Today on day 2 of the Data School we learnt (along with many other things) about colour. In this blog I want to talk about colour blindness (or the official term, colour vision deficiency).

This topic I am sure has been posted before on the Data School blog. I am writing about it myself as I feel I was already aware about colour blindness prior to today, yet I was still not applying the best practices for colour when using Tableau. I will use this blog as a reminder to myself and an explanation for others as to why we should be careful of using the classic connotations of green and red.

According to People Blindness, around 8% of males have colour vision deficiency, and under 1% of females. With 8% being a reasonably high number, Andy was surprised that he believes Joe from DS16 is the first to have colour vision deficiency out of all previous DS cohorts. This I hope will make our cohort even more wary of colour.

The most common form is red-green colour blindness, affecting around 6% of the male population.

Hopefully this image below can help people understand how people with red-green colour blindness may view green and reds. In Deuteranopia (one form of red-green colour blindness), the red and green are very similar, and in the milder version of Deuteranomaly they are not as contrasting as they would be with normal vision.

This is an issue when using Tableau, as we often have the use of red and green ingrained into us. With the traffic light system, green and red are the opposite and therefore you may naturally use these as colours in a Tableau visual. Green=good, Red=bad.

I used the colours of green and red last year when I was on a university placement. Yet as shown from the image of the forms of colour blindness, people with colour vision deficiency may struggle to see the difference.

Instead, Andy suggested to us to that blue and orange were an example of good contrasting colours to use.

I hope to keep this in mind in the future!