Government Health-Care Expenditure over last 20 years

Animated line plot comparing Public Health Spending per Capita (2000–2021) across countries. Data sourced from World Bank and processed by Our World in Data.

Government Health Care Expenditure vs. its Tax Revenue (in different Countries)

This animated line plot compares Public Health Spending per Capita from 2000 to 2021 across various countries. The Y-axis illustrates Public Health Spending per Capita, which includes all recurrent and capital spending from government sources, external borrowing, grants, and social health insurance funds. It is measured in current international dollars, adjusting for price differences between countries. Each line represents a country.

The data for public health spending is sourced from multiple providers, compiled by the World Bank and processed by Our World in Data. The data pipeline includes standardizing country names, converting units, and calculating derived indicators such as per capita measures. For a detailed description of the data processing and links to the original sources, please refer to Our World in Data’s data pipeline documentation and World Bank’s World Development Indicators.

An animated line plot that compares Public Health Spending per Capita from 2000 to 2021 across various countries.

How I made this graphic?

Getting the data

# Data Import and Wrangling Tools
library(tidyverse)            # All things tidy
library(owidR)                # Get data from Our World in R

# Final plot tools
library(scales)               # Nice Scales for ggplot2
library(fontawesome)          # Icons display in ggplot2
library(ggtext)               # Markdown text support
library(showtext)             # Display fonts in ggplot2
library(colorspace)           # To lighten and darken colours
library(gganimate)            # Animations

# Getting the data
search1 <- owidR::owid_search("health spending")

rawdf <- owid(search1[2,1])

Data Wrangling

df_continents <- rnaturalearth::ne_countries() |> 
  as_tibble() |> 
  select(continent, iso_a3, iso_a2) |> 
  rename(code = iso_a3) |> 
  mutate(iso_a2 = str_to_lower(iso_a2))

df <- rawdf |> 
  janitor::clean_names() |> 
  as_tibble() |> 
  rename(
    ghe = domestic_general_government_health_expenditure_per_capita_ppp_current_international,
    trpc = tax_revenues_per_capita_current_international,
    pop = population_historical
  ) |> 
  select(-countries_continents) |> 
  
  # Minor house-keeping corrections
  filter(!is.na(ghe)) |> 
  filter(!is.na(trpc)) |> 
  left_join(df_continents) |> 
  filter(!str_detect(entity, "(WB)")) |> 
  filter(!str_detect(entity, "income")) |> 
  filter(!is.na(code) & code != "OWID_WRL") |> 
  mutate(
    continent = case_when(
      code %in% c("FRA", "NOR") ~ "Europe",
      code %in% c("SGP", "MUS") ~ "Asia",
      .default = continent
    )
  ) |> 
  filter(!is.na(continent))

# selcon0 <- df |> 
#   mutate(rtio = ghe/trpc) |> 
#   group_by(code) |> 
#   slice_max(order_by = rtio, n = 1) |> 
#   ungroup() |> 
#   slice_max(order_by = rtio, n = 3) |> 
#   pull(entity)
# 
# selcon1 <- df |> 
#   filter(year == 2018) |> 
#   slice_max(order_by = pop, n = 10) |> 
#   pull(entity)
# 
# selcon2 <- df |> 
#   filter(year == 2020) |> 
#   slice_max(order_by = trpc, n = 2) |> 
#   pull(entity)
# 
# selcon3 <- df |> 
#   filter(year == 2020) |> 
#   slice_max(order_by = ghe, n = 2) |> 
#   pull(entity)

select_countries <-  c("United States", "China", "India", "Russia", "Germany", "United Kingdom")

df <- df |> 
  # Add dummy values for missing years of India
  bind_rows(
    df |> filter(entity == "India" & year > 2017) |> 
      mutate(year = 2019),

    df |> filter(entity == "India" & year > 2017) |> 
      mutate(year = 2020)

  ) |> 
  mutate(
    select_var = if_else(
      entity %in% select_countries,
      entity,
      "ZZZ"
    )
  )


# sel_cons <- df |> 
#   filter(year == 2018) |> 
#   filter(!is.na(code)) |>
#   filter(entity != "World") |> 
#   slice_max(order_by = pop, n = 10) |> 
#   pull(entity)
# 

# df |> 
#   filter(year < 2021) |> 
#   filter(entity %in% sel_cons) |> 
#   ggplot(
#     mapping = aes(
#       x = year,
#       y = ghe / trpc,
#       colour = entity
#     )
#   ) +
#   geom_line() +
#   geom_text(
#     data = df |> filter(year == 2020 & entity %in% sel_cons),
#     mapping = aes(
#       x = 2020.2,
#       y = ghe / trpc,
#       colour = entity,
#       label = entity
#     ),
#     check_overlap = T
#   ) +
#   scale_y_continuous(limits = c(0,0.5)) +
#   theme(legend.position = "none")
# 
# 
# df |> 
#   ggplot(
#     mapping = aes(
#       x = ghe/trpc,
#       y = as_factor(year)
#     )
#   ) +
#   geom_violin() +
#   # ggridges::geom_density_ridges(
#   #   alpha = 0.2
#   # ) +
#   geom_boxplot(
#     width = 0.4
#   ) +
#   scale_x_continuous(limits = c(0,0.5)) +
#   theme_minimal()
# 
# df |> 
#   filter(entity %in% sel_cons) |> 
#   ggplot(aes(year, ghe, colour = entity)) +
#   geom_point() +
#   geom_line()
# 

Visualization Parameters

# Font for titles
font_add_google("Acme",
  family = "title_font"
) 

# Font for the caption
font_add_google("Saira Extra Condensed",
  family = "caption_font"
) 

# Font for plot text
font_add_google(
  "Barlow Condensed", 
  family = "body_font"
)

showtext_auto()

# Colour Palette

# Background Colour
bg_col <- "white"
text_col <- "#2e5075"
text_hil <- "#18304a"

# Base Text Size
bts <- 80

plot_title <- "Tax and Health: Tracking Global Trends"

plot_caption <- "Data: Our World in Data  |  Code & Graphics on GitHub @aditya-dahiya"

Visualization-1

text_hil <- "grey30"
text_col <- "grey20"
mypal <- paletteer::paletteer_d("awtools::spalette")

df |> 
  group_by(year, entity) |> 
  count(sort = T)

g1 <- df |> 
  filter(year <= 2020) |> 
  # filter(code == "USA") |> 
  ggplot(
    mapping = aes(
      x = ghe,
      y = trpc
    ) 
  ) +
  geom_point(
    mapping = aes(
      colour = continent,
      size = pop
    ),
    alpha = 0.7, 
    pch = 19
  ) +
  geom_abline(
    slope = 3,
    intercept = 0,
    colour = text_col
  ) +
  annotate(
    geom = "text",
    x = 3000, y = 9800,
    label = "Slope: line at which 33% tax collected is spent on health-care",
    family = "body_font",
    size = 4,
    hjust = 0,
    colour = text_hil,
    angle = 36.67
  ) +
  geom_text(
    mapping = aes(
      label = paste0("Year: ", as_factor(year)),
      x = 10,
      y = 18000
    ),
    family = "body_font",
    size = 15,
    hjust = 0,
    colour = text_hil
  ) +
  # geom_text(
  #   mapping = aes(
  #     label = label_var
  #   ),
  #   colour = text_hil,
  #   family = "body_font",
  #   nudge_y = -500,
  #   size = 4
  # ) +
  scale_size(range = c(2, 15)) +
  coord_fixed(
    ratio = 1/4
  ) +
  scale_colour_manual(
    values = mypal
  ) +
  scale_x_continuous(
    labels = scales::label_currency(),
    limits = c(10, 6000),
    oob = scales::squish
  ) +
  scale_y_continuous(
    labels = scales::label_currency(),
    limits = c(100, 20000),
    oob = scales::squish
  ) +
  guides(
    size = "none",
    colour = guide_legend(
      title = NULL,
      nrow = 1
    )
  ) +
  labs(
    x = names(rawdf)[4],
    y = names(rawdf)[5],
    title = plot_title,
    caption = plot_caption
  ) +
  theme_classic(
    base_size = 11,
    base_family = "body_font"
  ) +
  theme(
    legend.position = "bottom",
    axis.line = element_line(
      arrow = arrow(length = unit(3, "mm")),
      linewidth = 0.3,
      colour = text_col
    ),
    axis.title = element_text(
      colour = text_col
    ),
    plot.title = element_text(
      hjust = 0.5,
      face = "bold",
      size = 25,
      colour = text_hil
    ),
    plot.caption = element_text(
      colour = text_hil,
      hjust = 0.5
    ),
    legend.text = element_text(
      colour = text_hil,
      size = 15
    )
  )

g2 <- g1 +
  transition_time(year) +
  ease_aes("linear")

anim_save(
  filename = here::here("data_vizs", "owid_govt_health_exp.gif"),
  animation = g2,
  fps = 15,
  duration = 30,
  width = 600,
  height = 600,
  rewind = FALSE,
  end_pause = 20
)

Visualization 2 (the ratio over time)

mypal <- c(paletteer::paletteer_d("khroma::okabeitoblack")[1:6], "grey80")
  
mypal |> seecolor::print_color()

bts = 14

g <- df |> 
  ggplot(
    mapping = aes(
      x = year,
      y = ghe
    )
  ) +
 
  annotate(
    geom = "text",
    x = 2000.2,
    y = 6400,
    hjust = 0, vjust = 1,
    family = "body_font",
    colour = text_hil,
    label = "Domestic general government\nhealth expenditure\nper capita, PPP\n(current international $)",
    lineheight = 1.2
  ) +
  
  annotate(
    geom = "text",
    x = 2006.5,
    y = 1950,
    hjust = 0, 
    vjust = 1,
    family = "body_font",
    colour = text_hil,
    label = str_wrap("Each line represents a country. Richer countries are spending increasingly more on health-care. The gap is widening.", 45)
  ) +
  
  
  geom_line(
    mapping = aes(
      group = entity,
      colour = select_var,
      alpha = entity %in% select_countries,
      linewidth = entity %in% select_countries
    )
  ) +
 
  geom_text(
    data = df |> filter(entity %in% select_countries),
    mapping = aes(
      label = entity,
      colour = select_var
    ),
    hjust = 0.5,
    family = "body_font",
    nudge_y = -200
  ) +
  
  ggflags::geom_flag(
    data = df |> filter(entity %in% select_countries),
    mapping = aes(
      country = iso_a2
    ),
    size = 8
  ) +
  
  # Scales & Coordinates
  coord_cartesian(
    clip = "off"
  ) +
  scale_linewidth_manual(
    values = c(0.25, 0.75)
  ) +
  scale_alpha_manual(
    values = c(0.1, 0.9)
  ) +
  scale_y_continuous(
    oob = scales::squish,
    expand = expansion(0),
    labels = scales::label_dollar()
  ) +
  scale_x_continuous(
    limits = c(2000, 2020),
    expand = expansion(c(0, 0.05)),
    breaks = seq(2000, 2020, 2)
  ) +
  
  # Labels and Themes
  labs(
    title = "Governments' Rising Health Expenditure in 21st Century",
    caption = plot_caption,
    x = "Year", y = NULL
  ) +
  theme_minimal(
    base_family = "body_font",
    base_size = bts
  ) +
  theme(
    plot.margin = margin(4,10,4,4, "mm"),
    plot.title = element_text(
      size = bts * 1.2, 
      hjust = 0.5,
      margin = margin(0,0,5,0, "mm")
    ),
    plot.subtitle = element_text(
      hjust = 0.5, 
      size = bts * 0.9,
      margin = margin(5,0,0,0, "mm")
    ),
    text = element_text(
      colour = text_hil
    ),
    axis.line = element_line(
      colour = text_hil,
      arrow = arrow(length = unit(3, "mm"))
    ),
    plot.caption = element_text(
      hjust = 0
    ),
    legend.position = "none",
    panel.grid.major = element_line(
      colour = "grey80",
      linetype = 3,
      linewidth = 0.5
    ),
    panel.grid.minor = element_blank(),
    axis.title.x = element_text(hjust = 1)
  )

library(gganimate)

g_anim <- g +
  transition_reveal(year) +
  shadow_mark(
    exclude_layer = c(2,3)
  )

anim_save(
  filename = here::here("data_vizs", "owid_govt_health_exp.gif"),
  animation = g_anim,
  fps = 15,
  duration = 20,
  width = 500,
  height = 500,
  rewind = FALSE,
  end_pause = 50
)

Save the graphic and a thumbnail

ggsave(
  filename = here::here("data_vizs", "owid_govt_health_exp.png"),
  plot = g,
  width = 500,
  height = 500,
  units = "mm",
  bg = bg_col
)


library(magick)
# Saving a thumbnail for the webpage
image_read(here::here("data_vizs", 
                      "owid_govt_health_exp.png")) |> 
  image_resize(geometry = "400") |> 
  image_write(here::here("data_vizs", "thumbnails", 
                         "owid_govt_health_exp.png"))