Academy Xi Blog

How to choose colour charts and graphs for data visualisations

By Academy Xi

colour charts and graphs for data visualisations

Don’t underestimate the power of colour when displaying data. Read on to discover why colour selection is important and approaches you can take to choose the right shades to support your data.

Why is colour important in data visualisation?

Colour is important in data visualisation because it can help to convey information and enhance the overall impact and understanding of the data being presented.

It is important to use colour effectively in data visualisation to ensure that it supports, rather than detracts from, the overall message of the data. When using colour, it is important to choose colours that are easily distinguishable and have appropriate contrasts, and to use colour consistently throughout the visualisation to maintain clarity and consistency.

How to choose best colours for data visualisations

Whichever colour scheme you select, ensure that it’s consistent throughout. This will support clarity and make it easier for your viewer to understand the data. In terms of how to choose the best colours, here are a few approaches to consider.

  • Analyse collected data insights

By analysing the data insights, you will be able to identify patterns and relationships between the variables included. You can then highlight certain patterns or emphasise the relationships using specific colours, perhaps choosing colours that best represent the patterns.

  • Evaluate other visualisations

Consider what you think works, or doesn’t, in other visualisations you have seen when it comes to the colour selection. Finding examples that reflect your data could also be helpful in assisting you with your colour choices.

  •  Highlight important data points

If you want to draw attention to particular sections of data or specific points, consider using a colour that is in high contrast to the rest of your selection so it will stand out. 

Types of colour palette

The following palette types are worth considering when selecting colours for your data.

  • Qualitative palette

This palette is specifically for categorical data where the emphasis is on the distinction between categories rather than on quantitative differences. Qualitative palettes use a limited number of colours with high saturation and good contrast. This ensures each category is easily distinguishable from the next. 

  • Sequential palette

Sequential palette’s use a range of colours that change smoothly from one colour to another to represent a progression of values. This palette is often used to show a continuous variable that has a clear ordering, such as temperature, rainfall, or population density. The colour palette is chosen such that each colour represents a different range of values, with the first colour representing the lowest values and the last colour representing the highest values. For example, with temperature, blue to represent cooler temperatures and red the warmer.

  • Diverging colour palette

A typical diverging colour palette has a neutral colour in the middle, such as white or grey, that represents the baseline or reference value. The colours on either side of the neutral colour represent values that deviate from the reference value, with one colour representing values above the reference and the other colour representing values below the reference. The change from one colour to the next is usually gradual, allowing the viewer to easily see the progression of values represented by the data.

Diverging colour palettes are useful for showing the spread of data around a central value and for highlighting the difference between two opposite values. They are often used in data visualisations such as heat maps and bar charts.

Tools for using colours

It’s well worth checking out these tools for supporting your colour selection process.

  • ColorBrewer

Providing pre-designed colour palettes for a wide range of data visualisation types including sequential and diverging palettes. Designed to be colour-blind friendly, ColorBrewer is widely used by data scientists, cartographers and designers – a popular resource for creating effective and accessible data visualisations. 

  • Color Thief

A small library for grabbing the dominant colour or a representative colour palette from an existing image is the central focus of Color Thief. It was created for web developers who need to extract colours from images to use in their designs. The tool analyses the pixels of an image and finds the most frequently occuring colours, then returns the dominant colour palette of the image. 

  • Viz Palette

Viz Palette helps to create colour palettes based on colour theory, or provides predefined colour palettes for use in visualisations. This tool can ensure colour consistency across different visualisations and presentations, making it easier to compare and interpret data. 

How to get into Data Analytics

At Academy Xi, we offer flexible study options in Data Analytics that will suit your lifestyle and training needs, giving you the perfect foundation for your future in data modelling.

 Whether you’re looking to upskill or entirely transform your career path, we have industry designed training to provide you with the practical skills and experience needed.

If you have any questions, our experienced team is here to discuss your training options. Speak to a course advisor and take the first steps in your Data Analytics journey.