colour_guidance.Rmd

---
title: "Colour Guidance"
date: "`r Sys.Date()`"
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    site_branding: "Suffolk County Council"
  navigation:
    breadcrumb_trail:
      - href: "index.html"
        text: "Home"
      - text: "Colour Guidance"
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---

# A note on colour palettes, branding and accessibility
## Putting thought into requirements and responsibilities

The goal of colour palettes is to improve storytelling with data, whether it is to show continuous change in a value, differences between distinct categories or to highlight a specific category that you want to highlight. 

Some questions we need to think about when choosing colour:

- Is it accessible for everyone, specifically for those with colour blindness/color vision deficiency?
- Is there an association with the colour we use (ie green is good/positive, red is bad/negative)
- Does it fit within the SCC brand?

## Sticking to the Suffolk County Council brand, where possible

The Suffolk County Council has its own colour schemes, the recognisable blues and orange found on the [website](https://www.suffolk.gov.uk/).

This <span style = "color:#2d6ca2">**blue (#2d6ca2)**</span> is used as the primary colour in graphs, with <span style = "color:#e8850c">**orange (#e8850c)**</span> as the secondary colour. Where applicable, the <span style = "color:#e2eefa">**light blue (#e2eefa)**</span> can used as a tertiary colour - but note that this not easily visible against white background (consider using a black outline).

Blue and Orange are colours that are distinct for most common forms of colour blindness/color vision deficiency. However, different types of graphs have vastly different requirements to tell a story, and where the number of categories exceeds three colours the SCC palette is not sufficient.

## Colourblind friendly palettes - **Viridis**

Therefore, instead of designing our own colour palette, I'd recommend the use of [viridis](https://github.com/sjmgarnier/viridis), for graphs with more than three categories. This package has visually appealing colour palettes, and improve graph readability for readers with **common** forms of color blindness and/or color vision deficiency. Note that for some specific forms, you may have to choose another palette within viridis (for example [seaborn](https://seaborn.pydata.org/tutorial/color_palettes.html), or [turbo](https://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html)).

```{r viridis_example, warning = FALSE, message = FALSE}
library(sccthemes)
library(gapminder)
library(dplyr)

asia_pop <- gapminder |> 
  filter(year == 1997 & continent == "Asia") |> 
  select(country, lifeExp, pop, gdpPercap) |> 
  top_n(5)

asia_pop$country <- forcats::fct_drop(asia_pop$country)

scc_piechart(
  asia_pop,
  asia_pop$gdpPercap,
  asia_pop$country,
  title = "GDP per Capita contribution percentage by county",
  subtitle = "Five largest countries only"
  )
```