Exercises

Data

For the exercises, we’ll be using data on global health spending via the TidyTuesday project. There are several datasets available, we’ll focus on health_spending. You can read in the data directly from GitHub.

health_spending <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-04-21/health_spending.csv")

This dataset has the following variables:

variable	class	description
country_name	character	Country or territory name as used by WHO.
iso3_code	character	ISO 3166-1 alpha-3 country code.
year	integer	Year of observation.
indicator_code	character	GHED indicator code. Suffix indicates unit: _che = % of CHE, _usd2023 = constant 2023 US dollars. Indicators: che — Current health expenditure (total, only _usd2023). gghed — Domestic general government health expenditure (GGHE-D). pvtd — Domestic private health expenditure (PVT-D). ext — External health expenditure (EXT).
expenditure_type	character	Type of health expenditure.
value	double	Indicator value in the unit specified.
unit	character	Unit of measurement: % of current health expenditure or constant 2023 US$.

Since we’re focusing on making visualisations, the following code will clean up the raw data and find the continent for each country:

library(tidyverse)
library(countrycode)
clean_data <- health_spending |> 
  filter(str_starts(indicator_code, "che")) |> 
  select(country_name, iso3_code, year, value) |> 
  mutate(continent = countrycode(iso3_code, "iso3c", "continent"))

Exercises

Exercises 1

The following code produces a chart using ggplot2. Edit the code to:

• Change to using facets instead of (or as well as) colours.
• Make sure the year labels can all be read.
• Make sure it saves nicely with a width of 6 inches and a height of 4 inches.
• Increase all font sizes to be larger.
• Further increase the size of the title.

plot_data <- clean_data |>
  filter(continent == "Oceania")

ggplot(
  data = plot_data,
  mapping = aes(x = year, y = value, colour = country_name)
) +
  geom_line()

Exercises 2

The following code produces a chart using ggplot2. Edit the code to:

• Use either ggtext or marquee to ensure the subtitle text wraps onto multiple lines.
• Use either ggtext or marquee to make sure country names are all visible in the face labels.
• Add an annotation to the "Australia" facet.

ggplot(
  data = plot_data,
  mapping = aes(x = year, y = value)
) +
  geom_line() +
  facet_wrap(~country_name) +
  scale_x_continuous(
    guide = guide_axis(check.overlap = TRUE)
  ) +
  labs(
    x = NULL, y = NULL,
    title = "Australia has highest healthcare expenditure in Oceania",
    subtitle = "Health expenditure by country. 2000 to 2023. Values are scaled to US Dollars (2023).",
    caption = "Source: WHO Global Health Expenditure Database"
  ) +
  theme_minimal(base_size = 12) +
  canvas(
    width = 6, height = 4,
    units = "in",
    dpi = 300
  )

Exercises 3

Create your own theme using some of the options and packages we’ve already discussed:

my_theme <- function() {
  # Your theme code goes here
}

Apply your theme:

ggplot(
  data = plot_data,
  mapping = aes(x = year, y = value)
) +
  geom_line() +
  facet_wrap(~country_name, scales = "free_y") +
  scale_x_continuous(
    guide = guide_axis(check.overlap = TRUE)
  ) +
  scale_y_continuous(labels = label_currency(scale = (1/1000000), suffix = "M")) +
  labs(
    x = NULL, y = NULL,
    title = "Australia has highest healthcare expenditure in Oceania",
    subtitle = "Health expenditure by country. 2000 to 2023. Values are scaled to US Dollars (2023).",
    caption = "Source: WHO Global Health Expenditure Database"
  ) +
  my_theme() +
  canvas(
    width = 6, height = 6,
    units = "in",
    dpi = 300
  )