eASMR vs Government Stringency [World]

.chart_world_easmr_v_gvt_stringency.r

library(readr)
library(scales)
library(ggrepel)
library(ggpubr)
library(ggpmisc)

sf <- 2
width <- 600 * sf
height <- 335 * sf

options(vsc.dev.args = list(width = width, height = height, res = 72 * sf))

source(paste0(
  "https://gist.githubusercontent.com/USMortality/",
  "cf0dc23d0d5ca609539fe11c9c089fcc/raw/asmr_country_dataset.r"
))

df2 <- df |> filter(date < 2023, stringency_index > 0)

chart <- ggplot(df2, aes(x = stringency_index, y = asmr_who_ex_p, label = iso3c)) +
  geom_text_repel() +
  stat_cor(aes(label = paste(..rr.label.., ..p.label.., sep = "~`,`~")),
    method = "pearson",
    label.x.npc = "left",
    label.y.npc = "top",
    r.accuracy = 0.01,
    p.accuracy = 0.01
  ) +
  stat_poly_line() +
  labs(
    title = paste0(
      "Oxford Government Stringency Index vs. ",
      "Age Standardized Excess Mortality (2020)"
    ),
    subtitle = paste(
      "Source: Mortality.Watch, Oxford",
      "Baseline: Mean 2017-'19",
      sep = " · "
    ),
    x = "Sum of Stringency Index",
    y = "Excess Mortality"
  ) +
  theme_bw() +
  scale_x_continuous(labels = comma) +
  scale_y_continuous(labels = scales::percent_format()) +
  facet_wrap(vars(date))
ggplot2::ggsave(
  filename = "chart1.png", plot = chart, width = width, height = height,
  units = "px", dpi = 72 * sf, device = grDevices::png, type = c("cairo")
)

# Create the plot
# Partition the stringency column into groups
# Determine the range of stringency and break it into 5 equal intervals
df2 <- df2 |>
  filter(date == "2020") |>
  mutate(stringency_group = cut(stringency_index,
    breaks = quantile(stringency_index,
      probs = seq(0, 1, length.out = 4),
      na.rm = TRUE
    ),
    labels = c("Low", "Moderate", "High"),
    include.lowest = TRUE
  ))

# Create the plot
chart <- ggplot(df2, aes(x = stringency_group, y = asmr_who_ex_p)) +
  scale_y_continuous(labels = scales::percent_format()) +
  geom_boxplot() + # Boxplot for the distribution
  geom_dotplot(binaxis = "y", stackdir = "center", dotsize = 1) +
  stat_summary(
    fun.data = function(x) {
      mean_cl_normal(x, conf.int = 0.95)
    },
    geom = "errorbar",
    width = 0.2,
    color = "red"
  ) +
  stat_summary(
    fun = mean,
    geom = "point",
    color = "blue",
    size = 3
  ) +
  labs(
    title =
      paste0(
        "Age-Standardized Excess Mortality Rate (2020) ",
        "by Government Stringency Group"
      ),
    subtitle = "Median/Quartiles · Mean/95% CI",
    x = "Stringency Group (by Sum of Oxford Government Stringency Index)",
    y = "Age-Standardized Excess Mortality Rate (2020)"
  ) +
  coord_cartesian(ylim = c(-0.1, .2)) +
  theme_bw()
ggplot2::ggsave(
  filename = "chart2.png", plot = chart, width = width, height = height,
  units = "px", dpi = 72 * sf, device = grDevices::png, type = c("cairo")
)

https://www.mortality.watch/charts/list.html#easmr-vs-government-stringency-world