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5-4_appendix-figures.R
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5-4_appendix-figures.R
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### 5.4 Appendix figures
# 1 Fig A1 Frontier comparison --------------------------------------------
applyEnv()
figure_name <- "FigA1_INFO.pdf"
sarahLoad("frontier_base", folder = "data/processed")
ggdata <- frontier_base %>%
left_join(cause_hierarchy %>% select(ghecause, parent_ghecause, parent_causename),
by = "ghecause") %>%
filter(age >= 20, is.na(sex) | sex == 2, definition %in% c("Minimum", "10th percentile")) %>%
mutate(causename = ifelse(causename %in% sex.specific, paste(causename, "(females)"), causename),
parent_causename = ifelse(parent_causename %in% sex.specific, paste(parent_causename, "(females)"), parent_causename)) %>%
mutate(display.causename = factor(wrapper(causename, 35), levels = wrapper(causename.levels2, 35)),
frontier = ifelse(frontier < 0.1, 0, frontier)) %>%
mutate(group = paste(definition, year))
colors <- colorFunct(2, names = c("10th percentile", "Minimum"), color.and.fill = TRUE)
for(parent in unique(ggdata$parent_causename)[!is.na(unique(ggdata$parent_causename))]){
ggdata2 <- ggdata %>% filter(causename == parent | parent_causename == parent)
gglist <- list("parent" = ggdata %>% filter(causename == parent),
"children" = ggdata %>% filter(parent_causename == parent))
id <- ids[which(unique(ggdata$parent_causename)[!is.na(unique(ggdata$parent_causename))] == parent)]
ylims <- ggRange(ggdata2$frontier)
if(all(ylims != 0)){ylims.log <- ylims}
if(ylims[1] == 0){ylims.log <- c(0.1, ylims[2])}
if(all(ylims == 0)){ylims.log <- c(0.1, 5)}
panels <- list()
for(i in names(gglist)){
xlab <- ifelse(i == "parent", "Age", "")
ylab <- ifelse(i == "parent", "Frontier mortality rate (per 100K, log scale)", "")
base <- ggplot(gglist[[i]]) +
# Minimum
geom_line(data = subset(gglist[[i]], definition == "Minimum"),
aes(x = age, y = frontier, group = group, color = year)) +
scale_color_material(palette = "amber", breaks = c(2000, 2005, 2010, 2015, 2019),
guide = guide_colorbar(title = "Minimum", title.position = "top",
frame.colour = "black", ticks = TRUE, ticks.colour = "black",
nbin = 10, label.position = "bottom", label.hjust = 0.5,
barwidth = 10, barheight = 1,
direction = 'horizontal', order = 1)) +
new_scale_color() +
# 10th percentile
geom_line(data = subset(gglist[[i]], definition == "10th percentile"),
aes(x = age, y = frontier, group = group, color = year)) +
scale_color_material(palette = "red", breaks = c(2000, 2005, 2010, 2015, 2019),
guide = guide_colorbar(title = "10th percentile", title.position = "top",
frame.colour = "black", ticks = TRUE, ticks.colour = "black",
nbin = 10, label.position = "bottom", label.hjust = 0.5,
barwidth = 10, barheight = 1,
direction = 'horizontal', order = 2)) +
scale_x_continuous(xlab) +
scale_y_continuous(ylab, trans = "log",
limits = ylims.log, breaks = log.breaks, labels = log.labels) +
theme(legend.position = "bottom")
if(i == "parent"){
panels[[paste(id, i)]] <- base +
labs(title = wrapper(paste(id, parent), 60))
saveGGplot(x = panels[[paste(id, i)]],
name = gsub("INFO", slug(paste0(gsub("[()]", "", id), 1)), figure_name),
folder = "output/figures/FigA1", width = 7.5, height = 5)
}else{
panels[[paste(id, i)]] <- base +
labs(subtitle = "Lower level causes") +
facet_wrap(~ display.causename, ncol = 2) +
theme(legend.position = "none")
h <- ceiling(length(unique(gglist[["children"]]$ghecause)) / 2) * 2.5
saveGGplot(x = panels[[paste(id, i)]],
name = gsub("INFO", slug(paste0(gsub("[()]", "", id), 2)), figure_name),
folder = "output/figures/FigA1", width = 7.5, height = h)
}
}
}
# 2 Fig A2 Frontier process -----------------------------------------------
applyEnv()
figure_name <- "FigA2_frontier-process.pdf"
sarahLoad(c("cause_hierarchy", "frontier_info/frontier_info_4"),
folder = "data/processed")
temp1 <- frontier_info_4 %>%
filter(age >= 30, year >= 2000, year <= 2050, definition == "10th percentile") %>%
mutate(projected = ifelse(is.na(harmonized), projected, harmonized)) %>%
mutate(causename = case_when(sex == 1 ~ paste0(causename, " (males)"),
sex == 2 ~ paste0(causename, " (females)"),
TRUE ~ causename)) %>%
pivot_longer(cols = c(base, harmonized, projected, scaled), names_to = "stage", values_to = "frontier") %>%
mutate(stage = capitalize(stage)) %>%
mutate(stage = factor(stage, levels = c("Base", "Harmonized", "Projected", "Scaled"))) %>%
mutate(observed = year < 2020) %>%
filter(!is.na(frontier))
ggdata <- temp1 %>%
filter(is.na(sex) | sex == 2) %>%
mutate(age2 = makeMathersAgeGroup(age),
age = makeAgeGroup(age),
frontier = ifelse(frontier < 0.1, 0, frontier)) %>%
mutate(zero = frontier == 0) %>%
arrange(age)
color.info <- groupColorInfo(ggdata, "age2", "age")
colors <- groupColor(color.info$n, color.info$names, color.and.fill = TRUE)
shapes <- c(21, NA)
grobs <- list()
for(i in unique(ggdata$causename)){
ggdata2 <- ggdata %>% filter(causename == i)
id <- ids[which(unique(ggdata$causename) == i)]
ylims <- ggRange(ggdata2$frontier)
if(all(ylims == 0)){
ylims <- c(0.1, 1)
}
if(ylims[1] == 0){
ylims <- c(0.1, ylims[2])
}
figure <- ggplot(ggdata2) +
facet_grid(cols = vars(stage), rows = vars(age2)) +
geom_line(data = subset(ggdata2, !observed),
aes(x = year, y = frontier, color = age)) +
geom_point(data = subset(ggdata2, observed),
aes(x = year, y = frontier, color = age, fill = age, shape = zero)) +
labs(title = paste(id, i)) +
scale_x_continuous("", breaks = seq(2000, 2050, 10), labels = c("'00", "'10", "'20", "'30", "'40", "'50")) +
scale_y_continuous("Frontier mortality rate (per 100K, log scale)", trans = "log",
limits = ylims, breaks = log.breaks, labels = log.labels) +
scale_color_manual("", values = colors$colors) +
scale_fill_manual("", values = colors$fills) +
scale_shape_manual("", values = shapes) +
theme(axis.line = element_blank(), axis.title.x = element_blank(),
legend.position = "bottom",
panel.border = element_rect(color = alpha("black", 0.4), size = 0.2)) +
guides(fill = guide_legend(nrow = 2),
color = guide_legend(nrow = 2, override.aes = list(pch = 21)),
shape = "none")
grobs[[id]] <- as_grob(figure)
}
saveGGplot(grobs, figure_name, folder = "output/figures", width = 12, height = 10, multipage = TRUE)
# 3 Fig B1 Frontier -------------------------------------------------------
applyEnv()
sarahLoad("frontier_scaled", folder = "data/processed")
figure_name <- "FigB1_frontier.pdf"
ggdata <- frontier_scaled %>%
filter(year >= 2000, year <= 2050, is.na(sex) | sex == 2, age >= 30, definition == "10th percentile") %>%
left_join(cause_hierarchy %>% select(ghecause, prefix), by = "ghecause") %>%
mutate(causename = ifelse(!is.na(sex), paste(causename, "(females)"), causename),
frontier = ifelse(frontier < 0.01, 0, frontier),
age2 = makeMathersAgeGroup(age),
age = makeAgeGroup(age),
observed = year < 2020) %>%
mutate(causename = factor(causename, levels = causename.levels2),
zero = frontier == 0) %>%
arrange(prefix, age)
color_info <- groupColorInfo(ggdata, "age2", "age")
colors <- groupColor(color_info$n, color_info$names, color.and.fill = TRUE)
grobs <- list()
for(i in unique(ggdata$causename)){
ggdata2 <- ggdata %>% filter(causename == i)
id <- ids[which(unique(ggdata$causename) == i)]
panels <- list()
ylims <- ggRange(ggdata2$frontier)
if(all(ylims != 0)){ylims.log <- ylims}
if(ylims[1] == 0){ylims.log <- c(0.1, ylims[2])}
if(all(ylims == 0)){
ylims <- c(0, 5)
ylims.log <- c(0.1, 5)
}
base <- ggplot(ggdata2) +
facet_wrap(~ age2, nrow = 1) +
geom_line(data = subset(ggdata2, observed),
aes(x = year, y = frontier, color = age), alpha = 0.3) +
geom_point(data = subset(ggdata2, observed),
aes(x = year, y = frontier, fill = age, color = age), pch = 21) +
geom_line(data = subset(ggdata2, !observed),
aes(x = year, y = frontier, color = age)) +
scale_x_continuous("", breaks = seq(2000, 2050, 10), labels = c("'00", "'10", "'20", "'30", "'40", "'50"),
expand = expansion(mult = 0.1)) +
scale_color_manual("", values = colors$colors) +
scale_fill_manual("", values = colors$fills) +
theme(axis.title.x = element_blank(),
legend.position = "right",
panel.background = element_rect(fill = NA)) +
guides(color = guide_legend(ncol = 1), fill = guide_legend(ncol = 1))
panels[["standard"]] <- base +
labs(title = paste(id, i), subtitle = "Standard scale") +
scale_y_continuous("Frontier mortality rate (per 100K)", limits = ylims,
labels = label_number(scale_cut = cut_short_scale()))
panels[["spacer"]] <- ""
panels[["log"]] <- base +
labs(title = "", subtitle = "Log scale") +
scale_y_continuous("Frontier mortality rate (per 100K, log scale)", trans = "log", limits = ylims.log,
breaks = log.breaks, labels = log.labels)
grobs[[id]] <- ggarrange(plotlist = panels, ncol = 1, heights = c(1, -0.1 , 1),
align = "hv", common.legend = TRUE, legend = "right")
if(i == tail(unique(ggdata$causename), 1)){
saveGGplot(grobs, figure_name, folder = "output/figures", width = 11, height = 8.5, multipage = TRUE)
}
}
# 5 Fig B2-B7 Regional mortality projections ------------------------------
applyEnv()
figure_name <- "FigB-ID.pdf"
sarahLoad("region", folder = "data/processed")
ggdata <- region %>%
filter(age >= 30) %>%
mutate(sex = ifelse(sex == 1, "Males", "Females"),
age2 = makeMathersAgeGroup(age),
age = makeAgeGroup(age),
dths_rate = ifelse(dths_rate < 0.1, 0, dths_rate),
observed = year < 2020) %>%
mutate(dths_rate = ifelse(year %in% covid, NA, dths_rate)) %>%
mutate(sex = factor(sex, levels = c("Females", "Males")))
color.info <- groupColorInfo(ggdata, "age2", "age")
colors <- groupColor(color.info$n, color.info$names, color.and.fill = TRUE)
for(j in unique(ggdata$region)){
grobs <- list()
for(i in unique(ggdata$causename)){
ggdata2 <- ggdata %>% filter(region == j, causename == i) %>% arrange(sex)
id <- ids[which(unique(ggdata$causename) == i)]
ylims <- ggRange(ggdata2$dths_rate)
if(all(ylims != 0)){ylims.log <- ylims}
if(ylims[1] == 0){ylims.log <- c(0.1, ylims[2])}
if(all(ylims == 0)){
ylims <- c(0, 5)
ylims.log <- c(0.1, 5)
}
panels <- list()
for(k in unique(ggdata2$sex)){
ggdata3 <- ggdata2 %>% filter(sex == k)
title <- ifelse(k == "Females", paste(id, i), " ")
base <- ggplot(ggdata3) +
facet_wrap(~age2, nrow = 1) +
geom_line(data = subset(ggdata3, observed),
aes(x = year, y = dths_rate, color = age), alpha = 0.3) +
geom_line(data = subset(ggdata3, !observed),
aes(x = year, y = dths_rate, color = age)) +
geom_point(data = subset(ggdata3, observed),
aes(x = year, y = dths_rate, color = age, fill = age), pch = 21) +
scale_x_continuous("", breaks = seq(2000, 2050, 20), labels = c("'00", "'20", "'40"),
expand = expansion(mult = 0.14)) +
scale_color_manual("", values = colors$colors) +
scale_fill_manual("", values = colors$fills) +
theme(axis.title.x = element_blank(),
legend.position = "bottom",
panel.grid.minor.y = element_blank()) +
guides(color = guide_legend(nrow = 2),
fill = guide_legend(nrow = 2))
panels[[paste(k, "standard")]] <- base +
labs(title = title, subtitle = paste0(k, " | Standard scale")) +
scale_y_continuous("Mortality rate (per 100K)",
limits = ylims, labels = label_number(scale_cut = cut_long_scale()))
panels[[paste(k, "space")]] <- ""
panels[[paste(k, "log")]] <- base +
labs(subtitle = paste0(k, " | Log scale")) +
scale_y_continuous("Mortality rate (per 100K, log scale)", trans = "log",
limits = ylims.log, breaks = log.breaks, labels = log.labels)
}
panels <- panels[c("Females standard", "Males standard",
"Females space", "Males space",
"Females log", "Males log")]
grobs[[id]] <- ggarrange(plotlist = panels, nrow = 3, ncol = 2, align = "hv", heights = c(1, -0.1, 1),
common.legend = TRUE, legend = "bottom")
}
num <- which(unique(ggdata$region) == j) + 1; lab <- slug(j)
figure_name2 <- gsub("-ID", paste(num, lab, sep = "_"), figure_name)
saveGGplot(grobs, figure_name2, folder = "output/figures", width = 15, height = 10, multipage = TRUE)
}
# 6 Fig B8-B10 Economic value ---------------------------------------------
applyEnv()
figure_name <- "FigB-ID.pdf"
sarahLoad(c("cause_hierarchy", "cause_aesth"), folder = "data/processed")
sarahLoad("region_calculations", folder = "output/data")
ggdata <- region_calculations %>%
filter(mece.lvl != 2, sex != 3) %>%
left_join(cause_hierarchy %>% select(ghecause, main_causename), by = "ghecause") %>%
mutate(sex = ifelse(sex == 1, "Males", "Females")) %>%
mutate(sex = factor(sex, levels = c("Females", "Males"))) %>%
ungroup() %>%
arrange(region, year, sex, mece.lvl, ghecause)
grobs <- list()
for(i in unique(ggdata$mece.lvl)){
panels <- list()
for(j in unique(ggdata$sex)){
wrap <- 35
ggdata2 <- ggdata %>% filter(mece.lvl == i, sex == j) %>%
left_join(cause_aesth, by = c("ghecause", "causename", "mece.lvl")) %>%
mutate(causename = wrapper(causename, wrap)) %>%
mutate(causename = factor(causename, levels = wrapper(causename.levels, wrap)))
aesth <- ggdata2 %>% select(causename, color) %>% unique()
fills <- setNames(aesth$color, aesth$causename)
colors <- setNames(darken(aesth$color, 0.3), aesth$causename)
breaks <- wrapper(causename.levels, 35)[wrapper(causename.levels, 35) %in% unique(ggdata2$causename)]
panels[[j]] <- ggplot(ggdata2) +
facet_wrap(~ wrapper(region, 15), nrow = 1) +
geom_col(aes(x = as.factor(year), y = v.r, fill = causename, color = causename)) +
labs(title = j) +
scale_x_discrete("") +
scale_y_continuous("Proportion of annual income", limits = c(0, 0.5), expand = expansion(mult = c(0, 0.03))) +
scale_color_manual(paste("Level", i, "causes"), values = colors, breaks = breaks) +
scale_fill_manual(paste("Level", i, "causes"), values = fills, breaks = breaks) +
theme(axis.line = element_blank(), axis.ticks.x = element_blank(), axis.title.x = element_blank(),
legend.position = "right",
panel.grid.major.x = element_blank(),
panel.grid.minor.y = element_line(color = alpha("black", 0.2), size = 0.2)) +
guides(color = guide_legend(ncol = 1, title.position = "top"))
if(j == "Females"){
panels[["spacer"]] <- ""
}
}
grobs[[i]] <- ggarrange(plotlist = panels, ncol = 1, align = "hv", heights = c(1, -0.01, 1),
common.legend = TRUE, legend = "right")
num <- which(unique(ggdata$mece.lvl) == i) + 8; lab <- slug(paste("economic value lvl", i))
figure_name2 <- gsub("-ID", paste(num, lab, sep = "_"), figure_name)
saveGGplot(grobs[[i]], figure_name2, "output/figures", width = 13, height = 12)
}
# 7 Fig B10-13 Economic value distribution --------------------------------
applyEnv()
figure_name <- "FigB-ID.pdf"
sarahLoad(c("cause_hierarchy", "cause_aesth"), folder = "data/processed")
sarahLoad("region_calculations", folder = "output/data")
ggdata <- region_calculations %>%
filter(sex != 3) %>%
left_join(cause_hierarchy %>% select(ghecause, main_causename), by = "ghecause") %>%
mutate(sex = ifelse(sex == 1, "Males", "Females")) %>%
mutate(sex = factor(sex, levels = c("Females", "Males"))) %>%
ungroup() %>%
arrange(region, year, sex, mece.lvl, ghecause)
grobs <- list()
for(i in unique(ggdata$mece.lvl)){
panels <- list()
for(j in unique(ggdata$sex)){
wrap <- 35
ggdata2 <- ggdata %>% filter(mece.lvl == i, sex == j) %>%
left_join(cause_aesth, by = c("ghecause", "causename", "mece.lvl")) %>%
mutate(causename = wrapper(causename, wrap)) %>%
mutate(causename = factor(causename, levels = wrapper(causename.levels, wrap)))
aesth <- ggdata2 %>% select(causename, color) %>% unique()
fills <- setNames(aesth$color, aesth$causename)
colors <- setNames(darken(aesth$color, 0.3), aesth$causename)
breaks <- wrapper(causename.levels, 35)[wrapper(causename.levels, 35) %in% unique(ggdata2$causename)]
panels[[j]] <- ggplot(ggdata2) +
facet_wrap(~ wrapper(region, 15), nrow = 1) +
geom_col(aes(x = as.factor(year), y = v.r, fill = causename, color = causename), position = "fill") +
labs(title = j) +
scale_x_discrete("") +
scale_y_continuous("Fraction", expand = expansion(mult = c(0, 0.03))) +
scale_color_manual(paste("Level", i, "causes"), values = colors, breaks = breaks) +
scale_fill_manual(paste("Level", i, "causes"), values = fills, breaks = breaks) +
theme(axis.line = element_blank(), axis.ticks.x = element_blank(), axis.title.x = element_blank(),
legend.position = "right",
panel.grid.major.x = element_blank(),
panel.grid.minor.y = element_line(color = alpha("black", 0.2), size = 0.2)) +
guides(color = guide_legend(ncol = 1, title.position = "top"))
if(j == "Females"){
panels[["spacer"]] <- ""
}
}
grobs[[i]] <- ggarrange(plotlist = panels, ncol = 1, align = "hv", heights = c(1, -0.01, 1),
common.legend = TRUE, legend = "right")
num <- which(unique(ggdata$mece.lvl) == i) + 10; lab <- slug(paste("economic value dist lvl", i))
figure_name2 <- gsub("-ID", paste(num, lab, sep = "_"), figure_name)
saveGGplot(grobs[[i]], figure_name2, "output/figures", width = 13, height = 12)
}
# 8 Fig C1-C5 Sensitivity analyses ----------------------------------------
applyEnv()
# Figure name
figure_name <- "FigC-ID.pdf"
# Loading data
sarahLoad(c("cause_hierarchy", "cause_aesth"), folder = "data/processed")
sarahLoad("SA_region_calculations", folder = "output/data")
ggdata <- SA_region_calculations %>%
filter(mece.lvl == 2, sex != 3) %>%
left_join(cause_hierarchy %>% select(ghecause, main_causename), by = "ghecause") %>%
mutate(sex = ifelse(sex == 1, "Males", "Females")) %>%
mutate(sex = factor(sex, levels = c("Females", "Males")),
scenario = factor(scenario, levels = c("Minimum frontier definition",
"Lower income elasticity", "Higher income elasticity",
"OECD base income",
"Lower discount rate", "Higher discount rate"))) %>%
ungroup() %>%
arrange(scenario, region, year, sex, mece.lvl, ghecause)
ylims <- ggdata %>%
group_by(scenario, region, year, sex, mece.lvl) %>%
summarize(v.r = sum(v.r)) %>% pull(v.r) %>%
ggRange(accuracy = 0.1)
panels <- list()
for(i in unique(ggdata$scenario)){
id <- which(unique(ggdata$scenario) == i)
id <- paste(id, slug(i), sep = "_")
for(j in unique(ggdata$sex)){
ggdata2 <- ggdata %>% filter(scenario == i, sex == j) %>%
left_join(cause_aesth, by = c("ghecause", "causename", "mece.lvl")) %>%
mutate(causename = factor(causename, levels = causename.levels))
aesth <- ggdata2 %>% select(causename, color) %>% unique()
fills <- setNames(aesth$color, aesth$causename)
colors <- setNames(darken(aesth$color, 0.3), aesth$causename)
breaks <- causename.levels[causename.levels %in% unique(ggdata2$causename)]
panels[[j]] <- ggplot(ggdata2) +
facet_wrap(~ wrapper(region, 15), nrow = 1) +
geom_col(aes(x = as.factor(year), y = v.r, fill = causename, color = causename)) +
labs(title = j) +
scale_x_discrete("") +
scale_y_continuous("Proportion of annual income", limits = ylims, expand = expansion(mult = c(0, 0.03))) +
scale_color_manual("Level 2 causes", values = colors, breaks = breaks) +
scale_fill_manual("Level 2 causes", values = fills, breaks = breaks) +
theme(axis.line = element_blank(), axis.ticks.x = element_blank(), axis.title.x = element_blank(),
legend.position = "right",
panel.grid.major.x = element_blank(),
panel.grid.minor.y = element_line(color = alpha("black", 0.2), size = 0.2)) +
guides(color = guide_legend(ncol = 1, title.position = "top"))
if(j == "Females"){
panels[["spacer"]] <- ""
}
if(j == "Males"){
grob <- ggarrange(plotlist = panels, ncol = 1, align = "hv", heights = c(1, -0.01, 1),
common.legend = TRUE, legend = "right")
saveGGplot(grob, gsub("-ID", id, figure_name), "output/figures", width = 13, height = 12)
}
}
}
# 9 Fig D1 Rankings -------------------------------------------------------
applyEnv()
figure_name <- "FigD1_rankings.pdf"
sarahLoad("region_calculations", folder = "output/data")
sarahLoad(c("cause_aesth", "ghe_recoded", "region"), folder = "data/processed")
temp1 <- region_calculations %>%
filter(mece.lvl == 2, sex != 3) %>%
group_by(region, year, sex) %>%
mutate(rank = rank(-v.r)) %>%
arrange(region, sex, year, desc(v.r)) %>%
select(region, year, sex, causename, v.r_rank = rank)
temp2 <- region %>%
filter(year %in% c(2000, 2019, 2050)) %>%
group_by(region, year, sex, ghecause, causename) %>%
summarize_at(vars(dths, pop), ~ sum(.)) %>%
mutate(dths_rate = dths / pop * 100000) %>%
left_join(cause_hierarchy %>% select(ghecause, mece_lvl2), by = "ghecause") %>%
filter(mece_lvl2) %>%
group_by(region, year, sex) %>%
mutate(rank = rank(-dths_rate)) %>%
arrange(region, sex, year, desc(dths_rate)) %>%
select(region, year, sex, causename, dths.rate_rank = rank)
temp3 <- full_join(temp1, temp2, by = c("region", "year", "sex", "causename")) %>%
left_join(cause_aesth %>% filter(mece.lvl == 2), by = "causename") %>%
arrange(region, year, sex, v.r_rank, dths.rate_rank)
ggdata <- temp3 %>%
filter(year != 2000) %>%
pivot_longer(cols = ends_with("rank"), names_to = "rank.sys", values_to = "rank") %>%
mutate(sex = ifelse(sex == 1, "Males", "Females"),
rank.sys = ifelse(rank.sys == "dths.rate_rank", "Mortality rate\nranking", "Economic value\nranking")) %>%
mutate(rank.sys = factor(rank.sys, levels = c("Mortality rate\nranking", "Economic value\nranking")))
aesth <- ungroup(ggdata) %>% select(causename, color) %>% unique()
fills <- setNames(aesth$color, aesth$causename)
colors <- setNames(darken(aesth$color, 0.4), aesth$causename)
colors2 <- setNames(rep("black", length(aesth$causename)), aesth$causename)
colors2["Intentional injuries"] <- colors2["Unintentional injuries"] <- "white"
colors3 <- setNames(darken(fills, 0.1), aesth$causename)
colors3["Infectious and parasitic diseases"] <- darken(colors3["Infectious and parasitic diseases"], 0.3)
colors3["Maternal and neonatal conditions"] <- darken(colors3["Maternal and neonatal conditions"], 0.1)
grobs <- list()
for(i in unique(ggdata$region)){
id <- ids[which(unique(ggdata$region) == i)]
ggdata2 <- ggdata %>% filter(region == i)
grobs[[i]] <- ggplot(ggdata2) +
facet_grid(rows = vars(year), cols = vars(sex), scales = "free_x") +
# Ranking label
geom_text(data = subset(ggdata2, rank == 1),
aes(x = rank.sys, y = 0, label = rank.sys),
family = "Barlow", nudge_y = 0.5) +
# Transparent thick line
geom_line(aes(x = rank.sys, y = rank, color = causename, group = causename), size = 3, alpha = 0.5) +
scale_color_manual(values = fills) +
new_scale_color() +
# Opaque thin line and circles
geom_line(aes(x = rank.sys, y = rank, color = causename, group = causename)) +
geom_point(aes(x = rank.sys, y = rank, color = causename, fill = causename), pch = 21, size = 9) +
scale_color_manual(values = colors) +
new_scale_color() +
geom_text(aes(x = rank.sys, y = rank, label = rank, color = causename),
family = "Barlow", fontface = "bold", size = 4) +
scale_color_manual(values = colors2) +
new_scale_color() +
# Cause labels
geom_text(data = subset(ggdata2, rank.sys == "Economic value\nranking"),
aes(x = rank.sys, y = rank, label = causename, color = causename),
family = "Barlow", hjust = 0, nudge_x = 0.15, size = 4) +
scale_color_manual(values = colors3) +
labs(title = paste(id, i)) +
scale_x_discrete("", expand = expansion(mult = c(0.4, 1.85))) +
scale_y_reverse("", expand = expansion(mult = c(0.06, 0.08))) +
scale_fill_manual(values = fills) +
theme(axis.line = element_blank(), axis.text = element_blank(), axis.ticks = element_blank(), axis.title = element_blank(),
panel.background = element_rect(fill = "white", color = alpha("black", 0.2)), panel.grid.major = element_blank(),
legend.position = "none")
if(i == tail(unique(ggdata$region), 1)){
saveGGplot(grobs, figure_name, "output/figures", width = 10, height = 10, multipage = TRUE)
}
}
# 11 END ------------------------------------------------------------------
# Tidying environment and notifying the end of the script
notifyScript(); tidy()
# -------------------------------------------------------------------------
frontier_comparison$prefix <- getCauseInfo(frontier_comparison$ghecause, return = "prefix")
frontier_comparison$level <- getCauseInfo(frontier_comparison$ghecause, return = "level")
ggdata <- frontier_comparison %>%
filter(is.na(sex) | sex == 2, age >= 30) %>%
mutate(age2 = makeMathersAgeGroup(age),
causename = ifelse(is.na(sex), causename, paste(causename, "(females)"))) %>%
mutate(age = makeAgeGroup(age)) %>%
pivot_longer(cols = c("min", "p10"), names_to = "definition", values_to = "frontier") %>%
mutate(definition = ifelse(definition == "min", "Minimum", "10th percentile"),
frontier = ifelse(frontier < 0.00001, NA, frontier)) %>%
arrange(prefix, causename, age, desc(definition), is.na(frontier))
shapes <- c("10th percentile" = 16, "Minimum" = 21)
lines <- c("10th percentile" = 1, "Minimum" = 1)
color_info <- ggdata %>%
dplyr::select(age2, age) %>% unique() %>%
group_by(age2) %>%
dplyr::summarize(n = n(), age = paste0(age, collapse = "; "))
colors <- groupColorFunct(n = color_info$n,
names = unlist(str_split(paste(color_info$age, collapse = "; "), "; ")), color.and.fill = TRUE)
grobs <- list()
for(i in unique(ggdata$causename)){
id <- ids[which(unique(ggdata$causename) == i)]
ggdata2 <- ggdata %>% filter(causename == i)
figure <- ggplot(ggdata2) +
facet_wrap(~ age, ncol = 6) +
geom_path(aes(x = year, y = frontier, color = age, group = definition), linetype = "dotted") +
geom_line(aes(x = year, y = frontier, color = age, linetype = definition)) +
geom_point(aes(x = year, y = frontier, color = age, shape = definition), fill = "white") +
labs(title = paste(id, i),
subtitle = "Log scale*",
caption = makeCaption(captions["Zero"])) +
scale_x_continuous("", breaks = c(seq(2000, 2019, 10), 2019), labels = c("'00", "'10", "'19")) +
scale_y_continuous("Frontier mortality rate (per 100K)", trans = "log",
breaks = log.breaks, labels = log.labels, limits = c(0.1, NA)) +
scale_color_manual("Age", values = colors$colors) +
scale_shape_manual("Frontier definition", values = shapes, breaks = c("10th percentile", "Minimum")) +
scale_linetype_manual("Frontier definition", values = lines) +
guides(color = "none",
shape = guide_legend(title.position = "top"),
linetype = guide_legend(title.position = "top")) +
theme(axis.title.x = element_blank(),
legend.position = "bottom",
panel.grid.minor.x = element_line(color = alpha("black", 0.2), size = 0.2))
grobs[[id]] <- as_grob(figure)
}
saveMultipage(grobs, figure_name, width = 10, height = 7)
# Region comparison -------------------------------------------------------
sarahLoad(c("frontier_lagged", "region"), folder = "data/processed")
temp1 <- region %>%
filter(year <= 2040, age >= 30) %>%
select(-c(dths, pop))
temp2 <- temp1 %>%
select(year, sex, age, ghecause, causename) %>%
unique() %>%
mutate(sex_match = ifelse(causename %in% sex.specific, sex, NA),
region = "Frontier") %>%
select(region, year, sex_match, sex, everything())
temp3 <- frontier_lagged %>%
filter(year >= 2000, age >= 30) %>%
dplyr::rename(sex_match = sex,
dths_rate = frontier)
temp4 <- full_join(temp2, temp3, by = c("year", "sex_match", "age", "ghecause", "causename")) %>%
select(-sex_match)
temp5 <- bind_rows(temp1, temp4)
region.levels <- c(sort(unique(temp5$region[temp5$region != "Frontier"])), "Frontier")
ggdata <- temp5 %>%
mutate(region = factor(region, levels = region.levels),
age = makeAgeGroup(age),
sex = ifelse(sex == 1, "Males", "Females"),
dths_rate = ifelse(dths_rate < 0.1, 0, dths_rate)) %>%
mutate(age = gsub(" ", "\n", age),
zero = dths_rate == 0)
colors <- colorFunct(n = length(region.levels)-1, names = region.levels[1:length(region.levels)-1], color.and.fill = TRUE)
colors$colors <- c(colors$colors, "Frontier" = "black")
colors$fills <- c(colors$fills, "Frontier" = lighten("black", 0.3))
shapes <- c(21, NA)
grobs <- list()
for(i in unique(ggdata$causename)){
panels <- list()
for(j in unique(ggdata$sex)){
ggdata2 <- ggdata %>% filter(causename == i, sex == j)
ylims <- ggRange(ggdata %>% filter(causename == i) %>% pull(dths_rate))
if(ylims[1] <= 0){
ylims <- c(0.1, ylims[2])
}
id <- ids[which(unique(ggdata$causename) == i)]
base <- ggplot(ggdata2) +
facet_wrap(~ age, nrow = 3) +
geom_line(data = subset(ggdata2, year >= 2020), aes(x = year, y = dths_rate, group = region, color = region)) +
geom_point(data = subset(ggdata2, year < 2020), aes(x = year, y = dths_rate, group = region, color = region, fill = region, shape = zero)) +
scale_x_continuous("Year", breaks = seq(2000, 2040, 10), labels = c("", "'10", "'20", "'30", "'40")) +
scale_y_continuous("Mortality rate (per 100K)", trans = "log", limits = ylims, breaks = log.breaks, labels = log.labels) +
scale_color_manual("Region", values = colors$colors) +
scale_fill_manual("Region", values = colors$fills) +
scale_shape_manual("", values = shapes) +
guides(color = guide_legend(title.position = "bottom", ncol = 4, override.aes = list(shape = 21)),
fill = guide_legend(title.position = "bottom", ncol = 4),
shape = "none") +
theme(legend.position = "bottom", legend.direction = "horizontal")
if(j == "Males"){
panels[[j]] <- base +
labs(title = paste(id, i), subtitle = j)
}else{
panels[[j]] <- base +
labs(title = "", subtitle = j)
}
}
legend <- get_legend(base)
grobs[[i]] <- ggarrange(plotlist = panels, ncol = 2, align = "hv",
common.legend = TRUE, legend = "bottom", legend.grob = legend)
}
saveGGplot(grobs, "region_frontier.pdf", folder = "output/figures",
width = 15, height = 12, multipage = TRUE)
# # 3 Fig 2 Latin America mortality trajectory ------------------------------
#
# sarahLoad("region", folder = "data/processed")
#
# figure_name <- "Fig2.pdf"
#
# ggdata <- region %>%
# filter(grepl("Latin America", region),
# causename %in% c("Cardiovascular diseases", "Malignant neoplasms",
# "Injuries", "All causes",
# "Communicable, maternal, perinatal and nutritional conditions"),
# age >= 30) %>%
# left_join(cause_hierarchy %>% select(ghecause, prefix), by = "ghecause") %>%
# mutate(dths_rate = ifelse(dths_rate < 0.01, 0, dths_rate),
# sex = case_when(sex == 1 ~ "Males",
# sex == 2 ~ "Females",
# TRUE ~ NA_character_),
# age2 = makeDisplayAgeGroup(age),
# age = makeAgeGroup(age)) %>%
# mutate(zero = dths_rate == 0) %>%
# arrange(prefix, region, age, sex, year) %>%
# ungroup()
#
# color_info <- groupColorInfo(ggdata, "age2", "age")
# colors <- groupColor(color_info$n, color_info$names, color.and.fill = TRUE)
# shapes <- c(21, 0)
#
# grobs <- list()
# for(i in unique(ggdata$causename)){
#
# ggdata2 <- ggdata %>% filter(causename == i)
#
# id <- ids[which(unique(ggdata$causename) == i)]
#
# ylims <- ggRange(ggdata2$dths_rate)
# if(ylims[1] <= 0){
# ylims <- c(0.1, ylims[2])
# }
#
# Panels <- list()
# for(j in unique(ggdata2$sex)){
#
# ggdata3 <- ggdata2 %>% filter(sex == j)
# panels <- list()
#
# title <- ifelse(j == "Females", paste(id, i), "")
#
# panels[["Standard"]] <- ggplot(ggdata3) +
# facet_wrap(~ age2, nrow = 1) +
# geom_line(data = subset(ggdata3, year >= 2020), aes(x = year, y = dths_rate, color = age)) +
# geom_point(data = subset(ggdata3, year < 2020), aes(x = year, y = dths_rate, fill = age, color = age, shape = zero)) +
# labs(title = title,
# subtitle = paste0(j, " | Standard scale")) +
# scale_x_continuous("", breaks = seq(2000, 2040, 20), labels = c("2000", "'20", "'40")) +
# scale_y_continuous("Mortality rate (per 100K)", limits = ylims, labels = label_number(scale_cut = cut_long_scale())) +
# scale_color_manual("", values = colors$colors) +
# scale_fill_manual("", values = colors$fills) +
# scale_shape_manual("", values = shapes) +
# theme(axis.title.x = element_blank(),
# panel.background = element_rect(fill = NA),
# legend.position = "bottom",
# panel.grid.minor.x = element_line(color = alpha("black", 0.2), size = 0.2)) +
# guides(color = guide_legend(nrow = 2), shape = "none")
#
# if(j == "Males"){
# panels[["Standard"]] <- panels[["Standard"]] + theme(axis.title.y = element_blank())
# }
#
# panels[[paste(j, "Spacer")]] <- ""
#
# panels[["Log"]] <- ggplot(ggdata3) +
# facet_wrap(~ age2, nrow = 1) +
# geom_line(data = subset(ggdata3, year >= 2020), aes(x = year, y = dths_rate, color = age)) +
# geom_point(data = subset(ggdata3, year < 2020), aes(x = year, y = dths_rate, fill = age, color = age, shape = zero)) +
# labs(subtitle = paste0(j, " | Log scale")) +
# scale_x_continuous("", breaks = seq(2000, 2040, 20), labels = c("2000", "'20", "'40")) +
# scale_y_continuous("Mortality rate (per 100K)", trans = "log",
# limits = ylims, breaks = log.breaks, labels = log.labels) +
# scale_color_manual("", values = colors$colors) +
# scale_fill_manual("", values = colors$fills) +
# scale_shape_manual("", values = shapes) +
# theme(axis.title.x = element_blank(),
# panel.background = element_rect(fill = NA),
# panel.grid.minor.x = element_line(color = alpha("black", 0.2), size = 0.2))
#
# if(j == "Males"){
# panels[["Log"]] <- panels[["Log"]] + theme(axis.title.y = element_blank())
# }
#
# Panels[[j]] <- ggarrange(plotlist = panels, ncol = 1, heights = c(1, -0.1, 1),
# align = "hv", legend = "none")
#
# }
#
# figure <- ggarrange(plotlist = Panels, ncol = 2, align = "hv", widths = c(1.05, 1),
# common.legend = TRUE, legend = "bottom", legend.grob = get_legend(panels[[1]]))
#
# grobs[[id]] <- cowplot::as_grob(figure)
#
# }
#
# saveGGplot(grobs, figure_name, "output/figures", width = 13, height = 8, multipage = TRUE)
#
#
#
# 5 Fig 4 economic values -------------------------------------------------
figure_name <- "Fig4-OPTION.pdf"
sarahLoad("region_calculations", folder = "data/processed")
region.levels <- rev(sort(unique(region_calculations$region)))
ggdata <- region_calculations %>%
filter(ghecause %in% c(0, 10, 1100, 610, 1510),
year == 2019, lambda == 5, epsilon == 1, mece %in% c("Level 1", "Level 2")) %>%
mutate(sex = ifelse(sex == 1, "Males", "Females"),
sex = factor(sex, levels = c("Males", "Females")),
causename = factor(wrapper(causename, 35), levels = rev(wrapper(causename.levels, 35))),
multiple = V_bar / Y) %>%
group_by(region, year, sex, ghecause, causename) %>%
slice_head(n = 1) %>%
dplyr::select(region, year, sex, ghecause, causename, multiple) %>%
group_by(region, year, ghecause, causename) %>%
mutate(lower = min(multiple),
upper = max(multiple)) %>%
ungroup()
ggdata_segments <- ggdata %>%
select(region, year, ghecause, causename, lower, upper) %>%
unique()
colors <- colorFunct(length(unique(ggdata$causename)),
unique(ggdata$causename), color.and.fill = TRUE)
shapes <- c("Females" = 21, "Males" = 24)
figure <- ggplot(ggdata) +
facet_wrap(~ region, ncol = 1) +
geom_segment(data = ggdata_segments,
aes(y = causename, yend = causename, x = lower, xend = upper, color = causename),
size = 0.75, alpha = 0.5) +
geom_point(aes(x = multiple, y = causename, color = causename, fill = causename, shape = sex), size = 2) +
geom_label(data = ggdata_segments, aes(x = lower-0.04, y = causename, label = format(round(lower, 2), nsmall = 2, trim = TRUE)),
hjust = 1, family = "Barlow", fill = alpha("white", 0.8), label.size = 0) +
geom_label(data = ggdata_segments, aes(x = upper+0.04, y = causename, label = format(round(upper, 2), nsmall = 2, trim = TRUE)),
hjust = 0, family = "Barlow", fill = "white", label.size = 0) +
labs(caption = bquote("Note: Economic values are calculated using an income elasticity of 1.")) +
scale_x_continuous("Multiples of GNI per capita", expand = expansion(mult = c(0.1, 0.1)),
breaks = pretty_breaks(5)) +
scale_y_discrete("") +
scale_color_manual("", values = colors$colors) +
scale_fill_manual("", values = colors$fills) +
scale_shape_manual("", values = shapes) +
theme(axis.title.y = element_blank(), axis.line.y = element_blank(),
legend.position = "bottom",
panel.grid.minor.x = element_line(color = alpha("black", 0.2), size = 0.2)) +
guides(color = "none", fill = "none",
shape = guide_legend(override.aes = list(shape = c(19, 17))))
# # 4 Fig 3 Proportion of of GNI -------------------------------------------
#
# # Figure name
# figure_name <- "Fig3.pdf"
#
# # Loading data
# sarahLoad("cause_hierarchy", folder = "data/processed")
# sarahLoad("region_calculations", folder = "output/data")
#
# wrap <- 35
#
# ggdata <- region_calculations %>%
# left_join(cause_hierarchy %>% select(ghecause, main_causename)) %>%
# mutate(sex = ifelse(sex == 1, "Males", "Females"),
# causename = wrapper(causename, wrap),
# main_causename = factor(main_causename, levels = c("Communicable, maternal, perinatal and nutritional conditions",
# "Noncommunicable diseases", "Injuries"))) %>%
# arrange(region, sex, mece.lvl, main_causename, causename, year) %>%
# ungroup()
#
# grobs <- list()
# for(i in unique(ggdata$mece.lvl)){
#
# id <- ids[which(unique(ggdata$mece.lvl) == i)]
#
# panels <- list()
# for(j in unique(ggdata$sex)){
#
# ggdata2 <- ggdata %>% filter(mece.lvl == i, sex == j) %>%
# mutate(causename = factor(causename, levels = wrapper(causename.levels, wrap)))