Communicating using colour
This week we have launched a new addition to our colours guidance that looks at using our colour palettes in Microsoft, R, and Python.
It provides instructions and examples to show you how easy it is to implement our recommended colour palettes.
Why it is important to be aware of colour when communicating data
Colour is often used when communicating data, statistics and analysis in charts, maps, tables, and infographics. When used well, colour can enhance statistical content. But when used incorrectly, it can cause confusion or fail the Web Content Accessibility Guidelines (WCAG).
We should be conscious that not everyone sees and interprets colour in the same way.
Low vision
Some users may not be able to see pale colours distinctly on a white background. We need to use colours which have a high enough contrast ratio with the background.
Figure 1: Example of a blurred line chart and bar chart
These line and bar charts compare an original version with a representation of what someone with low vision might experience. The original charts are clear with distinguishable axes labels. The low vision simulation is blurry and hard to read.
Colour blindness
There are many types of colour blindness.
This can mean a set of colours which look different to some people, can look very similar or the same to others. We therefore need to ensure that colour is not the only way we communicate information. For example, some users might not be able to match legend labels to chart elements.
Figure 2: Bar charts representing different types of colour blindness
Figure 2 shows the same chart through filters that simulate different types of colour blindness. The original chart is in the bottom right corner.
Unintentional colour associations
Some users may draw unintended associations between colours. For example, if there are two lines on a line chart in different shades of purple, a user may assume they are associated.
We need to make sure we use a colour palette with distinct colours when dealing with different categories.
Figure 3: Line charts representing an example of colour associations
Figure 3 compares two line charts showing fictional data of revenues for four companies. One uses a distinct colour palette which makes it clear each line is for a separate company. The other uses a purposely bad palette which has two similar purple colours in it. A user may think this means Company 1 and 3 are associated in some way.
Technology
Another factor often forgotten when using colour, is that sometimes appearance can vary in different formats or lighting. For example, switching between screens or looking at a printed documents may make colours appear differently. Especially if the printer only has black and white ink!
Figure 4: Comparison of printing in colour with printing in black and white
Figure 4 shows two pages with charts on. One is representing what it would look like printed in colour. The other represents printing in greyscale.
Time can be wasted by users trying to understand the messages you are sharing. We must keep possible issues in mind when choosing to use colour. We must not rely solely on colour to communicate a message.
New guidance available
Our new guidance item called “Using our colour palettes in Microsoft, R and Python” includes a Microsoft theme file, R code and Python code.
The examples in this guidance show how to:
- add labels at the end of lines in line charts
- specify the colour and thickness of gridlines
- remove clutter from default charts
- change font size
- add borders around bars (when needed)
Other guidance items you might find useful are:
Ella Goodman
Find out more about the support the team offer with communicating statistics and analysis.
