ggfoundry

Shape landscape

Yes, there is already a seemingly near-infinite number of shapes out there, the capabilities of which we’ll contrast in a moment:

The shapes most familiar to users of ggplot (some fillable) are described here;
Colourable unicodes and icons like fontawesome extend the options;
ggimage enables the use of whole pictures;
And then there is the Do-It-Yourself approach: Conjuring up grobs (grid graphical objects); perhaps with a sprinkle of trigonometry.

But sometimes you just can’t find what you want. Or manipulate it in the way you would like.

ggfoundry offers arbitrary hand-crafted colourable and fillable shapes for ggplot2.

New shapes may be feature requested via a Github issue.

Foundry process

These artisanal symbols begin life as hand-drawn vector images with two layers: an outline and a fill. Each SVG pair is converted to Cairo graphics format, forged at extreme temperatures into objects of class “Picture”, and finally delicately cast as a gTree representation of the original shape. But not quite back to where we started, because they are now editable.

When cooled and finely burnished, the gTree and all its grob children may then be manipulated by geom_casting() to render the desired ggplot with those special high-end adornments.

Contrast with unicodes

The extensive range of unicodes is a great option. They are colourable and appear in the legend.

library(ggfoundry)
library(tibble)
library(patchwork)

random_walk <- \(x, y, z) cumsum(rnorm(x, mean = y, sd = sqrt(z)))

set.seed(123)

df <- tibble(
  x = rep(1:10, 3),
  y = c(
    random_walk(10, 1, 1),
    random_walk(10, 2, 1.2),
    random_walk(10, 3, 1.3)
  ),
  group = factor(c(rep(1, 10), rep(2, 10), rep(3, 10)))
)

p <- df |>
  ggplot(aes(x, y, shape = group, colour = group, fill = group)) +
  geom_line(show.legend = FALSE) +
  scale_colour_manual(values = c("darkred", "darkblue", "darkgreen")) +
  scale_fill_manual(values = c("pink", "lightgreen", "skyblue")) +
  theme_bw() +
  theme(plot.subtitle = element_text(size = 10))

p1 <- p +
  geom_casting(size = 0.15) +
  annotate("label", x = 2.5, y = 22, label = "Baby\ngeoms") +
  scale_shape_manual(values = c("violin", "box", "dendro")) +
  labs(
    title = "ggfoundry",
    subtitle = "On-request colourable fillable shapes"
    )

p2 <- p +
  geom_point(size = 4) +
  scale_shape_manual(values = c("\u25BC","\u25CF","\u25B2")) +
  labs(
    title = "geom_point with unicodes",
    subtitle = "Range of colourable-only symbols"
    )

p1 + p2 + plot_layout(guides = "collect", axes = "collect")

Contrast with ggtext / icons

Icons are also a great option, e.g. for brands. One way to use these as ggplot points is via the showtext and ggtext packages. They too are colourable, but the fill is for the surrounding label rather than the symbol itself. The legend reflects the use of the richtext geom, i.e. shows letters.

library(showtext)
library(ggtext)

font_add("fa-solid", "Font_Awesome_6_Brands-Regular-400.otf")

showtext_auto()

p2 <- p +
  geom_richtext(
    aes(label = "<span style='font-family: \"fa-solid\"'>&#xf16c;</span>"),
    size = 5, label.colour = NA,
    ) +
  labs(
    title = "ggtext with icons",
    subtitle = paste0(
      "Colourable icons; label fill; ", 
      "text legend"
      )
    )

p1 + p2 + plot_layout(guides = "collect", axes = "collect")


showtext_auto(enable = FALSE)

Contrast with ggimage

ggimage is a great option for full images, e.g. png files. Country flags, company logos and sports team badges are good example use-cases, as rendering the full image as-is is often the desired outcome.

library(ggimage)

df <- tribble(
  ~x, ~y,
  5, 5,
  6, 6,
  7, 7,
  8, 8
)

p <- df |>
  ggplot(aes(x, y, shape = factor(x), fill = factor(x))) +
  geom_col() +
  scale_fill_viridis_d() +
  scale_y_continuous(limits = c(NA, 9)) +
  theme_bw() +
  theme(plot.subtitle = element_text(size = 10))

p1 <- p +
  geom_casting(size = 0.3, aes(colour = factor(x))) +
  geom_text(aes(label = x, colour = factor(x)), show.legend = FALSE) +
  annotate("label", x = 5.5, y = 8, label = "Polygons") +
  scale_shape_manual(values = c("pentagon", "hexagon", "heptagon", "octagon")) +
  scale_colour_viridis_d(direction = -1) +
  labs(
    title = "ggfoundry",
    subtitle = "Arbitrary hand-crafted fillable shapes"
    )

p2 <- p +
  geom_flag(size = 0.1, image = c("DE", "FR", "CA", "US")) +
  labs(
    title = "ggimage",
    subtitle = "Any as-is whole picture, e.g. png"
    )

p1 + p2 + plot_layout(guides = "collect", axes = "collect")

Contrast with cunning strategies

Where existing symbols are not fillable, there are possible strategies to achieve a similar effect using only colour and compromising a little on the legend:

Have one larger layer with a coloured symbol. Then superimpose a smaller second layer with a differently-coloured symbol.
Use photoshop-style special effects provided by the ggfx package, e.g. adding a differently-coloured outer-glow.

library(ggfx)

df <- tribble(
  ~x, ~y, ~label,
  5, 5, "+",
  6, 6, "x"
)

p <- df |>
  ggplot(aes(x, y, shape = factor(x), fill = factor(x))) +
  geom_col(alpha = 0.5) +
  scale_y_continuous(limits = c(NA, 7)) +
  scale_fill_viridis_d() +
  scale_colour_viridis_d(direction = -1) +
  theme_bw() +
  theme(plot.subtitle = element_text(size = 9))

p1 <- p +
  geom_casting(size = 0.7, aes(colour = factor(x))) +
  geom_text(aes(label = x, colour = factor(x)), show.legend = FALSE) +
  annotate("label", x = 5, y = 6.5, label = "Crosses") +
  scale_shape_manual(values = c("cross2", "cross1")) +
  labs(
    title = "ggfoundry",
    subtitle = "Hand-crafted fillable shapes"
    )

p2 <- p +
  geom_text(aes(label = label), colour = "#22A884", fontface = "bold",
            size = 24, show.legend = FALSE) +
  geom_text(aes(label = label, colour = factor(x)), 
            size = 20, show.legend = FALSE) +
  geom_text(aes(label = x), colour = "grey60", nudge_y = -0.15,
            size = 3, show.legend = FALSE) +
  labs(
    title = "Cunning strategy 1",
    subtitle = "Superimposed layers"
    )

p3 <- p +
  with_outer_glow(geom_text(aes(label = label, colour = factor(x)),
    size = 22, show.legend = FALSE, 
  ), sigma = 0, expand = 8, colour = "#22A884") +
  geom_text(aes(label = x), colour = "grey60", nudge_y = -0.15,
            size = 3, show.legend = FALSE) +
  labs(
    title = "Cunning strategy 2",
    subtitle = "Photoshop-style outer glow"
  )

p1 + p2 + p3 + plot_layout(guides = "collect", axes = "collect")

Contrast with DIY

Making your own grob with grid graphics is a further option. Then use ggpp and geom_grob() to add the appropriate layer. A circle is used here as a very basic example.

library(ggpp)
library(grid)

df <- tibble(
  x = 5:6, y = 5:6,
  grob = c(
    list(circleGrob(r = 0.7, gp = gpar(
      col = "#fde725",
      fill = "#440154",
      lwd = 4
    ))),
    list(circleGrob(r = 0.7, gp = gpar(
      col = "#440154",
      fill = "#fde725",
      lwd = 4
    )))
  )
)

p <- df |>
  ggplot(aes(x, y, shape = factor(x), fill = factor(x))) +
  geom_col(alpha = 0.5) +
  scale_y_continuous(limits = c(NA, 7)) +
  scale_fill_viridis_d() +
  scale_colour_viridis_d(direction = -1) +
  theme_bw()

p1 <- p +
  geom_casting(size = 0.7, aes(colour = factor(x))) +
  annotate("label", x = 5.5, y = 8, label = "Circles") +
  scale_shape_manual(values = c("circleL", "circleR")) +
  labs(
    title = "ggfoundry",
    subtitle = "Hand-crafted fillable shapes"
    )

p2 <- p +
  geom_grob(aes(x, y, label = grob)) +
  labs(
    title = "Grob DIY",
    subtitle = "Lower-level graphics"
  )

p1 + p2 + plot_layout(guides = "collect", axes = "collect")

Available shapes

In summary, ggfoundry may be the destination of “last resort”!

After travelling the mountains, seas and forests of the world in search of that elusive shape (or small set), a hand-made grob may be the fillable “Holy Grail” sought via a Github issue.

These modest starter sets are included with the package. You can “mix and match” shapes from different sets; the “set” is for grouping shapes in the documentation and for use in shapes_cast() to filter for the desired shapes.

Acknowledgements

Without the pivotal grConvert (Potter 2024) and grImport2 (Potter and Murrell 2023) packages, the foundry process would not have been viable. And only thanks to the work behind ggplot2 (Wickham 2016), may the shapes cast be so beautifully visualised.

Potter, Simon. 2024. “grConvert: Converting Vector Graphics.” https://github.com/sjp/grConvert.

Potter, Simon, and Paul Murrell. 2023. “grImport2: Importing ’SVG’ Graphics.” https://CRAN.R-project.org/package=grImport2.

Wickham, Hadley. 2016. “Ggplot2: Elegant Graphics for Data Analysis.” https://ggplot2.tidyverse.org.