","D02","D03","D04","D05","D06","D07","D08","D09","D10"))
p3 = pdat_country_summary_by_eu_ntile %>%
filter(year == 2015,
indicator == "total_co2eq_kg") %>%
group_by(eu_q_rank) %>%
summarise(value = sum(value)*0.000000001,
eu_ntile_name = first(eu_ntile_name)) %>%
ggplot(aes(x=eu_ntile_name, y=value)) +
geom_col(position = position_dodge(), fill=pal[1]) +
#theme_ipsum() +
theme_minimal() +
theme(text=element_text(family="Liberation Sans Narrow")) +
labs(x="", y="Carbon footprint (MtCO2eq)") +
theme(axis.text.x = element_text(angle = 90)) +
scale_x_discrete(labels = c("D01","D02","D03","D04","D05","D06","D07","D08","D09","D10"))
p_top = p1 + p2 + p3
p1 = dat_country_summary_by_eu_ntile %>%
filter(year == 2015) %>%
ggplot(aes(x=factor(eu_q_rank), y=pe_co2eq_kg)) +
geom_violin(aes(weight=total_fd_me), fill=pal[1], color=pal[1], alpha=0.5) +
geom_point( alpha=0.3) +
geom_segment(data=dat_country_summary_by_eu_ntile %>%
filter(year == 2015) %>%
group_by(eu_q_rank) %>%
summarise(pe_co2eq_kg = weighted.mean(pe_co2eq_kg,total_fd_me)),
aes(y=pe_co2eq_kg, yend=pe_co2eq_kg, x=eu_q_rank-0.3, xend=eu_q_rank+0.3), size=1.5) +
#theme_ipsum() +
theme_minimal() +
theme(text=element_text(family="Liberation Sans Narrow")) +
labs(x="", y="Carbon intensity per expenditure (kgCO2eq/€)") +
theme(axis.text.x = element_text(angle = 90)) +
scale_x_discrete(labels = c("D01","D02","D03","D04","D05","D06","D07","D08","D09","D10"))
p2 = dat_country_summary_by_eu_ntile %>%
filter(year == 2015) %>%
ggplot(aes(x=factor(eu_q_rank), y=pe_energy_use_mj)) +
geom_violin(aes(weight=total_fd_me), fill=pal[1], color=pal[1], alpha=0.5) +
geom_point( alpha=0.3) +
geom_segment(data=dat_country_summary_by_eu_ntile %>%
filter(year == 2015) %>%
group_by(eu_q_rank) %>%
summarise(pe_energy_use_mj = weighted.mean(pe_energy_use_mj,total_fd_me)),
aes(y=pe_energy_use_mj, yend=pe_energy_use_mj, x=eu_q_rank-0.3, xend=eu_q_rank+0.3), size=1.5) +
#theme_ipsum() +
theme_minimal() +
theme(text=element_text(family="Liberation Sans Narrow")) +
labs(x="", y="Energy intensity per expenditure (MJ/€)") +
theme(axis.text.x = element_text(angle = 90)) +
scale_x_discrete(labels = c("D01","D02","D03","D04","D05","D06","D07","D08","D09","D10"))
dat3 = dat_country_summary_by_eu_ntile %>%
filter(year == 2015) %>%
mutate(intensity_e_c = total_co2eq_kg*0.001/total_energy_use_tj)
p3 = dat3 %>%
ggplot(aes(x=factor(eu_q_rank), y=intensity_e_c)) +
geom_violin(aes(weight=total_energy_use_tj), fill=pal[1], color=pal[1], alpha=0.5) +
geom_point( alpha=0.3) +
geom_segment(data=dat3 %>%
filter(year == 2015) %>%
group_by(eu_q_rank) %>%
summarise(intensity_e_c = weighted.mean(intensity_e_c,total_energy_use_tj)),
aes(y=intensity_e_c, yend=intensity_e_c, x=eu_q_rank-0.3, xend=eu_q_rank+0.3), size=1.5) +
#theme_ipsum() +
theme_minimal() +
theme(text=element_text(family="Liberation Sans Narrow")) +
labs(x="", y="Carbon intensity per energy (gCO2eq/TJ)") +
theme(axis.text.x = element_text(angle = 90)) +
scale_x_discrete(labels = c("D01","D02","D03","D04","D05","D06","D07","D08","D09","D10"))
p_bottom = p2 + p1 + p3
#p3 & theme(plot.margin = margin(1, 1, 1, 1, "mm"))
a = p_top / p_bottom + plot_annotation(tag_levels = 'a') +
plot_layout(guides = 'collect') &
theme(plot.margin = unit(c(0.25,0.25,0.25,0.25), "cm"),
legend.position = 'bottom',
axis.title.y = element_text(size=13, hjust = 0.5),
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
legend.text = element_text(size=12),
legend.title = element_text(size=13))
ggsave(here("analysis", "figures", "figure1.pdf"), device=cairo_pdf)
knitr::include_graphics(here::here("analysis", "figures", "figure1.pdf"))Fig.2
pdat_basket = get_sector_summary_by_eu_ntile_direct(eu_q_count) %>%
ungroup() %>%
filter(year==2015) %>%
left_join(read_csv(here("analysis/data/derived/sectors_agg_method1_ixi.csv")), by="sector_agg_id") %>%
group_by(eu_q_rank) %>%
mutate(value = total_fd_me/sum(total_fd_me)*100) %>%
select(five_sectors, eu_q_rank,
value) %>%
mutate(indicator = "Expenditure share (%)")
pdat_int_co2eq = get_sector_summary_by_eu_ntile_direct(eu_q_count) %>%
ungroup() %>%
filter(year==2015) %>%
left_join(read_csv(here("analysis/data/derived/sectors_agg_method1_ixi.csv")), by="sector_agg_id") %>%
mutate(value = (total_co2eq_kg)/(total_fd_me*1000000)) %>%
select(five_sectors, eu_q_rank,
value) %>%
mutate(indicator = "Carbon intensity (kgCO2eq/€)")
pdat_int_energy = get_sector_summary_by_eu_ntile_direct(eu_q_count) %>%
ungroup() %>%
filter(year==2015) %>%
left_join(read_csv(here("analysis/data/derived/sectors_agg_method1_ixi.csv")), by="sector_agg_id") %>%
mutate(value = (total_energy_use_tj)/(total_fd_me)) %>%
select(five_sectors, eu_q_rank,
value) %>%
mutate(indicator = "Energy intensity (MJ/€)")
library(viridis)
pdat = pdat_int_co2eq %>%
bind_rows(pdat_int_energy) %>%
bind_rows(pdat_basket)
pdat$facet = factor(pdat$indicator, levels = c("Expenditure share (%)", "Energy intensity (MJ/€)", "Carbon intensity (kgCO2eq/€)"))
a = ggplot(pdat, aes(x=value, y=five_sectors, color=eu_q_rank)) +
#geom_point(shape=18, alpha=0.75) +
geom_text(label="I", alpha=0.75, fontface="bold") +
scale_color_viridis(option = "A", end = 0.8,
direction = -1,
breaks = c(1, 5, 10),
name="European\ndecile") +
facet_wrap(~facet, scales="free_x") +
theme_minimal() +
labs(x="",y="")+
theme(text=element_text(size=13, family="Liberation Sans Narrow"),
panel.spacing = unit(2.5, "lines"))
ggsave(here("analysis", "figures", "figure2.pdf"), device=cairo_pdf)knitr::include_graphics(here::here("analysis", "figures", "figure2.pdf"))Fig.3
pdat = get_sector_summary_by_eu_ntile_direct(eu_q_count) %>%
ungroup() %>%
filter(year==2015) %>%
left_join(read_csv(here("analysis/data/derived/sectors_agg_method1_ixi.csv")), by="sector_agg_id") %>%
select(five_sectors, eu_q_rank, contains("pae_co2eq"), contains("pae_energy")) %>%
pivot_longer(cols=-c(five_sectors, eu_q_rank), names_to="indicator", values_to="value") %>%
mutate(indicator_type = if_else(str_detect(indicator, "co2eq"),
"tCO2eq per adult eq",
"Energy GJ per adult eq")) %>%
filter(indicator != "pae_co2eq_t",
indicator != "pae_energy_use_gj")
pal <- wes_palette("Zissou1", 4, type = "discrete")
pal = pal[c(2,1,3,4)]
p1 = ggplot(pdat %>% filter(indicator_type == "tCO2eq per adult eq"),
aes(x=(eu_q_rank), y=value, fill=indicator)) +
geom_col(position = position_stack()) +
facet_wrap(~five_sectors, ncol = 5) +
scale_fill_manual(values = pal, name="Location", labels=c("Direct", "Domestic", "Europe", "non-Europe")) +
scale_x_continuous(breaks = c(1,2,3,4,5,6,7,8,9,10),
labels = c("D01","D02","D03","D04","D05","D06","D07","D08","D09","D10")) +
#theme_ipsum() +
theme_minimal() +
theme(text=element_text(family="Liberation Sans Narrow")) +
labs(x="", y="Carbon footprint tCO2eq per adult eq") +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, size = 12),
axis.text.y = element_text(size = 12),
strip.text = element_text(size = 12),
axis.title.y = element_text(size = 10))
p2 = ggplot(pdat %>% filter(indicator_type == "Energy GJ per adult eq"),
aes(x=(eu_q_rank), y=value, fill=indicator)) +
geom_col(position = position_stack()) +
facet_wrap(~five_sectors, ncol = 5) +
scale_fill_manual(values = pal, name="Location", labels=c("Direct", "Domestic", "Europe", "non-Europe")) +
scale_x_continuous(breaks = c(1,2,3,4,5,6,7,8,9,10),
labels = c("D01","D02","D03","D04","D05","D06","D07","D08","D09","D10")) +
#theme_ipsum() +
theme_minimal() +
theme(text=element_text(family="Liberation Sans Narrow")) +
labs(x="", y="Energy footprint GJ per adult eq") +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, size = 12),
axis.text.y = element_text(size = 12),
strip.text = element_text(size = 12),
axis.title.y = element_text(size = 10))
a = p1 / p2 + plot_layout(guides = "collect") & theme(legend.position = 'bottom',
plot.margin = margin(1, 1, 1, 1, "mm"),
panel.spacing = unit(c(1, 1, 1, 1), "mm"))
hm = pdat %>% filter(indicator_type == "Energy GJ per adult eq") %>%
group_by(eu_q_rank) %>%
summarise(value = sum(value))
ggsave(here("analysis", "figures", "figure3.pdf"))knitr::include_graphics(here::here("analysis", "figures", "figure3.pdf"))Fig.4
pal <- wes_palette("Zissou1", 4, type = "discrete")
pal = pal[c(2,1,3,4)]
pdat_int_energy = get_sector_summary_by_eu_ntile_direct(eu_q_count) %>%
ungroup() %>%
filter(year==2015) %>%
left_join(read_csv(here("analysis/data/derived/sectors_agg_method1_ixi.csv")),
by="sector_agg_id") %>%
mutate(intensity_energy = (total_energy_use_tj)/(total_fd_me)) %>%
select(five_sectors, eu_q_rank,
intensity_energy) %>%
filter(eu_q_rank == 10) %>%
select(-eu_q_rank)
pdat_final_demand = get_sector_summary_by_eu_ntile_direct(eu_q_count) %>%
ungroup() %>%
filter(year==2015) %>%
left_join(read_csv(here("analysis/data/derived/sectors_agg_method1_ixi.csv")),
by="sector_agg_id") %>%
left_join(pdat_int_energy, by="five_sectors") %>%
mutate(total_energy_use_tj_new = (total_fd_me)*intensity_energy) %>%
mutate(total_energy_use_tj_diff = total_energy_use_tj-total_energy_use_tj_new) %>%
select(five_sectors, eu_q_rank,
total_energy_use_tj_diff, total_energy_use_tj_new) %>%
group_by(eu_q_rank) %>%
summarise(total_energy_use_tj_new = sum(total_energy_use_tj_new)*0.000001,
total_energy_use_tj_diff = sum(total_energy_use_tj_diff)*0.000001) %>%
pivot_longer(-c(eu_q_rank), names_to = "indicator", values_to = "value")
p1 = ggplot(pdat_final_demand, aes(x=eu_q_rank, y=value, fill=indicator, alpha=indicator)) +
geom_col(position = position_stack()) +
scale_fill_manual(values=c(pal[1], pal[2]),
labels=c("2015", "Best technology"), name="Energy\nfootprint") +
scale_alpha_manual(values=c(0.3,1),labels=c("2015", "Best technology"), name="Energy\nfootprint")+
scale_x_continuous(breaks = c(1,2,3,4,5,6,7,8,9,10),
labels = c("D01","D02","D03","D04","D05","D06","D07","D08","D09","D10")) +
labs(y="Energy footprint (EJ)", x="") +
theme_minimal() +
coord_flip() +
theme(legend.position="bottom")
pdat_energy_country = get_sector_summary_by_country_quintile_direct() %>%
ungroup() %>%
filter(year==2015) %>%
left_join(read_csv(here("analysis/data/derived/sectors_agg_method1_ixi.csv")),
by="sector_agg_id") %>%
left_join(pdat_int_energy, by="five_sectors") %>%
mutate(total_energy_use_tj_new = (total_fd_me)*intensity_energy) %>%
#mutate(total_energy_use_tj_save = total_energy_use_tj_new/total_energy_use_tj*100) %>%
select(iso2,
total_energy_use_tj_new, total_energy_use_tj) %>%
group_by(iso2) %>%
summarise(total_energy_use_tj_save = 100 - sum(total_energy_use_tj_new)/sum(total_energy_use_tj)*100)
library(rworldmap)
library(ggthemes)
df_country_mean = pdat_energy_country %>%
mutate(iso2 = if_else(iso2 == "EL", "GR", iso2)) %>%
mutate(iso2 = if_else(iso2 == "UK", "GB", iso2)) %>%
left_join(ISO_3166_1 %>% select(iso2 = Alpha_2, iso3 = Alpha_3), by="iso2")
quantile_rank_map = joinCountryData2Map(df_country_mean,
joinCode = "ISO_A3",
nameJoinColumn = "iso3")
quantile_rank_map_poly = fortify(quantile_rank_map) #extract polygons
quantile_rank_map_poly = merge(quantile_rank_map_poly, quantile_rank_map@data, by.x="id", by.y="ADMIN", all.x=T)
quantile_rank_map_poly = quantile_rank_map_poly %>% arrange(id, order)
map = ggplot() +
geom_polygon(data = quantile_rank_map_poly, aes(long, lat, group = group), color="black") +
geom_polygon(data = quantile_rank_map_poly, aes(long, lat, group = group,
fill=total_energy_use_tj_save)) +
#scale_fill_gradient(name="Mean decile \nrank", low="skyblue", high = "skyblue4", na.value = "#EEEEEE") +
scale_fill_viridis(direction = -1, discrete = F, na.value = "white", name="Energy\nsavings (%)") +
theme_map() +
labs(x="",y="")+
coord_map("bonne", lat0 = 50,xlim = c(-9, 40), ylim = c(38, 68), clip="on") +
theme(legend.position = c(0.85, 0.6))
a = p1 + map + plot_annotation(tag_levels = 'a') +
theme(plot.margin = unit(c(0.25,0.25,0.25,0.25), "cm"),
legend.position = 'bottom',
axis.title.y = element_text(size=13, hjust = 0.5),
legend.text = element_text(size=12),
legend.title = element_text(size=13))
ggsave(here("analysis", "figures", "figure4.pdf"))knitr::include_graphics(here::here("analysis", "figures", "figure4.pdf"))Fig.5
pdat_int_energy = get_sector_summary_by_eu_ntile_direct(eu_q_count) %>%
ungroup() %>%
filter(year==2015) %>%
left_join(read_csv(here("analysis/data/derived/sectors_agg_method1_ixi.csv")),
by="sector_agg_id") %>%
mutate(intensity_energy = (total_energy_use_tj)/(total_fd_me)) %>%
select(five_sectors, eu_q_rank,
intensity_energy) %>%
filter(eu_q_rank == 10) %>%
select(-eu_q_rank)
pdat_final_demand = get_sector_summary_by_eu_ntile_direct(eu_q_count) %>%
ungroup() %>%
filter(year==2015) %>%
left_join(read_csv(here("analysis/data/derived/sectors_agg_method1_ixi.csv")),
by="sector_agg_id") %>%
left_join(pdat_int_energy, by="five_sectors") %>%
mutate(total_energy_use_tj_new = (total_fd_me)*intensity_energy) %>%
mutate(total_energy_use_tj_diff = total_energy_use_tj-total_energy_use_tj_new) %>%
select(eu_q_rank,total_energy_use_tj_new) %>%
group_by(eu_q_rank) %>%
summarise(total_energy_use_tj_new = sum(total_energy_use_tj_new)) %>%
mutate(pae_energy_use_tj = total_energy_use_tj_new/33417583,
pae_energy_use_gj = pae_energy_use_tj*1000)
df_energy_deciles = pdat_final_demand %>%
select(eu_q_rank, pae_energy_use_gj)
ineq_curr = df_energy_deciles$pae_energy_use_gj[10]/df_energy_deciles$pae_energy_use_gj[1]
library(readxl)
df_scenario_info = read_excel(here("analysis/data/raw/scenarios.xlsx"), sheet="overview") %>%
select(scenario, fe_gj_aeu = final_energy_gj_per_aeu_2050,
ccs_required = primary_energy_fossil_w_ccs2050_ej,
description) %>%
arrange(fe_gj_aeu) %>%
mutate(fe_gj_aeu = round(fe_gj_aeu),
ccs_required = round(ccs_required))
mea = c(min(df_scenario_info$fe_gj_aeu),max(df_scenario_info$fe_gj_aeu))
mer = c(min(df_scenario_info$fe_gj_aeu),53)
c_mean_mea = round((27+mea[2])/2)
c_mean_mer = round((mer[1]+mer[2])/2)
# run once to save file. If changing scenarios included or input data, need to re-run and save 'scenarios_fine.rds'
# vectorized function that returns scaled quantiles given
#quantile index column and column with quantile averages
#qidx: quantile index
#qavg_to_scale: column to scale
#first_target: target value of first quantile
#mean_target: target mean of scaled quantiles
scaled_quantiles <- function(.data,
qidx,
qavg_to_scale,
first_target,
mean_target) {
# cumbersomely extract current quantile mean
mean_current = .data %>%
ungroup() %>%
summarise(mean_cur = first(mean({{qavg_to_scale}}))) %>%
pull(mean_cur)
# cumbersomely extract current first wuantile value
first_current = .data %>%
ungroup() %>%
arrange({{qidx}}) %>%
summarise(first_cur = first({{qavg_to_scale}})) %>%
pull(first_cur)
df_tmp = .data %>%
mutate(tmp = {{qavg_to_scale}}*mean_target/mean_current)
first_tmp = df_tmp$tmp[1]
df_tmp = df_tmp %>%
mutate(scaled = mean_target-(mean_target-tmp) * (mean_target-first_target)/(mean_target-first_tmp)) %>%
select({{qidx}}, scaled) %>%
mutate(v_mean = mean_target, v_first = first_target)
}
## run once to save file
df_all = NULL
for (min_energy in seq(from=mer[1], to=mer[2], by=0.25)) {
for (mean_energy in seq(from=mea[1], to=mea[2], by=0.25)) {
if (min_energy <= mean_energy) {
df_all = df_all %>%
bind_rows(df_energy_deciles %>%
scaled_quantiles(eu_q_rank, pae_energy_use_gj, min_energy, mean_energy))
}
}
}
saveRDS(df_all, here("analysis/data/derived/scenarios_fine.rds"))
round_by = 10
df_all = readRDS(here("analysis/data/derived/scenarios_fine.rds")) %>%
filter(eu_q_rank %in% c(1,10)) %>%
group_by(v_mean, v_first) %>%
summarise(ratio = last(scaled)/first(scaled)) %>%
mutate(bin_ratio = if_else((ratio*100)%%round_by > round_by*0.5,
ratio*100+(round_by-(ratio*100)%%round_by),
ratio*100-(ratio*100)%%round_by)) %>%
group_by(bin_ratio, v_first) %>%
summarise(v_mean = mean(v_mean)) %>%
mutate(bin_ratio = bin_ratio*0.01)
df_scenario = df_all %>%
filter(v_mean %in% df_scenario_info$fe_gj_aeu) %>%
left_join(df_scenario_info, by=c("v_mean"="fe_gj_aeu"))
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/ae)",
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/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)) +
scale_y_continuous(breaks = c(2.5,5,7.5,10,12.5)) # +
#theme_ipsum()
ggsave(here("analysis", "figures", "figure5.pdf"))knitr::include_graphics(here::here("analysis", "figures", "figure5.pdf"))