We know data visualisation is one of the most important ways to make sense of data and consume information. And for that to happen well, the correct use of colour is essential.
In this article, I’ll highlight some important considerations when selecting colours for charts, and show examples of some common mistakes to avoid.
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Humans are visual creatures. The vast majority of information we process comes from our sense of sight. It’s no surprise, then, that data visualisation is one of the most important cornerstones in analytics.
And I’m not talking about big numbers written down for all to see, but the relationship between the forms and numbers that make up our charts.
One element that is often overlooked, and commonly misused, is colour. Well, the vast majority of people who create charts (me included), never had formal training in any type of design-focused discipline, much less dedicated time to learning colour theory.
But, we can’t let that get in the way of powerful data visualisation, so here are some important considerations when colouring your charts, and mistakes you should avoid.
- Emotions
The colours you select will evoke certain emotions in your audience, based on previous associations. Don’t try to fight this. Instead, use it in your favour. Look at the two examples below, taken from the decluttering exercise in our Data Visualisation & Storytelling Certificate. Which one feels more natural to you when it comes to the colour and what it’s associated with?
I’m fairly confident that you’d say that the first one, with “Sales” in blue, and “Costs” in red. This is a common association we have: blue for positive, red for negative. So, should we use blue for positive numbers and red for negative? Again, depends on context. If we are using a sequential palette for visualising company profit, that would make sense, but not for temperature: for that, the opposite would be best, with blue for cold (low temperature) 🥶 and red for hot (high temperature) 🥵.
Credit: Wikipedia
- Accessibility
Colour vision deficiencies (CVD) are more common than most of us realise.
It’s estimated that around 4.5% of the world population has some type of CVD, but the majority are men: around 8% of all men, whereas it’s about 0.5% of all women. The popular, yet inaccurate, term used to describe these conditions is colour blindness. But it’s not a blindness to colour in general. Let me explain.
There are different types of CVD. Total colour blindness (only seeing in black and white, a condition known as achromatopsia) is extremely rare. By far, the most common CVD is some sort of red-green colour deficiency, accounting for 99% of everyone who has a CVD. Some red-green CVD types are milder, just making one colour look a bit more like the other (red-ish green, or green-ish red), but some types make people unable to tell the difference between them at all!
Credit: colourblindawareness.org
Avoid traffic light (green, yellow, red) colour palettes, as they will negatively affect people with colour deficiencies. If you must use this palette for whatever reason, add other elements to convey the same information that the colour is conveying. Instead, go for blue and orange, as they are complementary colours and accessible. Default to them if you are unsure, especially for diverging palettes.
And, this is important for everyone, ensure enough distinction between the different colours, and from colour to their background. Please don’t do anything similar to this next chart. 👇 And yes, the colours are different, and the two lines are there. 😵💫
- Harmony
Speaking of colour palettes, not only do we care for them in terms of accessibility, but also in terms of aesthetics. We want our charts to be pretty and easy to understand. Hopefully, you have great designers in your company who take care of that for you and provide you with a brand-specific palette that is great in those two aspects.
If you don’t, consider using some tried and tested palettes given by specialised data visualisation software (e.g. Tableau) and coding libraries (e.g. “viridis” for R). And if you want to try your hand at creating your own, look at colours that commonly go together in nature (like the orange from the sun against the blue sky during sunset).
Credit: Wikimedia Commons
Channel your inner critic now and judge the following charts based on what we covered here:
Dealroom’s Startup Jobs in Amsterdam
Storytelling With Data - Exercise
UnderscoreStreets (_STREETS) Healthy Streets Index
"Colorwise" by Kate Strachnyi: This book is a data storyteller guide focused on the use of colour in charts, tables and infographics. There is a bit of everything here, from psychological, historical and cultural considerations for colour. Good for a real deep dive.
The Role of Color Theory in Data Visualization by RevUnit: This page discusses exactly that, from colour anatomy, harmony, schemes and many best practices.
Practical Rules for Using Color in Charts by Stephen Few: This is an effective short guide, with quick rules and clearly explains some very common mistakes and how to avoid them.
Data Visualization for Beginners (Color) by Plain Concepts: This page is a more thorough guide, on how to select colour schemes. It also has some guides on creating colour palettes and also lists several effective ones.
Colorblind Guide: This website contains a ton of information on CVD. Perhaps most useful is the CVD Simulator: you can upload the image of your chart and simulate how it will look for people with different colour visual deficiencies.
Here are the key points we covered:
Colour plays an essential role in data visualisation: It can be a great way to convey information and make the chart more effective and pleasant to look at.
But it is often misused: Many people who create charts don't think about this element enough and/or don't know how to use it correctly.
Some key considerations: In this article, we covered some important points to consider and mistakes to avoid.
See you in the next one!
