Showcase
In ggfoundry: Shape Foundry & Geom for 'ggplot2'

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(ggfoundry)
library(tibble)
library(forcats)
library(stringr)
suppressPackageStartupMessages(library(dplyr))
library(paletteer)
library(scales)
library(palmerpenguins)

Exhibit a palette

A large collection of palettes are brought together under a single interface by the paletteer package. It's used here to load the Van Gogh palette "Starry Night".

As also rendered in this blog post, a ggfoundry container-set shape may then be used by geom_casting() to display the loaded palette (optionally with associated hex codes).

n <- 5
palette <- "vangogh::StarryNight"

cols <- paletteer_d(palette, n = n)

tibble(x = 1:n, y = 1) |>
  ggplot(aes(x, y, fill = fct_inorder(cols))) +
  geom_casting(shape = "tube", size = 1.1) +
  geom_label(aes(label = cols |> str_remove("FF$")),
             size = 3, vjust = 1.6, fill = "white") +
  annotate(
    "text",
    x = (n + 1) / 2, y = 2,
    label = palette,
    colour = "grey70",
    alpha = 0.8,
    size = 6
  ) +
  scale_fill_manual(values = as.character(cols)) +
  scale_x_continuous(limits = c(0.5, n + 0.5)) +
  scale_y_continuous(limits = c(0, 2.5)) +
  theme_void() +
  theme(legend.position = "none")

Sunflower plot

Using the palette exhibited above, and inspired by this python Stack Overflow answer, sunflower shapes combined with geom_hex() make possible this kind of ggplot.

Each additional petal reflects an increased range in the count values as shown in the legend. And the choice of ggplot2 cut, i.e. cut_number(), cut_interval() or cut_width(), provides flexibility in how these ranges are constructed.

shapes <- shapes_cast() |>
  filter(set == "flower") |>
  pull(shape)

ggplot(diamonds, aes(carat, price)) +
  geom_hex(bins = 10, colour = cols[3]) +
  geom_casting(
    aes(
      shape = cut_number(after_stat(count), 8, dig.lab = 4),
      group = cut_number(after_stat(count), 8)
    ),
    size = 0.12, bins = 10, stat = "binhex", colour = cols[1], fill = cols[4]
  ) +
  scale_shape_manual(values = shapes) +
  scale_y_continuous(labels = label_currency(scale_cut = cut_short_scale())) +
  scale_fill_gradient(
    low = cols[2], high = cols[1],
    labels = label_number(scale_cut = append(cut_short_scale(), 1))
  ) +
  labs(
    title = "Sunflower Plot",
    shape = "Count\nIntervals",
    fill = "Counts", y = "Price", x = "Carat"
  ) +
  theme_bw()

Shapes identified by the data

In the sunflower plot, scale_shape_manual() specifies the desired shapes. Alternatively, the data may already specify their identity as illustrated below using Allison Horst's palmerpenguins dataset and ggfoundry's penguin-set shapes.

count_df <- penguins |> 
  filter(!is.na(body_mass_g)) |> 
  mutate(
    species = str_to_lower(species),
    cut_mass = cut_width(body_mass_g, width = 500, dig.lab = 4)
    ) |> 
  count(species, island, cut_mass) 

count_df |> 
  ggplot(aes(species, cut_mass, fill = species)) +
  geom_casting(aes(shape = species), size = 0.25) +
  geom_text(aes(label = n), size = 3, nudge_y = 0.2, nudge_x = 0.1) +
  scale_discrete_identity(aesthetics = "shape", guide = "legend") +
  facet_wrap(~island, scales = "free_x") +
  labs(
    title = "Palmer Penguins", 
    subtitle = "Counts by Species, Island & Body Mass Ranges",
    shape = NULL, fill = NULL, x = NULL, y = "Body Mass (g)"
    ) +
  theme_bw() +
  theme(
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank()
    ) +
  guides(shape = guide_legend(override.aes = list(size = 8)))