require(pacman)
p_load(tidytuesdayR, tidyverse, trashpanda, magick, rnaturalearth, sf, hexbin)
options(scipen = 999)Global Health Spending
Load Packages
Load Data
tuesdata <- tidytuesdayR::tt_load('2026-04-21')
financing_schemes <- tuesdata$financing_schemes
health_spending <- tuesdata$health_spending
spending_purpose <- tuesdata$spending_purposePlot
df <- spending_purpose |>
filter(country_name == "Canada",
unit == "constant 2023 US$")
plot <- df |>
ggplot() +
geom_col(aes(x = year, y = value, group = spending_purpose, fill = spending_purpose), position = position_stack()) +
scale_y_continuous(labels = scales::unit_format(unit = "B", scale = 1e-9),
breaks = c(0,
50000000000,
100000000000,
150000000000,
200000000000)) +
scale_fill_viridis_d("Spending Purpose") +
theme_cole(remove_grid = TRUE, base_size = 20) +
labs(x = "Year", y = "Amount Spent (USD)",
title = "Annual Health Spending in Canada, WHO Global Health Expenditure Database") +
theme(plot.title = element_text(hjust = 0))
# Save and display images
current_dir <- dirname(knitr::current_input())
plot_name <- "canada_health_spending.png"
ggsave(plot = plot,
dpi = "screen",
width = 16,
height = 10,
device = ragg::agg_png,
filename = file.path(current_dir, plot_name))
# Read the big plot
img <- image_read(file.path(current_dir, plot_name))
# Force 16:9 aspect ratio with minimal padding
# Target size: 1200x675 px (16:9)
img_card <- image_scale(img, "1200x675") # scale to fit inside 16:9
img_card <- image_extent(
img_card,
geometry = "1200x675",
gravity = "center"
)
# Save as card preview
image_write(img_card, path = file.path(current_dir, "preview.png"))
knitr::include_graphics(
file.path(current_dir, plot_name)
)
References
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