---
title: "Comparing the palette under colour vision deficiency"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Comparing the palette under colour vision deficiency}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 9,
  fig.height = 4,
  dpi = 120
)
```

Roughly 8% of men and 0.5% of women have some form of colour vision deficiency (CVD).
A categorical palette is only useful if its colours remain distinguishable to those viewers.
This vignette shows the ten discrete `poispalette` colours as they appear under normal vision and under the three main types of CVD.

```{r setup}
library(poispalette)
library(ggplot2)
library(colorspace)
```

The discrete palette contains ten named colours.

```{r}
pois_cols()
```

CVD is simulated with `colorspace`, which provides `deutan()`, `protan()`, and `tritan()` to approximate how colours appear under deuteranopia, protanopia, and tritanopia.
The palette is rendered as swatches, one row per vision type.

```{r}
cols <- pois_cols()

sims <- list(
  Normal = identity,
  Deuteranopia = deutan,
  Protanopia = protan,
  Tritanopia = tritan
)

swatches <- do.call(rbind, lapply(names(sims), function(vision) {
  data.frame(
    vision = vision,
    colour = names(cols),
    hex = sims[[vision]](unname(cols)),
    x = seq_along(cols),
    stringsAsFactors = FALSE
  )
}))

swatches$vision <- factor(swatches$vision, levels = names(sims))
swatches$colour <- factor(swatches$colour, levels = names(cols))
```

```{r}
ggplot(swatches, aes(x = x, y = 1, fill = hex)) +
  geom_tile(colour = "grey40", linewidth = 0.3) +
  scale_fill_identity() +
  facet_wrap(~vision, ncol = 1, strip.position = "left") +
  scale_x_continuous(
    breaks = seq_along(cols),
    labels = names(cols),
    position = "top",
    expand = c(0, 0)
  ) +
  labs(x = NULL, y = NULL) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 0, vjust = 0),
    axis.text.y = element_blank(),
    panel.grid = element_blank(),
    strip.text.y.left = element_text(angle = 0),
    legend.position = "none"
  )
```

Under deuteranopia and protanopia the blues, green, and light blue shift toward muddy yellows and blues.
Across all three deficiency types the ten colours remain distinguishable, though pairs such as `blue`/`light blue` are closer than under normal vision and are best reserved for categories that do not need to be told apart at a glance.