Hello, Quarto

library(tidyverse)
library(palmerpenguins)

Meet Quarto

Quarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see https://quarto.org.

Meet the penguins

The penguins data from the palmerpenguins package contains size measurements for 344 penguins from three species observed on three islands in the Palmer Archipelago, Antarctica.

The plot below shows the relationship between flipper and bill lengths of these penguins.

ggplot(penguins, 
       aes(x = flipper_length_mm, y = bill_length_mm)) +
  geom_point(aes(color = species, shape = species)) +
  scale_color_manual(values = c("darkorange","purple","cyan4")) +
  labs(
    title = "Flipper and bill length",
    subtitle = "Dimensions for penguins at Palmer Station LTER",
    x = "Flipper length (mm)", y = "Bill length (mm)",
    color = "Penguin species", shape = "Penguin species"
  ) +
  theme_minimal()

ggplot(mpg, aes(x = hwy, y = cty, color = cyl)) +
  geom_point(alpha = 0.5, size = 2) +
  scale_color_viridis_c() +
  theme_minimal()

Figure 1

Figure 1 shows a positive, strong, and linear relationship between the city and highway mileage of these cars.

The plots in Figure 2 show the relationship between city and highway mileage for 38 popular models of cars. In Figure 2 (a) the points are colored by the number of cylinders while in Figure 2 (b) the points are colored by engine displacement.

ggplot(mpg, aes(x = hwy, y = cty, color = cyl)) +
  geom_point(alpha = 0.5, size = 2) +
  scale_color_viridis_c() +
  theme_minimal()

ggplot(mpg, aes(x = hwy, y = cty, color = displ)) +
  geom_point(alpha = 0.5, size = 2) +
  scale_color_viridis_c(option = "E") +
  theme_minimal()

(a) Color by number of cylinders

(b) Color by engine displacement, in liters

Figure 2: City and highway mileage for 38 popular models of cars.

library(donutsk)

# Create an example data set
n <- 40
set.seed(2021)
df <- dplyr::tibble(
 lvl1 = sample(LETTERS[1:5], n, TRUE),
 lvl2 = sample(LETTERS[6:24], n, TRUE),
 value = sample(1:20, n, TRUE),
 highlight_ext = sample(c(FALSE,TRUE), n, TRUE, c(.9, .1))) |>
 dplyr::mutate(highlight_int = dplyr::if_else(lvl1 == "A",TRUE,FALSE))

# Doubled donut with advanced labeling 
dplyr::group_by(df, lvl1, lvl2, highlight_ext, highlight_int) |>
  dplyr::summarise(value = sum(value), .groups = "drop") |>
  # Pack values effectively 
  packing(value, lvl1) |> 
  ggplot(aes(value = value, fill = lvl1)) +
  # The donutsk visualization functions
  geom_donut_int(aes(highlight = highlight_int), alpha=.5, r_int = .25) +
  geom_label_int(aes(label = "Sum {fill}:\n{.sum} ({scales::percent(.prc)})"),
                 alpha = .6, col = "white", r=1.2) +
  geom_donut_ext(aes(opacity = lvl2, highlight = highlight_ext)) +
  geom_label_ext(aes(label = paste0(lvl2, ": {scales::percent(.prc_grp)}")),
                 show.legend = FALSE, col="white", 
                 layout = tv(thinner = TRUE)) +
  geom_pin(size=.5, linewidth=.1, show.legend = FALSE, cut = .25,
           layout = tv(thinner = TRUE)) +
  # Additional appearance settings
  scale_fill_viridis_d(option = "inferno", begin = .1, end = .7) +
  theme_void() + 
  theme(legend.position = "none")  + 
  coord_polar(theta = "y")