Gallery • ggtaichi

A tour of what geom_taichi() can look like. Every plot below is a single layer call plus ordinary ggplot2.

One legend, two fish

When the two sources share units, shared_legend = TRUE paints both fish with one palette on one scale, so the two halves of every glyph can be read against a single legend. The bundled cafes_tg data (synthetic espresso vs. matcha orders) is made for this:

ggplot(cafes_tg, aes(x = week, y = neighbourhood)) +
  geom_taichi(yin = matcha, yang = espresso,
              shared_legend = TRUE,
              yin_name = "orders / 100 customers") +
  remove_padding() +
  theme_taichi() +
  ggtitle("Espresso (yang) vs matcha (yin), one shared scale")

A 12-week by 8-neighbourhood grid of taichi diagrams where espresso and matcha orders share one grey fill scale and a single legend.

Two palettes, shared limits

Keep each source’s own palette but align the limits, so equal values carry equal ink:

ggplot(cafes_tg, aes(x = week, y = neighbourhood)) +
  geom_taichi(yin = matcha,   yin_name = "Matcha",
              yin_colors  = c("#deebf7", "#3182bd", "#08306b"),
              yang = espresso, yang_name = "Espresso",
              yang_colors = c("#fee6ce", "#e6550d", "#7f2704"),
              shared_limits = TRUE) +
  remove_padding() +
  theme_taichi()

The same espresso versus matcha grid with a blue palette for matcha and an orange palette for espresso on identical scale limits.

Classic eyes, data-driven eyes

d <- data.frame(x = 1:4, y = 1, yin = c(2, 4, 6, 8), yang = c(8, 6, 4, 2),
                pull = c(30, 5, 18, 45))

ggplot(d, aes(x, y)) +
  geom_taichi(yin = yin, yang = yang, eyes = TRUE,
              yin_eye_size = pull, yang_eye_size = 0.12,
              limits = c(0, 10)) +
  coord_fixed() +
  theme_taichi() +
  ggtitle("Eye size as a fifth channel")

Left, a row of classic taichi symbols with white and black eyes; right, the same symbols with eye sizes driven by data.

A turning grid

Rotation can be pure annotation or a data channel — here each glyph’s angle encodes its column:

grid16 <- expand.grid(x = 1:4, y = 1:4)
grid16$yin <- seq(1, 10, length.out = 16)
grid16$yang <- rev(grid16$yin)
grid16$turn <- grid16$x * 22.5

ggplot(grid16, aes(x, y)) +
  geom_taichi(yin = yin, yang = yang, angle = turn, eyes = TRUE,
              limits = c(0, 10)) +
  coord_fixed() +
  theme_taichi()

A four-by-four grid of taichi diagrams whose rotation angle increases along the x direction.

Categorical fills

d9 <- expand.grid(x = 1:3, y = 1:3)
d9$roast  <- factor(c("light", "medium", "dark")[(d9$x + d9$y) %% 3 + 1])
d9$origin <- factor(c("blend", "single")[(d9$x * d9$y) %% 2 + 1])

ggplot(d9, aes(x, y)) +
  geom_taichi(yin = roast, yang = origin) +
  coord_fixed() +
  theme_taichi()

A three-by-three grid of taichi diagrams filled by discrete categories on both fish.

Texture at scale

Dense grids stop being symbols you read one by one and become a texture of two interleaved fields — still useful for spotting bands and regime changes:

ggplot(subset(states_tg, state %in% c("New York", "Texas")),
       aes(x = week, y = category)) +
  geom_taichi(yin = Twitter, yang = Google) +
  facet_wrap(~ state, ncol = 1) +
  remove_padding() +
  theme_taichi() +
  ggtitle("30 weeks as texture")

A dense 30-week by 9-category grid of small taichi diagrams for four states, read as an overall texture rather than glyph by glyph.

Bring your own scales

yin_scale / yang_scale accept any fill scale, and the exported geom_yin_fish() / geom_yang_fish() let you assemble everything by hand (your scales, your ggnewscale stacking):

ggplot(d, aes(x, y)) +
  geom_taichi(yin = yin, yang = yang,
              yin_scale = scale_fill_viridis_c,
              yang_scale = scale_fill_viridis_c(name = "yang", option = "magma")) +
  coord_fixed() +
  theme_taichi()

A row of taichi diagrams using viridis palettes supplied as custom scales.