Discrete color schemes • ggPopi

ggpop focuses on population-genomics categorical variables, so the palette API is discrete-only. It follows the tidyplots color-scheme idea: pass a named scheme or hex vector, downsample extra colours, and interpolate when more categories are requested.

Built-in schemes

ggpop_palette(4, "population")
#> [1] "#4E79A7" "#59A14F" "#76B7B2" "#EDC948"
ggpop_palette(8, "admixture")
#> [1] "#4E79A7" "#F28E2B" "#E15759" "#76B7B2" "#EDC948" "#9C755F"
#> [7] "#2F4B7C" "#FF7C43"
ggpop_palette(5, "manhattan")
#> [1] "#4E79A7" "#C4E2F3" "#4E79A7" "#C4E2F3" "#4E79A7"

ggplot scales

df <- data.frame(
  pop = rep(c("PopA", "PopB", "PopC", "PopD"), each = 5),
  pc1 = rnorm(20),
  pc2 = rnorm(20)
)

ggplot2::ggplot(df, ggplot2::aes(pc1, pc2, colour = pop)) +
  ggplot2::geom_point(size = 2) +
  scale_colour_ggpop("population") +
  ggplot2::theme_classic()

Scatter chart. Simulated PC1 values are on the x-axis and simulated PC2 values are on the y-axis, with point colour encoding four population categories through scale_colour_ggpop.

Custom palettes

my_pop_colors <- new_pop_palette(
  c("#4E79A7", "#F28E2B", "#59A14F", "#E15759"),
  name = "my population palette"
)

ggpop_palette(6, my_pop_colors)
#> [1] "#4E79A7" "#B0855C" "#D39132" "#779D47" "#8F8353" "#E15759"