---
title: "Polychrome: Color Deficits"
author: "Kevin R. Coombes"
data: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Color Deficits}
  %\VignetteKeywords{OOMPA,Polychrome,Color Palettes,Palettes}
  %\VignetteDepends{Polychrome}
  %\VignettePackage{Polychrome}
  %\VignetteEngine{knitr::rmarkdown}
---

```{r opts, echo=FALSE}
knitr::opts_chunk$set(fig.width=8, fig.height=5)
```
# Introduction

There are three main forms of color-deficient vision, depending on
which of the three types of cones (red, green, or blue) are missing
or defective in the eyes of an individual.

1. Deuteranopes (about 6% of men) are red-blind, and so they have
   trouble distinguishing red from green.
2. Protanopes (about 2% of men) are green-blind, and they also have
   trouble distinguishing red from green.
3. Tritanopes (less than 1% of men) are blue-blind, and they have
   trouble distinguishing blue from yellow.

<tt>Polychrome</tt> includes facilities to simulate different forms of
color deficit and to create palettes that can be used by individuals
with color deficient vision. This vignette describes those facilities.

# Red-Green Color Maps
We start by loading the package.
```{r pkgs}
library(Polychrome)
```
Next, we create the common "standard" color map used by 
bioinformaticians to draw heatmaps. 
```{r rg, fig.cap="Common red-green palette."}
rg <- colorRampPalette(c("red", "black", "green"))(64)
image(matrix(1:64), col=rg)
```

Now, we convert it to what would be seen by a deuteranope.
```{r deut, fig.cap="Deuteranopes' view of the common red-green palette."}
rg.deut <- colorDeficit(rg, "deut")
image(matrix(1:64), col=rg.deut)
```

We can also convert it to what would be seen by a protanope...
```{r prot, fig.cap="Protanopes' view of the common red-green palette."}
rg.prot <- colorDeficit(rg, "prot")
image(matrix(1:64), col=rg.prot)
```

... or a tritanope.
```{r trit, fig.cap="Tritanopes' view of the common red-green palette."}
rg.trit <- colorDeficit(rg, "trit")
image(matrix(1:64), col=rg.trit)
```

# Large Qualitative Palettes
We repeat those operations for the 26-color alphabet palette from
<tt>Polychrome</tt>. 

```{r alfa, fig.cap = "The 26-color alphabet palette."}
alfa <- alphabet.colors(26)
swatch(alfa)
```

```{r adeut, fig.cap = "The 26-color alphabet palette, as seen by a deuteranope."}
alfa.deut <- colorDeficit(alfa, "deut")
swatch(alfa.deut)
```

```{r aprot, fig.cap = "The 26-color alphabet palette, as seen by a protanope."}
alfa.prot <- colorDeficit(alfa, "prot")
swatch(alfa.prot)
```

```{r atrit, fig.cap = "The 26-color alphabet palette, as seen by a tritanope."}
alfa.trit <- colorDeficit(alfa, "trit")
swatch(alfa.trit)
```

# Creating New Palettes
We can also use the <tt>createPalette</tt> function to create new palettes
designed for individuals with color deficits. This operation requires us to
use the <tt>target</tt> option of the function. The default value of "normal"
assumes that the target audience consists of individuals with normal color
vision.

```{r cp1, fig.cap="A palette designed for deuteranopes."}
cp <- createPalette(20, "#111111", target="deuteranope")
names(cp) <- colorNames(cp)
swatch(cp)
```


```{r cp2, fig.cap = "A palette designed for protanopes."}
cp <- createPalette(20, "#111111", target="protanope")
names(cp) <- colorNames(cp)
swatch(cp)
```


```{r cp3, fig.cap = "A palette designed for tritanopes."}
cp <- createPalette(20, "#111111", target="tritanope")
names(cp) <- colorNames(cp)
swatch(cp)
```


## A Small Color-Safe Palette

Here we try to build a small palette that has at least some chance of being
useful for people with any of the three forms of color-blindness. First, we
get a slightly larger starting palette, and convert it to simulate each of
the three forms of color deficits.
```{r dists}
p34 <- palette36.colors(36)[3:36]
names(p34) <- colorNames(p34)
p34.deut <- colorDeficit(p34, "deut")
p34.prot <- colorDeficit(p34, "prot")
p34.trit <- colorDeficit(p34, "trit")
```

Next, we reorder the palettes by distinguishability.
```{r shift}
shift <- function(i, k=34) c(i, 1:(i-1), (1+i):k)
co <- shift(13)
pd <- computeDistances(p34.deut[co])
pp <- computeDistances(p34.prot[co])
pt <- computeDistances(p34.trit[co])
```

Now we use the ranks from this ordering to compute a distinguishability score.
```{r score}
rd <- rank(pd)[order(names(pd))]
rp <- rank(pd)[order(names(pp))]
rt <- rank(pd)[order(names(pt))]
score <- 2*rd + 1.5*rp + rt
```

Now we show the top ten colors (ranked by this score) , adjusting for different
color deficits.
```{r, fig.width=8, fig.height=7, fig.cap="Color safe palette."}
x <- p34[names(rev(sort(score)))][1:10]
y <- colorDeficit(x, "deut")
z <- colorDeficit(x, "prot")
w <- colorDeficit(x, "trit")

opar <- par(mfrow=c(2,2))
swatch(x, main="Normal")
swatch(y, main="Deuteranope")
swatch(z, main="Protanope")
swatch(w, main="Tritanope")
```

```{r, echo=FALSE}
par(opar)
```

# Conclusion
We have illustrated how to convert arbitrary palettes into versions that
simulate different forms of color deficits. We have also shown how to
create palettes specifically for individuals with different color deficits.
Finally, we have created a ten-color palette that has a reasonable chance
of working simultaneously for individuals with any of the three most common
form of color deficits.