Data visualisation: colours

Introduction

Colour can change how we see information in charts. When colour is used well it can enhance statistical content and make it clearer. When colour is used badly it can confuse users. Your content may even fail the accessibility legislation that applies to public sector websites.

This guidance sets out what to consider when using colour in charts.

It also suggests accessible colour palettes to use with categorical and sequential data, and in focus charts.

Accessibility legislation

Accessibility legislation relevant to public sector websites came into force in September 2020. This means all content on these websites must meet the A and AA success criteria listed in the Web Content Accessibility Guidelines 2.2.

Consequences for inaccessible content

Content on public sector websites that does not meet the Web Content Accessibility Guidelines 2.2 can get complaints related to the Public Sector Bodies (Websites and Mobile Applications) Accessibility Regulations 2018 and/or the Equality Act 2010. This could cause reputational damage and loss of public trust.

Make sure whoever is responsible for the content you publish is aware of this and the possible risks involved.

What to consider when using colour in charts

Limit the number of colours you use

Limiting the number of colours you use in a chart makes them stand out. This is useful because colour helps the brain quickly identify differences. Using lots of colours makes this harder.

Think carefully before you introduce additional colours into a graph. Ask yourself:

• Do you really need the colours?
• Do they make your message clearer?

You should only use different colours when they show helpful differences in the data. When people look at a chart with lots of colours, they try to work out what those different colours mean. If your colours suggest meanings that are not there the user will waste time and effort trying to understand them.

When you have categorical data that cannot be grouped, use a single colour. The user is much more likely to compare the bars when they look alike than when they look different. See the first example in the Office for National Statistics’ (ONS) guidance on using colour in bar charts.

Use colour consistently

If you have a series of charts, assign the same colour to the same variable in each chart.

If you change the way you use colours in a series of graphs it becomes harder for users to follow the story. It can also cause them to mistake one data series for another, especially if a user is skim reading. See guidance from ONS on using colour to connect information.

Consider colour associations

People link certain colours to certain things. For example, people tend to link blue with water and green with grass. Using these types of links in data visualisations can help people understand information faster. If you use colours that go against these associations, it might take users longer to understand your chart. For example, it would take users longer to understand you were talking about grass if you used the colour red to represent it.

Colours can also have cultural associations. We may react to these consciously or unconsciously. These associations vary across countries and groups.

Finally, if shades of a similar colour are used on a chart users may assume the data series’ they represent are associated in some way. If your data has distinct categories you should use distinct colours.

Think about colour associations when choosing your palette. Consider whether your colours are appropriate.

More information on colour associations from ThoughtCo.

More detail on colour associations in charts from ONS.

Use a plain background

Never use an image as a background. This can make charts difficult to see. Charts that use an image as a background are also likely to fail the accessibility legislation.

We recommend using white as a background colour. All our suggested colour palettes are based on this.

There are some accessibility issues with using white as a background colour. Some users may find a white background difficult to look at because of glare or the high contrast of colours against it. On the other hand, other users prefer a white background because they need the high level of contrast it provides.

A perfect solution would be to have data visualisations that can be fully customised to meet specific user needs. As this is not possible at the moment we recommend a white background because it provides a helpful ‘anchor’ in general.

Consider colour contrast ratios

Colour contrast ratios are a measure of difference in brightness between two colours. Colour contrast ratios range between 1 to 1 (white on white) and 21 to 1 (black on white).

More information on how to calculate colour contrast ratios from the Accessibility Developer Guide.

The Web Content Accessibility Guidelines (WCAG) which the UK accessibility legislation aligns to, require a 3 to 1 ratio for graphical elements and 4.5 to 1 ratio for text.

Colour contrast checker

We recommend calculating your colour contrast ratio using the WebAIM colour contrast checker. This helpful resource calculates the colour contrast ratio between two colours. It also states if the colours pass or fail the WCAG criteria.

How to use the WebAIM colour contrast checker

To use the colour contrast checker you will need hex codes for your colours.

Identifying hex codes on Microsoft programs:

• select the graphical element or object of interest
• select the ‘Format’ ribbon
• select the ‘Shape Fill’ dropdown
• select ‘More Fill Colours’
• select ‘Custom’
• read off the hex code (it starts with a hashtag symbol (#))

Then:

• open the WebAIM colour contrast checker.
• insert the hex codes
• review the information the checker gives you

We should always meet colour contrast requirements to the AA level.

But it is even better if we can meet the AAA level.

If your background colour is white, the hex code for white is: #FFFFFF.

Consider low vision and colour blindness

Low vision

When we talk about the accessibility of colours in data visualisations we tend to concentrate on colour blindness.

But we should also consider people with low vision. This can be caused by conditions like glaucoma and cataracts.

You can find out more about the difference between low vision and colour blindness in this article on visual disabilities.

Keeping a 3 to 1 contrast ratio between adjacent colours (as set out in success criterion 1.4.11) is important for people with low vision.

Colour blindness

Colour blindness affects around 8% of men and 0.5% women. It can come in many different forms. You can find more information about types of colour blindness in ONS guidance on using colours.

The section on ‘Choosing your own colour palette’ gives details of some useful tools for checking palettes for colour blindness issues.

We have checked our suggested colour palettes for different types of colour blindness. Guidance related to this is available in each section.

Consider your colours in greyscale

It is a good idea to check your charts can be understood in greyscale.

This is because:

• it is a good way to check if your chart relies on colour to be understood
• there is still a need for some users to print in black and white
• seeing in greyscale is a specific type of colour blindness

Check how your charts look by using print preview and selecting ‘greyscale’ or ‘black and white’.

We have checked our suggested colour palettes to see how they look in greyscale. Guidance related to this is available in each section.

Do not rely on colour alone

Colour should not be used as the only way to communicate information. This is stated in accessibility success criterion 1.4.1.

See the section on accessibility legislation for more information.

Use the SVG format

Scalable Vector Graphics (SVGs) retain the same quality no matter what screen resolution or size they are viewed at. This means a user can zoom in and out and the image retains its quality. This adaptability makes everything on a chart easier to see, making it a more accessible format for images of charts.

Our guidance on ‘Making analytical publications accessible‘ has more information on SVG images and how to create them.

Our colour palettes

We have developed three colour palettes for use in charts.

All our palettes take into account the general considerations set out in this guidance and the accessibility legislation.

All our palettes are designed to be used with a white background.

The palettes are based on the colours in the Government Digital Service design system. But we have made some slight changes to increase the colour contrast.

Colours are given in three different ways:

• Hex code
• Red Green Blue (RGB) code
• Cyan, Magenta, Yellow and Black (CMYK) percentages

Contrast ratios are also given for all palettes. These measure the difference in brightness between two colours.

Contrast ratios are important because they are mentioned specifically in the accessibility regulations. See the section on accessibility legislation for more detail.

Categorical data colour palette

What is categorical data?

Categorical data can be divided into groups or categories by using names or labels.

Palette table

Note: the cells in the “Example of colour” column may appear blank to screen reader software. They contain a colour fill, but no data.

Contrast ratios

Numbers in the contrast ratio table give the ratio to 1 between the colours in the relevant row and column. These are important because of the accessibility legislation.

Information about these colours

• All colours have a contrast ratio of at least 3 to 1 against white.
• When used in the order presented in the list, all adjacent colours have a contrast ratio of at least 3 to 1.
• All colours have passed our checks for colour blindness – see the section on ‘Choosing your own colour palette’ for more information.
• When used in the order presented in the list, all adjacent colours appear distinct in greyscale.

Ideally, use just the first four

We recommend a limit of four categories as best practice for basic data visualisations. More than four series can make a chart too cluttered. This is why all our examples of using the categorical colour palette only use four series.

The dark grey and light purple have been added to this palette due to demand for extra colours. But these should only be used when essential.

Plain text alternative

Remember, using an accessible colour palette is not the only thing to consider when it comes to accessibility. All charts should have a plain text alternative that communicates the overall message of the chart. This can be given through a table of data or a text description. When deciding which to provide you should focus on the best way to communicate the message in the chart.

If you decide to provide a table, a good way of doing this is to use toggle functionality, if your website has it. The HTML horizontal bar charts on GOV.UK allow users to toggle between a chart or plain text table view. The charts on Department for Education’s website ‘Explore education statistics’ also have this functionality.

If you do not have toggle functionality we recommend providing the table of data in HTML in the body text of the page. However, you can link to the data if necessary: example of tables for charts linked to in an annex.

If you provide a text description of your chart this should also be given in the body text of the page. Example of a plain text description of a chart in the body text.

Note: when the chart shows a large amount of data, a text description is better than showing or linking to a large data table.

Using the categorical colour palette

Simple bar charts

Use a single colour for all bars in vertical column charts and horizontal bar charts where there are no groups to highlight. We recommend using the first colour in the palette. We call this colour ‘dark blue’. We recommend dark blue because it:

• has the highest contrast ratio with a white background out of the first four main colours in the palette
• works for different types of colour blindness
• works in greyscale

The gap between the bars should be slightly narrower than the width of a single bar. Unless you are presenting a histogram. Histograms should have no gaps between bars.

Remember: all charts should have a plain text alternative. This can be a text description or a table of data, ideally in the body text of a page.

Example: bar chart showing one series with accompanying text description

Figure 1: Profit for four fictional companies in 2020

Bar chart showing fictional data on profit for four companies. The same dark blue colour is used for all bars as they all represent profit.

Clustered and stacked bar charts

We advise that clustered and stacked bar charts should have no more than four series.

We recommend using our suggested colour palette in the order given in the list to make sure adjacent colours have enough contrast. It will also mean the colours will work for different types of colour blindness and in greyscale.

In clustered bar charts the gap between the clusters should be wider than a single bar. If you’re presenting discrete data in clustered bar charts, leave a small gap between the bars in each cluster to increase contrast and help users work out the meaning of the colours.

Accessibility and use of legends

Stacked and clustered bar charts both require the use of legends.

Make sure the order and orientation of the legend matches the order and orientation of the bars in each cluster. You should also state the legend order matches the order of the bars in the clusters.  This should be stated above the chart so people can use this information to match the labels.

Alternatively, for horizontal clustered bar charts the first cluster could be labelled directly.

Examples: clustered bar charts with four categories – providing information on legends

Figure 2a: Number of apples, bananas, pears and pineapples found in four shops (fictional data)

The legend is presented in the same order as the bars in the clusters.

Figure 2b: Number of apples, bananas, pears and pineapples found in four shops (fictional data)

Both these clustered bar charts show fictional data using the four colours in the categorical data colour palette. The fictional data shows all shops had more apples than any other fruit listed. Shop 4 had the highest number of apples (44). Out of the fruits listed, bananas were the next most stocked in all shops – shop 1 had the most (33). This was followed by pears and then pineapples. All shops had 10 pineapples.

Figure 2a states the layout of the legend above the chart. Figure 2b labels the first cluster directly instead of using a legend.

Example: stacked bar chart with four categories and accompanying table of data

Figure 3: Percentage of each house type within each ownership situation (fictional data)

The legend is presented in the same order as the categories within the bars.

Table of data

Line charts with one series

We recommend using the first colour in the palette called ‘dark blue’. We recommend dark blue because it has the highest contrast ratio with a white background.

Make sure you choose a suitable level of thickness for the line. Some people who have mild colour blindness are able to see a colour, but only if there is enough ‘mass’ of it. The exact thickness you need will depend on how you create your image.

Example: line chart with one series and accompanying text description

Figure 4: Revenue of a fictional company, 2010 to 2020

Line chart showing fictional data on revenue. There is one line on the chart. This line uses the dark blue colour from the palette. The fictional data shows volatile revenue since 2010, but in 2020 it stood at £60 million.

Line charts with two series

We recommend using the colours from the palette called ‘dark blue’ and ‘orange’. This is because this is the pair of colours from the palette with the highest contrast ratio. They also work for different types of colour blindness and are distinct in greyscale

Remember to be careful with the cultural associations of colour. Orange in particular can have strong cultural and political associations in some settings.

Label the lines directly rather than using a separate legend. This means that users will not have to rely on colour to match the labels to the series.

If the order of the series is important, use the colour palette for sequential data. See the sequential colour palette section of this guidance for more information.

Example: line chart with two series and accompanying text description

Figure 5: Annual revenue of two fictional companies, 2010 to 2020

Line chart showing fictional data on revenue. There are two lines on the chart. One uses the dark blue from the palette, the other uses the orange. The fictional data shows volatile revenue since 2010. In 2020, company 1 had revenue of around £56 million (up from £40 million in 2010) and company 2 had revenue of £40 million (up from around £31 million in 2010).

Line charts with more than two series

As with stacked and clustered bar charts, we advise that line charts should have no more than four series.

It is difficult to choose colours for line charts with more than two series.

This is because it is not possible to have a chart colour palette of more than two colours where all colours have at least a 3 to 1 contrast ratio with:

• the white background
• all other colours in the palette

In stacked and clustered bar charts with only positive values this is not an issue because the series always stay in the same order. This means we can control what colours are next to each other and make sure they have a high enough contrast ratio.

But lines do not always stay in the same order. So, it is not possible to make sure adjacent colours always have at least a 3 to 1 contrast ratio.

This means that it is not possible to provide a palette for line charts with more than two series that would meet the contrast ratio requirements. So, you should only use line charts with more than two series when they are essential to getting information across. This will help you meet accessibility success criterion 1.4.11.

It is possible to design a colour palette for line charts with more than two series which would work:

• for different types of colour blindness
• in greyscale

However, this would involve using colours that have a less than 3 to 1 contrast ratio with the white background. Remember that some people with visual disabilities may not be able to see these lines at all.

So, there is no perfect solution to this problem.

Our advice is:

1. Use our suggested palette or another that makes sure all lines have a 3 to 1 contrast with the white background. We have chosen to prioritise the colour contrast ratio with a white background. This is because this will help many people see the lines, even if they cannot tell the colours apart.
2. Label the lines instead of using a legend.
3. Provide a plain text version of the chart. A plain text version can be provided through a text description or a table of data. It is necessary for all charts but particularly important when your chart cannot meet the colour contrast requirements.
4. Consider a different approach.

Other possible approaches:

• A small multiples plot (see figure 2 in this statistical bulletin) – these can make it much easier to see differences in each series.
• Using texture like dots and dashes to differentiate the lines – but be aware that this can make a chart too cluttered and textured lines may be misinterpreted as they are often used for projections, forecasts or targets.
• Add shapes to the data points on the lines – but be careful not to overuse these as they can make a chart too cluttered
• Using a focus chart which highlight specific chart elements – you can find more information on these in the ‘Focus charts colour palette’ section.

Example: Line chart with four series and accompanying data table

Figure 6: Annual revenue of four fictional companies, 2010 to 2020

Table of data

Pie charts

You should only use pie charts in certain circumstances. Please see our ‘Data visualisation: charts’ guidance.

Advice for pie charts:

• Put a white border around each sector to enhance the contrast.
• Label the sectors directly rather than in a separate legend – this helps users to match the labels to the variables without relying on colour.
• Consider adding value labels to help users understand the chart.
• Provide a plain text version of the chart in the body text – this can be a text description or a table of data.

Colours for pie charts with two variables

When pie charts present two variables we recommend using the dark blue and orange colours from our palette. This is because these colours have the greatest contrast. These colours also work for different types of colour blindness and are distinct in greyscale.

Be careful with the cultural associations of colour. Remember that orange can have strong cultural and political associations in some settings.

Example: pie chart showing two sectors with accompanying text description

Figure 7: Questions in an exam by whether they were answered

A pie chart with two sectors. One is labelled ‘Answered (82%)’, the other is labelled ‘Not answered (18%)’.

Colours for pie charts with more than two variables

We advise to use our suggested palette. Use the order given in a clockwise direction, starting from the 12 o’clock position.

As with all charts, all colours you use in a pie chart need to have at least a 3 to 1 contrast ratio with the white background. But for pie charts with more than two variables, each colour also needs to have at least a 3 to 1 contrast ratio with the two other colours it will be next to.

This is possible when you have two or four variables because you can alternate darker and lighter colours. This allows you to make sure adjacent colours have the level of contrast they need. It is not possible when you have three or five variables. This means, when you use pie charts with three and five variables the plain text alternative is very important.

Note: we would not recommend using a pie chart with more than five variables.

Example: pie chart showing four sectors with accompanying table of data

Figure 8: Responses to the “What is your religion?” question on the 2011 Census of England and Wales

Table of data

Source: 2011 Census for England and Wales 

Sequential data colour palette

What we mean by sequential data

Sequential data is any sort of data where the order has some meaning. For example, age groups.

Choosing colours for sequential data

When you are presenting sequential data, it is best to use a single hue, or small set of closely related hues. You should change the lightness from pale to dark, rather than alternating between hues. Colours should also progress in the direction a user would expect.

For example, on the UK government website for data on coronavirus (COVID-19) there is a visualisation of vaccine doses. In this visualisation, the colour used to represent the number of vaccine doses gets darker as we move from no dose to three doses.

Palette table

Note: the cells in the ‘Example of colour’ column may appear blank to screen reader software. They contain a colour fill, but no data.

Contrast ratios

Numbers in the contrast ratio table give the ratio to 1 between the colours in the relevant row and column. These are important because of the accessibility legislation.

Information about these colours

These colours pass our checks for colour blindness and appear distinct in greyscale.

But this palette is restricted to tints of one colour. This means that:

• neither dark or light blue have a high enough contrast with the mid blue to meet the contrast ratio requirements
• only the dark and mid blue have the required level of contrast with a white background

As a result, we need to take care when using this colour palette in charts.

Using the sequential colour palette in charts

Due to the problems with contrast ratios in this palette our advice is as follows.

For line charts:

• label the lines instead of using a legend

For clustered bar charts:

• make sure the order and orientation of bar chart legends match how categories are presented and state this above the chart, or label the bars in the first cluster directly
• leave small gaps between the bars within clusters
• give all bars a dark blue outline

Note: we only recommend outlining the bars when using this palette as bar outlines add unnecessary clutter. However, to use a sequential colour palette you have to use a light colour for one of the bars. This is because the light colour does not have enough contrast with the white background. A border allows people to see the size of the bar even if they cannot see the colour fill. We outline all bars (instead of just the light blue bars) to maintain consistency.

For stacked bar charts

• make sure the order and orientation of bar chart legends match how categories are presented and state this above the chart

Remember, provide a plain text version of all charts – this is even more important with this palette when you cannot meet the colour contrast requirements.

Example: line chart with three series of sequential data and accompanying data table 

Figure 9: Percentage of different age groups who live in a flat, 2010 to 2020 (fictional data)

Table of data

Example: clustered bar chart with three series of sequential data 

Figure 10: age of shoppers in four different shops on a given day (fictional data)

The legend is presented in the same order as the bars in each cluster.

The bar chart shows shop 3 had the highest number of shoppers aged 26 to 64 and 65 and over but shop 1 had the highest number of shoppers aged 0 to 25. Shop 2 had the lowest number of shoppers overall.

Focus charts colour palette

What we mean by focus charts

Focus charts are charts which use colour to highlight specific elements to help users understand the information.

Palette table

Note: the cells in the ‘Example of colour’ column may appear blank to screen reader software. They contain a colour fill, but no data.

Contrast ratios

Numbers in the contrast ratio table give the ratio to 1 between the colours in the relevant row and column. These are important because of the accessibility legislation.

Information about these colours

These colours have passed our checks for colour blindness and appear distinct in greyscale.

The light grey has a high contrast with the dark blue. But its contrast ratio with the white background is below the required 3 to 1 level. This means you should only use this approach when it is essential to communicate your message. This will help you meet accessibility success criterion 1.4.11.

The plain text alternative for the chart becomes very important when you cannot meet the colour contrast requirements.

We have chosen this light grey because we want these lines to be less obvious. This helps us emphasise the series we are interested in.

We would also suggest making the line showing the series of interest slightly thicker than the others. This will also help to make it more prominent.

Using the focus chart palette

Example: a focus chart with eight lines and accompanying text description

Figure 11: Coronavirus (COVID-19) cases per million people in selected European countries, 7-day rolling average, by date reported, 1 August 2021 to 14 November 2021

A line chart where the lines for Austria, Belgium, Netherlands, Germany, France, Italy and Spain are a light grey. The line for the UK is in dark blue and stands out from the rest. It shows the UK’s cases per million were higher than the other European countries for most of this period, fluctuating between 400 and 700. Towards the end, cases per million in Austria, Belgium and Netherlands rose sharply and were above the figure for the UK.

Source: Coronavirus (COVID-19) cases taken from Our World in Data

Using our colour palettes in Microsoft, R and Python

We have created guidance on how to implement all of our accessible colour palettes in Microsoft, R and Python.

R and Python colour palette packages

The Analysis Standards and Pipelines (ASAP) team have also created packages in Python and R for the colour palettes.

They are open source and available for anyone across government to use when creating data visualisations. It will be great for those working in DAP and they will eliminate the need for copy and pasting codes from the webpage.

For more information on how to install and use the packages, please read the R afcolours documentation, or py_af_colours documentation if you are using Python.

Please get in touch if you have any questions or feedback, by emailing Analysis.Function@ons.gov.uk.

Colours for axes and labels

You should only use colour in the foreground items of charts, such as lines and bars.

Drawing and labelling axes

We recommend using a grey shade for drawing and labelling axes. The grey you use should have a 3 to 1 ratio with a white background.

The Microsoft default grey for axes and axes labels has a hex code of #595959. This is fine.

Gridlines

Gridlines are useful, but they are not needed to understand a chart. Remember that they can make a chart more difficult to read and cluttered if they are too dark.

In our opinion if they are used, they do not need to meet the 3 to 1 colour contrast ratio with the white background. This is because they are useful but not necessary to understand the data. Also, if they are too dark they make it hard to see the data.

The Microsoft default colour for gridlines has a hex code of #D9D9D9. We think this is fine to use.

Data labels

Data labels are the labels given to lines on line charts or sectors on pie charts.

It is best to use black or very dark grey text for data labels. If lines or sectors are very close together you may decide it would be helpful to colour the text to match the line or sector. The colours in categorical data colour palette can be used for text on a white background if the font is large enough. But remember that as images of charts often get resized this will change the size of the text. See the accessibility legislation section of this guidance for more information on using colours for text.

Other pointers

All chart text and labels should be in a sans serif font. Your text should be at least size 12 when you are creating the chart image.

Some people find serif fonts, such as Times New Roman, hard to read. The British Dyslexia Association recommends not to use them.

Choosing your own colour palette

While we have suggested a standard palette to use, there may be reasons for using different colours. Follow these five steps if you’re choosing your own colour palette.

Some organisations will have their own branding schemes. If you have an existing palette you want to test for accessibility, skip step 1 and use steps 2 to 5 to test it.

Step 1: Use a tool to find a palette

Use online tools like colourbrewer and colourhexa to help you find a suitable palette.

If you have it, Adobe Illustrator can also help you choose a palette.

Step 2: Remember criteria 1.4.1 and 1.1.1

Visualisations should not rely only on colour to communicate information.

Make sure your visualisations have a plain text alternative to communicate the message of the chart. You should do this even if the colours meet the contrast requirements and work for the main forms of colour blindness.

More information on making images of charts accessible.

Step 3: Check the contrast ratios of adjacent colours

Remember: adjacent that colours need a 3 to 1 contrast ratio. This is needed to pass the accessibility legislation.

Test the contrast ratios of colours using the colour contrast checker from WebAIM.

If you are using R, you can also do this using the coloratio package.

Step 4: Check colours for colour blindness compatibility

People with colour blindness find it hard to identify some colours. They also find it hard to tell some colours apart.

You can use a variety of tools to check colours against the main types of colour blindness:

• Adobe’s colour accessibility tool gives a pass or fail indication for up to five colours. It also visualises where they sit in a colour wheel.
• The DavidMathLogic site gives an immediate visualisation of the main types of colour blindness.
• Coblis, a colour blindness simulator, allows you to upload an image and check it against the main types of colour blindness.
• If using R, use the ColorBlindCheck package to visualise colours from a series.
• If you have it, Adobe Illustrator allows you to simulate different kinds of colour blindness.

Step 5: Check your colours work in black and white

This is less important if the visualisation is only going to be in digital form. However, if you are producing something that may be printed, you should check your colours work in black and white. Use print preview to check your palette.

If using R then the ‘ColToGrey’ function from the DescTools package can change the colour palette for you.

Related links

WebAIM content

• WebAIM colour contrast checker
• WebAIM article on visual disabilities

Checking for colour blindness

• Adobe’s colour accessibility tool
• DavidMathLogic site
• Coblis: a colour blindness simulator 
• The ColorBlindCheck package for R

ONS guidance

• Using colours 
• ONS guidance on data visualisation

Analysis Function guidance

• Charts: a checklist
• Using our colour palettes in Microsoft, R and Python
• Data visualisation e-learning
• Data visualisation: charts
• Data visualisation: infographics
• Data visualisation: tables
• Releasing statistics in spreadsheets 
• Using symbols and shorthand 
• Making analytical publications accessible
• Dashboards

Other guidance

• Dyslexia friendly style guide from the British Dyslexia Association
• Foundations of colour contrast from TetraLogical

Tools for choosing your own colour palette

• Colorbrewer can help you visualise and choose your own palette
• Colourhexa gives useful information on colours
• Adobe Illustrator has built in tools that can help you choose a suitable colour palette

Other links

• Analysis Function blog post on communicating using colour
• ONS blog posts on data visualisation
• Analysis Function YouTube channel 

Any questions?

If you have any further questions you can:

• email Analysis.Function@ons.gov.uk
• join our Basecamp project for accessibility for statistics and analysis or presentation and dissemination of statistics and analysis – if you are not on the Cross Government Basecamp, email Analysis.Function@ons.gov.uk for an invite link
• contact your departmental presentation or web dissemination champion
• contact us through Twitter @gov_analysis