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5-2_figures.R
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### 5.2 Figures
source("config/init.R")
# 1 Fig 1 LAC vs frontier -------------------------------------------------
applyEnv()
figure_name <- "Fig1_latin-america-caribbean-vs-frontier.pdf"
sarahLoad(c("cause_hierarchy", "frontier_scaled", "region"), folder = "data/processed")
temp1 <- region %>%
filter(region == "Latin America & Caribbean", year >= 2000, year <= 2050, age >= 20, causename %in% focus) %>%
mutate(sex = ifelse(sex == 1, "Males", "Females"),
age2 = makeDisplayAgeGroup(age),
age = makeAgeGroup(age),
causename = factor(causename, levels = focus)) %>%
group_by(region, year, sex, age2, ghecause, causename) %>%
summarize_at(vars(dths, pop), ~sum(.), .groups = "drop") %>%
mutate(dths_rate = dths / pop * 100000) %>%
ungroup() %>%
select(year, sex.frontier = sex, age2, causename, dths_rate)
temp2 <- frontier_scaled %>%
filter(year >= 2000, year <= 2050, age >= 20, causename %in% focus) %>%
mutate(sex.frontier = "Frontier",
age2 = makeDisplayAgeGroup(age),
age = makeAgeGroup(age),
causename = factor(causename, levels = focus)) %>%
group_by(year, sex, age2, ghecause, causename) %>%
mutate(frontier = mean(frontier)) %>%
ungroup() %>%
select(year, sex.frontier, age2, causename, dths_rate = frontier)
ggdata <- bind_rows(temp1, temp2) %>%
mutate(sex.frontier = factor(sex.frontier, levels = c("Females", "Males", "Frontier")),
dths_rate = ifelse(year %in% covid, NA, dths_rate)) %>%
arrange(year, age2, causename, rev(sex.frontier))
fills <- setNames(colorFunct(length(levels(ggdata$sex.frontier))), levels(ggdata$sex.frontier))
colors <- setNames(darken(fills, 0.3), levels(ggdata$sex.frontier))
panels <- list()
for(i in unique(ggdata$causename)){
ggdata2 <- ggdata %>% filter(causename == i)
ylims <- ggRange(ggdata2$dths_rate)
if(ylims[1] <= 0){
ylims <- c(0.1, ylims[2])
}
ylab <- ifelse(i == "Cardiovascular diseases", "Mortality rate (per 100K, log scale)", "")
panels[[i]] <- ggplot(ggdata2) +
facet_wrap(~ age2, nrow = 1) +
geom_line(aes(x = year, y = dths_rate, color = sex.frontier, group = sex.frontier), alpha = 0.3) +
geom_line(data = subset(ggdata2, year >= 2020),
aes(x = year, y = dths_rate, color = sex.frontier, group = sex.frontier)) +
geom_point(data = subset(ggdata2, year < 2020),
aes(x = year, y = dths_rate, color = sex.frontier, fill = sex.frontier),
shape = 21, size = 1) +
labs(title = i) +
scale_x_continuous("", breaks = seq(2000, 2050, 10), labels = c("'00", "'10", "'20", "'30", "'40", "'50"),
expand = expansion(mult = 0.1)) +
scale_y_continuous(ylab, trans = "log",
limits = ylims, breaks = log.breaks, labels = log.labels) +
scale_color_manual("", values = colors) +
scale_fill_manual("", values = fills) +
theme(axis.title.x = element_blank(),
legend.position = "bottom")
if(i != tail(unique(ggdata$causename), 1)){
panels[[paste(i, "spacer")]] <- ""
}
if(i == tail(unique(ggdata$causename), 1)){
grob <- ggarrange(plotlist = panels, ncol = 1, align = "hv", heights = c(1, -0.025, 1, -0.025, 1, -0.025, 1, -0.025, 1),
common.legend = TRUE, legend = "bottom")
saveGGplot(grob, figure_name, "output/figures", width = 9, height = 12)
}
}
# 2 Fig 2 Economic value --------------------------------------------------
applyEnv()
figure_name <- "Fig2_economic-value.pdf"
sarahLoad(c("cause_hierarchy", "cause_aesth"), folder = "data/processed")
sarahLoad("region_calculations", folder = "output/data")
ggdata <- region_calculations %>%
filter(mece.lvl == 2, sex != 3) %>%
left_join(cause_hierarchy %>% select(ghecause, main_causename), by = "ghecause") %>%
mutate(sex = ifelse(sex == 1, "Males", "Females")) %>%
mutate(sex = factor(sex, levels = c("Females", "Males"))) %>%
ungroup() %>%
arrange(region, year, sex, mece.lvl, ghecause)
panels <- list()
for(j in unique(ggdata$sex)){
ggdata2 <- ggdata %>% filter(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$causename)]
panels[[j]] <- ggplot(ggdata2) +
facet_wrap(~ wrapper(region, 15), nrow = 1) +
geom_col(aes(x = as.factor(year), y = v.r, fill = causename, color = causename)) +
labs(title = j) +
scale_x_discrete("") +
scale_y_continuous("Proportion of annual income", limits = c(0, 0.5), expand = expansion(mult = c(0, 0.03))) +
scale_color_manual("Level 2 causes", values = colors, breaks = breaks) +
scale_fill_manual("Level 2 causes", values = fills, breaks = breaks) +
theme(axis.line = element_blank(), axis.ticks.x = element_blank(), axis.title.x = element_blank(),
legend.position = "right",
panel.grid.major.x = element_blank(),
panel.grid.minor.y = element_line(color = alpha("black", 0.2), size = 0.2)) +
guides(color = guide_legend(ncol = 1, title.position = "top"))
if(j == "Females"){
panels[["spacer"]] <- ""
}
if(j == "Males"){
grob <- ggarrange(plotlist = panels, ncol = 1, align = "hv", heights = c(1, -0.01, 1),
common.legend = TRUE, legend = "right")
saveGGplot(grob, figure_name, "output/figures", width = 13, height = 12)
}
}
# 3 Fig 3 ROC -------------------------------------------------------------
applyEnv()
# Figure name
figure_name <- "Fig3_roc.pdf"
sarahLoad(c("cause_hierarchy", "cause_aesth"), folder = "data/processed")
sarahLoad("roc", folder = "output/data")
wrap <- 35
ggdata <- roc %>% ungroup() %>%
filter(region != "World", sex != 3) %>%
left_join(cause_hierarchy %>% select(ghecause, main_causename), by = "ghecause") %>%
filter((main_causename == "Noncommunicable diseases" & mece.lvl == 2) |
(main_causename != "Noncommunicable diseases" & mece.lvl == 1)) %>%
left_join(cause_aesth, by = c("ghecause", "causename", "mece.lvl")) %>%
mutate(causename = factor(wrapper(causename, wrap), levels = wrapper(causename.levels, wrap)),
sex = ifelse(sex == 1, "Males", "Females")) %>%
arrange(causename)
bound <- max(abs(ggRange(c(ggdata$roc1, ggdata$roc2), 0.01)))
steady.decline <- data.frame(x = c(-bound, -bound, 0, 0), y = c(-bound, 0, 0, -bound), label = "Steady decline")
steady.increase <- data.frame(x = c(bound, bound, 0, 0), y = c(bound, 0, 0, bound), label = "Steady increase")
polys <- bind_rows(steady.decline, steady.increase)
aesth <- ggdata %>% select(causename, color) %>% unique()
fills <- setNames(aesth$color, aesth$causename)
colors <- setNames(darken(aesth$color, 0.5), aesth$causename)
grob <- ggplot(ggdata) +
facet_wrap(~ region, ncol = 2) +
geom_vline(aes(xintercept = 0), color = lighten("black", 0.3)) +
geom_hline(aes(yintercept = 0), color = lighten("black", 0.3)) +
geom_polygon(data = polys, aes(x = x, y = y, group = label), fill = alpha(ggsci::pal_material("grey")(9)[8], 0.2)) +
geom_text(data = subset(polys, x == min(x) & y == min(y)),
aes(x = x, y = y, label = label),
hjust = 0, vjust = 0, nudge_x = 0.005, nudge_y = 0.005, family = "Barlow") +
geom_text(data = subset(polys, x == max(x) & y == max(y)),
aes(x = x, y = y, label = label),
hjust = 1, vjust = 1, nudge_x = -0.005, nudge_y = -0.005, family = "Barlow") +
geom_point(aes(x = roc1, y = roc2, color = causename, fill = causename, shape = sex), size = 4) +
scale_x_continuous("Annual rate of change, 2000-2019\n(percent per year)", limits = c(-bound, bound), expand = expansion(mult = 0),
label = label_percent(), breaks = pretty_breaks(5)) +
scale_y_continuous("Annual rate of change, 2019-2050\n(percent per year)", limits = c(-bound, bound), expand = expansion(mult = 0),
label = label_percent(), breaks = pretty_breaks(5)) +
scale_color_manual("Cause", values = colors) +
scale_fill_manual("Cause", values = fills) +
scale_shape_manual("Sex", values = c(23, 22)) +
theme(legend.position = "bottom",
strip.background = element_rect(fill = "white"),
strip.placement = "outside",
strip.text = element_text(face = "bold", size = 18, hjust = 0, vjust = 0, margin = margin(0, 0, 10, 0))) +
guides(color = guide_legend(title.position = "bottom", ncol = 2, order = 1, override.aes = list(shape = 21)),
fill = guide_legend(title.position = "bottom", ncol = 2, order = 1),
shape = guide_legend(title.position = "bottom", nrow = 2, order = 2, override.aes = list(fill = "white")))
saveGGplot(grob, figure_name, "output/figures", width = 10, height = 14)
# 4 Extended Data 1-6 -----------------------------------------------------
applyEnv()
figure_name <- "ED_FigNUMBER_REGION-vs-frontier.pdf"
sarahLoad(c("cause_hierarchy", "frontier_scaled", "region"), folder = "data/processed")
temp1 <- region %>%
filter(region != "Latin America & Caribbean", year >= 2000, year <= 2050, age >= 20, causename %in% focus) %>%
mutate(sex = ifelse(sex == 1, "Males", "Females"),
age2 = makeDisplayAgeGroup(age),
age = makeAgeGroup(age),
causename = factor(causename, levels = focus)) %>%
group_by(region, year, sex, age2, ghecause, causename) %>%
summarize_at(vars(dths, pop), ~sum(.), .groups = "drop") %>%
mutate(dths_rate = dths / pop * 100000) %>%
ungroup() %>%
select(region, year, sex.frontier = sex, age2, causename, dths_rate)
temp2 <- frontier_scaled %>%
filter(year >= 2000, year <= 2050, age >= 20, causename %in% focus) %>%
mutate(sex.frontier = "Frontier",
age2 = makeDisplayAgeGroup(age),
age = makeAgeGroup(age),
causename = factor(causename, levels = focus)) %>%
group_by(year, sex, age2, ghecause, causename) %>%
mutate(region = "Frontier", frontier = mean(frontier)) %>%
ungroup() %>%
select(region, year, sex.frontier, age2, causename, dths_rate = frontier)
ggdata <- bind_rows(temp1, temp2) %>%
mutate(sex.frontier = factor(sex.frontier, levels = c("Females", "Males", "Frontier")),
dths_rate = ifelse(year %in% covid, NA, dths_rate)) %>%
arrange(region, year, age2, causename, rev(sex.frontier))
fills <- setNames(colorFunct(length(levels(ggdata$sex.frontier))), levels(ggdata$sex.frontier))
colors <- setNames(darken(fills, 0.3), levels(ggdata$sex.frontier))
figs <- list()
for(j in unique(ggdata$region[ggdata$region != "Frontier"])){
panels <- list()
for(i in unique(ggdata$causename)){
ggdata2 <- ggdata %>% filter(region %in% c(j, "Frontier"), causename == i)
ylims <- ggRange(ggdata2$dths_rate)
if(ylims[1] <= 0){
ylims <- c(0.1, ylims[2])
}
ylab <- ifelse(i == "Cardiovascular diseases", "Mortality rate (per 100K, log scale)", "")
panels[[i]] <- ggplot(ggdata2) +
facet_wrap(~ age2, nrow = 1) +
geom_line(aes(x = year, y = dths_rate, color = sex.frontier, group = sex.frontier), alpha = 0.3) +
geom_line(data = subset(ggdata2, year >= 2020),
aes(x = year, y = dths_rate, color = sex.frontier, group = sex.frontier)) +
geom_point(data = subset(ggdata2, year < 2020),
aes(x = year, y = dths_rate, color = sex.frontier, fill = sex.frontier),
shape = 21, size = 1) +
labs(title = i) +
scale_x_continuous("", breaks = seq(2000, 2050, 10), labels = c("'00", "'10", "'20", "'30", "'40", "'50"),
expand = expansion(mult = 0.1)) +
scale_y_continuous(ylab, trans = "log",
limits = ylims, breaks = log.breaks, labels = log.labels) +
scale_color_manual("", values = colors) +
scale_fill_manual("", values = fills) +
theme(axis.title.x = element_blank(),
legend.position = "bottom")
if(i != tail(unique(ggdata$causename), 1)){
panels[[paste(i, "spacer")]] <- ""
}
}
figs[[j]] <- ggarrange(plotlist = panels, ncol = 1, align = "hv", heights = c(1, -0.025, 1, -0.025, 1, -0.025, 1, -0.025, 1),
common.legend = TRUE, legend = "bottom")
number <- which(unique(ggdata$region[ggdata$region != "Frontier"]) == j)
figure_name_ <- gsub("NUMBER", number, gsub("REGION", slug(j), figure_name))
saveGGplot(figs[[j]], figure_name_, "output/figures", width = 9, height = 12)
}