.large[The use of colour in graphics]

class: left, middle, inverse, title-slide

# .large[The use of colour in graphics]
## .large[A journey through the body & mind to the screen]
### <span style="font-size: 90%;">Nicholas Tierney, Monash University</span>
### <div class="medium">
Monash Data Fluency, June 26 <br><br> <strong>bit.ly/njt-monash-colour</strong> <br><br> <strong>nj_tierney</strong>
</div>

---

layout: true
<div class="my-footer"><span>bit.ly/njt-monash-colour • @nj_tierney</span></div>

---

# Your turn: What makes this graphic good, and bad?

---

# BoM 😢

---

# Viridis 😄

---

# Jet?

???

Sometimes the conversation ends there, and you provide them a few graphics that look better. Like this.

---
# How do we describe what is better/worse about them?

---

# Describing why and how one is better

.left-code[
<img src="imgs/bom-heat-map.png" width="418" style="display: block; margin: auto;" />
]

.right-plot[
- Vocabulary to describe **why**
- Not just: "It's not colourblind safe", "The colours are wrong" "Type of colourscale is wrong".
- I want to explain the other perceptual properties
]

???

I guess the other parts of my motivation for this talk come from seeing a lot of people using bad/sub optimal colourscales.

There is great tooling in R to do the following:

---

# We're going in for a deep dive here, folks

---
# Goals of this talk

- What is colour?
- How do we perceive colour?
- What is colourblindness?
- What is a vocabulary around colour?
- How do I use colour in my plot?
- How do I assess colour?
- How do I create new colour palettes?

???

This made sense to me, I know what colourblindness is, and I know what perceptually uniform means.

But I also have an honours degree in Psychological Science. My honours project studied the human visual system, in an area called psychophysics. I didn’t study colour, but we did need to learn about the visual system in neuropsychology.

# What is colour?

Disclaimer: This is a _brief_ introduction into a **complex** topic

This next section is inspired by [Calder Hansen's ](https://www.youtube.com/channel/UC7zwxzcgdrjnFkRrLnfN_CA) [The Science of Color Perception](https://www.youtube.com/watch?v=poL7nDmqjmk)

---
# Colour: Waves of light, perceived.

Just as there are soundwaves that you can hear 🔉 👂

There are lightwaves that we see 🚥 👀

???

This combines physics, biology, and a bit of philosophy.

---
# Physics of Colour

These lightwaves come in different shapes - different wavelengths:

---
# The visible spectrum

???

- At this end we have the longest wavelengths
- At this end we have the shortest wavelengths

---
# Physics of Colour (More than we can see)

> From https://commons.wikimedia.org/wiki/File:EM_spectrum.svg

<a rel="license" href="http://creativecommons.org/licenses/by-sa/3.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/3.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/3.0/">Creative Commons Attribution-ShareAlike 3.0 Unported License</a>.

???

- Note that this is just a tiny slice of the light spectrum
- We just happen to be able to see this range of colours
- But How?

---
# Physics of Colour

???

- Lightwaves enter our eyes, and we have the perception of colour.
  - Shorter wavelengths = "purple" / "blue
  - longer wavelengths = "red"
  
- As light enters the eye, it hits cells that are sensitive to light. 
- These cells are called rods and cones. 
- The cones are the bits that allow us to see colour

---
# Biology of colour: cones

<img src="imgs/cone-family-trim.png" width="60%" style="display: block; margin: auto;" />
  
???
  
- If we get only one type of colour entering in the eye, we get one colour.
- This is because of these three cones:
- Colours are brighter when there is more of the light
- most colours are a combination of multiple wave lengths

---
# Biology of colour: cone sensitivty to light

.small[Image inspired by [figure of responsivity of human eye from Wikipedia](https://en.wikipedia.org/wiki/File:Cone-fundamentals-with-srgb-spectrum.svg), data extracted from [Colour & Vision Research laboratory at UCL's](http://cvrl.ucl.ac.uk) section on [cones](http://cvrl.ucl.ac.uk/cones.htm).]

---
# How do we see colour? Blue?

???

So what happens when we are exposed to a lot of these longer wavelengths of "red" light?

- Long cones react
- Medium cones not so much
- S cones not at all

That information is then taken in to your brain and then you have the sensation of "red".

---
# But I don't just see red and green and blue?

Well, kinda?

---
# But I don't just see red and green and blue?

Well, kinda?

???

- Colour is made up of the _combination_ of light entering our eye.
- Red could be some of ..., and some of ...
- Orange could be
- White could be all colours, or it could just be

There are actually many different combinations of light waves that can get us to see different colours.

This means that colour is not just a single light waves, or a combination of many, but it is our perception of light

---
# Colourblindness

- What happens if someone doesn't have some of these cones?

- Or one of the cones isn't as responsive?

- This is what we call **colourblindness** and **colourweakness**

- Depending on which cones are missing, this means some sets of colours are indistinguishable from one another.

- It affects up to 10% of males of European descent, and 1 in 200 women.

---
# Full colour vision

---
# No L cone = Protonopia (Less "red")

---
# No M cone = Deutanopia (Less "green")

---
# No S cone = Tritanopia (Less "Blue")

---
# Colourblindness impacts everyday design

Without those cones, some colours look the same

This is why traffic lights have position markings, instead of just the same position changing colour.

---
# Normal Traffic Light

---
# Traffic Light (Protan)

---
# Traffic Light (Deutan)

---
# Traffic Light (Tritan)

---
# Some of these colours look very similar!

???

_gif of good traffic light for different vision_

_gif of bad traffic light with different vision_

---
# Colourblindness key points

- We perceive colour using the cones in our eyes
- Some people don't have certain cones, or reduced capacity in those
- This means some colours cannot be distinguished by those with colourblindness
- We need to be careful how we present colour, and what colours we present.

---
# Tools for testing how well your plot stands up to colourblindness?

- We can transform colours into approximations of colourblindness
- Many tools exist for exploring colourblindness
- We'll mostly be exploring the R package, `colorspace`

---
# Tools for testing how well your plot stands up to colourblindness?

- `colorspace`: `protan()`, `deutan()`, `tritan()` transform colours.

```r
# Just rainbow
rainbow(5) 
```

```
## [1] "#FF0000" "#CCFF00" "#00FF66" "#0066FF" "#CC00FF"
```

```r
# Rainbow into protan
rainbow(5) %>% protan()
```

```
## [1] "#261D00" "#FFDF00" "#F7D25F" "#3769FF" "#0030FF"
```

---
# How to test how well your plot stands up to colourblindness?

```r
rainbow(19) %>% protan() %>% swatchplot()
```

---
# How to test how well your plot stands up to colourblindness?

.left-code[
The `prismatic` package provides `check_color_blindness`:

```r
rainbow(19) %>% 
  check_color_blindness()
```
]

.right-plot[
<img src="figures/prismatic-check-out-1.png" width="100%" style="display: block; margin: auto;" />
]

---
# What if your plot is an image already?

- `colorspace` provides a "Colour Vision Deficiency (CVD) Emulator" - `cvd_emulator`.
- This takes in a JPG / PNG
- Let's explore what this original image from BoM looks like:

```r
cvd_emulator("imgs/bom-heat-map.png")
```

---

# Original image

---

# Protan image (No Red Cone)

---

# Deutan image (No Green Cone)

---

# Tritan image (No Blue Cone)

---

# Greyscale image

- What is a vocabulary around colour?
- How do I assess colour?
- How do I create new colour palettes?

---
# Describing colour in graphics

.pull-left[
<img src="imgs/desaturate_bom-heat-map.png" width="418" style="display: block; margin: auto;" />

]

.pull-right[
- Something about this grayscale plot was interesting
- It looped back on itself in places
- That was the colour ... saturation?
- Colour...brightness?
- Colour...depth?
]

---
# Describing colour in graphics

- There is a wide vocab that we can use to discuss colour. 
- We can describe any colour using the different values of red, green, and blue. 
- Remember the red, green, and blue cones?
  
---
# Describing colour in graphics

---
# Describing colour in graphics

.pull-left[
<img src="imgs/bom-heat-map.png" width="418" style="display: block; margin: auto;" />

]

.pull-right[
- But in looking at the BoM plot, it doesn't really help us to say: "We need 50% more red, and reduce the intensity of green".
- It is hard for us to really imagine and predict without a lot of training
- Is there another way to describe the colour?
]

---
# Yes: Hue, Chroma, Luminance

???

These go by other names, and are just one of a set of many different colour representations (HSV, CIELAB, … ), and the literature on colourspace is wide and vast. These allow us to explore and explain colour in a way that makes sense to human.

These are human readable ways to describe colour, but ultimately get expressed into terms of RGB.

---
# Colorspace

.pull-left[
<img src="imgs/colorspace-website.png" width="774" style="display: block; margin: auto;" />

]

.pull-right[

]

---

# Describing our previous maps in terms of hue, chroma, luminance

- Using `colorspace` to explore colour palettes
- We can explore some colour palettes using HCL, with the `specplot` function from `colorspace`. 
- We can take our palette and plot it along these dimensions

---

# HCL of BoM plot palette

???

I have the nice colours used in the Australian heat data here, which we can view with `swatchplot` from `colorspace`, or `show_cols` from `scales`

---

# Design decisions when choosing colour

.pull-left[
<img src="figures/specplot-bom-standard-pal-again-1.png" width="936" style="display: block; margin: auto;" />
]

.pull-right[
- Colours change hue a lot
- Expectation: As temperature increased, other parameters increase?
- Expectation: one unit step in the palette produces a constant perceptual change in colour (perceptually uniform).
- Doesn't seem to be the case
- Wrong type of palette?
]

---
# Types of palettes

- Qualitative: For coding categorical information, i.e., where no particular ordering of categories is available and every color should receive the same perceptual weight.

- Sequential: For coding ordered/numeric information, i.e., where colors go from high to low (or vice versa).

- Diverging: Designed for coding numeric information around a central neutral value, i.e., where colors diverge from neutral to two extremes.

.small[(From the R help file on colour palettes)]

---
# Qualitative: Every colour has same weight

---
# Sequential: Colours go low - high

---
# Sequential: Multiple hues

---
# Diverging: Neutral value to two extremes

---
# Improving the BoM plot

.pull-left[

<img src="figures/specplot-bom-standard-pal-again-again-1.png" width="936" style="display: block; margin: auto;" />
]

.pull-right[
- Multi hue
- Perceptually uniform
- Colourblind safe?
]

---
# Exploring colourblindness in BoM palettes

.pull-left[
BoM

<img src="figures/cvd-bom-standard-pal-again-again-1.png" width="936" style="display: block; margin: auto;" />
]

.pull-right[
Viridis

<img src="figures/cvd-bom-viridis-pal-again-again-1.png" width="936" style="display: block; margin: auto;" />
]

---
# Improving the BoM plot

.pull-left[
<img src="figures/specplot-bom-standard-pal-again-again2-1.png" width="936" style="display: block; margin: auto;" />
]

.pull-right[
<img src="figures/specplot-bom-viridis-pal-again-again2-1.png" width="936" style="display: block; margin: auto;" />
]

---
# Improving the BoM plot

.pull-left[
<img src="figures/specplot-bom-viridis-pal-again-again-1.png" width="936" style="display: block; margin: auto;" />
]

.pull-right[
- Multiple hues
- Luminance places importance on high/low values
]

---
# Creating your own colour palette with `colorspace`

1. Decide what type of palette you want (Sequential, Diverging, etc)
2. Decide what colours you want to go through

---
### `sequential_hcl(n = 20, h = 300)`

---
# Aside: the hue is a colour wheel

---
### `sequential_hcl(n = 20, h = c(360, 120))`

???

Use `sequential_hcl` and provide two hues

---
# Preview colours with `demoplot(type = "map")`

---
# Preview colours with `demoplot(type = "heatmap")`

---
# Check colourblindness `protan` and use demoplot

---
# Check colourblindness `protan` and use demoplot (compare to rainbow)

---
# Exploring palettes: demo

```r
hcl_palettes("qualitative", plot = TRUE)
hcl_palettes("sequential", plot = TRUE)
hcl_palettes("diverging", plot = TRUE)
```

---
# Tools for exploring colourblindness:

- Using the hex/colour codes
  - Colorspace: `protan`/`deutan`/`tritan`
  - prismatic: `check_color_blindness`
- For a ggplot object
  - colorblindr: `cvd_grid`
- For an image
  - colorspace: `cvd_emulator`

---
# Tools for creating good colours

- Colorspace: 
 - `qualitative_hcl`
 - `sequential_hcl`
 - `diverging_hcl`
- Pick the hue/chroma/luminance you want to travel through

---
# A workflow for choosing a colour palette

1. What type of data is it?
2. Decide the colour palette type: 
  - Qualitative: Are the categories equal?
  - Sequential: Does the value go from low to high?
  - Diverging: Does it have a natural balance/zero point?
1. Check colourblindness: `check_color_blindness`
1. Explore colour with `specplot` (combine with `protan` and friends)
1. Explore how it looks with `demoplot`  (combine with `protan` and friends)

---

# Take homes

- Colour choice matters
- Choosing colours is hard
- We can use Hue / Chroma / Luminance to describe colour
- See established palettes: colorspace / viridis / scico
- Assess colours with `colorspace::specplot()`
- Assess colourblindness with `colorspace::cvd_emulator()`
- Evaluate your own colour palettes at `hclwizard.com`
- Choose colour palettes with 
  - `colorspace::choose_palette()`
  - `colorspace::choose_color()`
  - `colorspace::hcl_color_picker()`
  - `colorspace::hcl_wizard()`

---

# Thanks

.large.pull-left[
- [Adam Sparks](https://adamhsparks.com/)
- [Lisa Charlotte Rost](https://twitter.com/lisacrost)
- [Philip Grove](http://researchers.uq.edu.au/researcher/1637)
- [Miles McBain](https://milesmcbain.xyz/)
]

.large.pull-right[
- [Di Cook](http://dicook.org/)
- [Stuart Lee](https://stuartlee.org/)
- [Achim Zeileis](https://eeecon.uibk.ac.at/~zeileis/)
- [Emil Hvitfeldt](https://www.hvitfeldt.me/about/)
]

---

# Resources

.large[
- [Colorspace Package by Zeileis et al](http://colorspace.r-forge.r-project.org/index.html)
- [Colorspace Paper by Zeileis et al](https://arxiv.org/abs/1903.06490)
- [Colorspace talk by Achim Zeileis](https://www.youtube.com/watch?v=6bv2IAcNE_c)
- http://hclwizard.org/ website for assessing colour

]

---
# Resources

.large[
- [How your colorblind and colorweak readers see your colors](https://blog.datawrapper.de/colorblindness-part1/) (Part 1 of 3) by [Lisa Charlotte Rost](https://twitter.com/lisacrost)
- [What to consider when visualizing data for colorblind readers](https://blog.datawrapper.de/colorblindness-part2/) (Part 2 of 3) by [Lisa Charlotte Rost](https://twitter.com/lisacrost)
- [The Science of Color Perception](https://www.youtube.com/watch?v=poL7nDmqjmk) by [Calder Hanson](https://www.youtube.com/channel/UC7zwxzcgdrjnFkRrLnfN_CA)
- [Radiolab episode on colour vision](https://www.wnycstudios.org/podcasts/radiolab/articles/rippin-the-rainbow-an-even-newer-one) 
]

---

# Colophon

.large[
- Slides made using [xaringan](https://github.com/yihui/xaringan)
- Extended with [xaringanthemer](https://github.com/gadenbuie/xaringanthemer)
- Colours taken + modified from [lorikeet theme from ochRe](https://github.com/ropenscilabs/ochRe)
- Header font is **Josefin Sans**
- Body text font is **Montserrat**
- Code font is **Fira Mono**
- template available: [njtierney/njt-talks](github.com/njtierney/njt-talks)
]

---

# Learning more

.large[
<svg viewBox="0 0 640 512" xmlns="http://www.w3.org/2000/svg" style="height:1em;fill:currentColor;position:relative;display:inline-block;top:.1em;">  <path d="M425.7 256c-16.9 0-32.8-9-41.4-23.4L320 126l-64.2 106.6c-8.7 14.5-24.6 23.5-41.5 23.5-4.5 0-9-.6-13.3-1.9L64 215v178c0 14.7 10 27.5 24.2 31l216.2 54.1c10.2 2.5 20.9 2.5 31 0L551.8 424c14.2-3.6 24.2-16.4 24.2-31V215l-137 39.1c-4.3 1.3-8.8 1.9-13.3 1.9zm212.6-112.2L586.8 41c-3.1-6.2-9.8-9.8-16.7-8.9L320 64l91.7 152.1c3.8 6.3 11.4 9.3 18.5 7.3l197.9-56.5c9.9-2.9 14.7-13.9 10.2-23.1zM53.2 41L1.7 143.8c-4.6 9.2.3 20.2 10.1 23l197.9 56.5c7.1 2 14.7-1 18.5-7.3L320 64 69.8 32.1c-6.9-.8-13.5 2.7-16.6 8.9z"></path></svg> [colorspace by Zeileis et al.](http://colorspace.r-forge.r-project.org/index.html)

]

---

.vhuge[
**End.**
]

---
# Workflows

---
# Explore rainbow palette with specplot: Rainbow + specplot

```r
rainbow(n = 20) %>% specplot()
```

---
# Explore rainbow palette with colorblindness specplot: Rainbow + hclplot

```r
rainbow(n = 20) %>% hclplot()
```

---
# Explore rainbow palette with colorblindness specplot: Rainbow + protan + specplot

```r
rainbow(n = 20) %>% protan() %>% specplot()
```

---
# Explore rainbow palette with colorblindness specplot: Rainbow + protan + hclplot

```r
rainbow(n = 20) %>% protan() %>% hclplot()
```

---
# Explore palettes with specplot: Rainbow + demoplot

```r
rainbow(n = 20) %>% demoplot()
```

---
# Explore palettes with specplot: Rainbow + protan + demoplot

```r
rainbow(n = 20) %>% protan() %>% demoplot()
```

---
# Compare Rainbow to Viridis: specplot

.pull-left[

```r
rainbow(n = 20) %>% 
  specplot()
```

<img src="figures/show-rainbow-viridis-specplot-1-1.png" width="936" style="display: block; margin: auto;" />
]

.pull-right[

```r
pals::viridis(n = 20) %>% 
  specplot()
```

<img src="figures/show-rainbow-viridis-specplot-2-1.png" width="936" style="display: block; margin: auto;" />
]

---
# Compare Rainbow to Viridis: specplot + protan

.pull-left[

```r
rainbow(n = 20) %>% 
  protan() %>% 
  specplot()
```

<img src="figures/show-rainbow-viridis-specplot-protan-1-1.png" width="936" style="display: block; margin: auto;" />
]

.pull-right[

```r
pals::viridis(n = 20) %>% 
  protan() %>% 
  specplot()
```

<img src="figures/show-rainbow-viridis-specplot-protan-2-1.png" width="936" style="display: block; margin: auto;" />
]

---
# Compare Rainbow to Viridis: hclplot

.pull-left[

```r
rainbow(n = 20) %>% 
  hclplot()
```

<img src="figures/show-rainbow-viridis-hclplot-1-1.png" width="936" style="display: block; margin: auto;" />
]

.pull-right[

```r
pals::viridis(n = 20) %>% 
  hclplot()
```

<img src="figures/show-rainbow-viridis-hclplot-2-1.png" width="936" style="display: block; margin: auto;" />
]

---
# Compare Rainbow to Viridis: hclplot + protan

.pull-left[

```r
rainbow(n = 20) %>% 
  protan() %>% 
  hclplot()
```

<img src="figures/show-rainbow-viridis-hclplot-protan-1-1.png" width="936" style="display: block; margin: auto;" />
]

.pull-right[

```r
pals::viridis(n = 20) %>% 
  protan() %>% 
  hclplot()
```

<img src="figures/show-rainbow-viridis-hclplot-protan-2-1.png" width="936" style="display: block; margin: auto;" />
]

---
# Compare Rainbow to Viridis: demoplot

.pull-left[

```r
rainbow(n = 20) %>% 
  demoplot()
```

<img src="figures/show-rainbow-viridis-demoplot-1-1.png" width="936" style="display: block; margin: auto;" />
]

.pull-right[

```r
pals::viridis(n = 20) %>% 
  demoplot()
```

<img src="figures/show-rainbow-viridis-demoplot-2-1.png" width="936" style="display: block; margin: auto;" />
]

---
# Compare Rainbow to Viridis: demoplot + protan

.pull-left[

```r
rainbow(n = 20) %>% 
  protan() %>% 
  demoplot()
```

<img src="figures/show-rainbow-viridis-demoplot-protan-show-1-1.png" width="936" style="display: block; margin: auto;" />
]

.pull-right[

```r
pals::viridis(n = 20) %>% 
  protan() %>% 
  demoplot()
```

<img src="figures/show-rainbow-viridis-demoplot-protan-show-2-1.png" width="936" style="display: block; margin: auto;" />
]