Author Archives: Toni

Gaps between bars

Standard bar chart

A standard bar chart should have gaps between bars that are slightly narrower than the bars. The exceptions to this are the exception of histograms and clustered bar charts.

Example of correct use of gaps between bars

A bar chart showing employment data with gaps between the bars that are slightly narrower than the bars.

Clustered bar chart

A clustered bar chart should have gaps between the clusters that are slightly wider than a single bar.

Example of correct use of gaps between clustered bars

A clustered bar chart showing employment data with gaps between the bars that are slightly wider than a single bar.

Choosing the correct chart

There are eight common relationships that charts display. Prioritise what you want to highlight in the data and choose the chart type accordingly.

The eight common relationships within data are the following:

  • comparisons of magnitude (size)
  • time series
  • ranking
  • part-to-whole
  • deviation
  • distribution
  • correlation
  • spatial (maps will be covered separately in phase 4)

Choice of data

Consider the message you want to communicate and choose your data accordingly. Your message might be better conveyed by deriving variables.

Comparisons of magnitude (size)

To show:

  • X is bigger than Y
  • A is almost twice the size of B

Comparisons of size are shown most effectively as horizontal or vertical bars. Always begin the y-axis at zero.

Small differences in magnitude, starting the y-axis at a non-zero value

If there are small differences between values sometimes it is necessary to start the y-axis at a non-zero value.

Always put a break in the y-axis if you do not start at zero.

Example of chart with a break in the y-axis

A bar chart with a break in the y axis at 750 to show that one category is slightly higher in value than the others.

Use a dot (or other symbol) plot to make comparisons between values. The size of the visual element representing the data (dot position) is representative of the data value itself.

Example of chart using a dot plot to make comparisons

A scatter chart using dot plots to show comparisons between the values.A scatter chart using cross plots to show comparisons between the values.

You can also show small differences between data by adjusting the deviation. This is changing what data can be seen from a chosen value (the deviation section has more information).

Example of chart showing deviation between categories

A bar chart showing the deviation between categories, with the UK average equal to 777.5

Deviation

To show:

  • number of times more than the average
  • the difference from 

Use a bar chart to plot deviation from a fixed value, or series of values.

Example of deviation where the value of data is most important

Gross disposable household income (GDHI) per head (£)
England, 2011

A rotated bar chart showing deviation where the value of data is most important, with an annotation to compare England as a whole with individual English regions.

Example of deviation where the amount of change is most important

GDHI per head index comparison with England average
England, 2011

A rotated bar chart only showing the deviation of individual English regions from the average from the whole of England.

Use small multiples to plot deviation for multiple series. The axes should be identical for each small multiple.

Example of deviation with small multiples

A small multiple chart showing deviation for multiple series over time, with an individual chart showing the deviation from the England average for 2001, 2002, 2003 and 2004.

Distribution

To show:

  • frequency
  • distribution
  • profile
  • range
  • concentration
  • normal curve
  • population pyramid
  • shape

For one variable

Use a histogram to show a distribution of data. Use small gaps between the bars to emphasise the profile of the data.

Example of histrogram chart showing one variable

Usually resident population aged 0 to 21 years
UK, 2013

A histogram with narrow bars that are close together to show the difference in ages in the usually resident population of the UK in 2013.

For two variables

Use a population pyramid to show the distribution of comparable data sets and highlight differences in the profile of the data.

Example of population pyramid showing two variables

A population pyramid with narrow bars that are close together to show the differences between the data for males and females.

For more than two variables

To compare four variables population pyramids can be overlaid, with the least important data set displayed using an outline pyramid instead of bars.

Example of population pyramid showing multiple variables

A population pyramid with narrow bars that are close together to show the differences between the data for males and females, with a bar for 2010 and a line for 1995.

Small multiple charts can also be used for multiple distributions. Use the same scale to enable comparison across charts.

Example of small multiples chart

A small multiple line chart showing the differences in the distribution between countries.

Box-plots can also be used to compare distributions with two or more variables.

Example of box plot

A box plot chart to show the distribution through boxes and confidence intervals.

Correlation

Correlation charts are often associated with causality and they should be used with caution.

Correlation can show:

  • increases with
  • relates to
  • changes with
  • varies with
  • caused by

Anscombe’s Quartet

Anscombe’s quartet is a powerful illustration of the drawback of relying solely on basic descriptive statistics to summarise data. The data in all four of the graphs in the quartet are virtually identical when using standard descriptive methods. Looking at your data before analysing it is something that Anscombe was passionate about.

Example of Anscombe’s Quartet

A scatter and line chart showing an example of a simple, linear relationship between data.A scatter and plot chart showing an example of a non-linear relationship between data.

A scatter and line chart showing an example of a mostly linear relationship between data, with one outlier.A scatter and line chart showing an example of where there doesn't seem to be a relationship between the data, other than one high outlier.

We are constantly improving based on research and best practice. Any significant changes to our guidance are available on the Updates page.

Chart titles

Label charts as a figure and number them in order. Figures should have a main title and a statistical subtitle. Titles and subtitles should be concise and in sentence case.

Main title

The main title should be descriptive, and tell the trend of the data or highlight the main story. Try to limit the number of words to no more than 10. This should make the description easier to read and avoid the text wrapping onto several lines, especially on mobile devices.

If you need to add context or detail to the chart, use annotations or support with your analysis.

Statistical subtitle

The statistical subtitle should be as short as possible and must include the:

  • statistical measure
  • geographic coverage
  • time period

You do not need to include these elements in the subtitle if they are already in the main heading.

Writing chart titles to support your analysis

When writing your chart title and analysis:

  • use chart titles to complement or build on, but not repeat section headings
  • add further context and explanation of the chart’s message in your main text
  • do not try and summarise everything the chart says in the title, but prioritise the main message

Take care not to use language in a title that you would not use in your analysis. Exaggerated language such as “greatest rise ever” may be more eye-catching, but use sparingly as it may appear sensationalist or could potentially be misinterpreted.

It can be useful to draw attention to a record level being recorded in the most recent data, but if a new record continues to be set every month, using the same title will lose its impact. Use sparingly and find another message to concentrate on instead.

Examples of how to write chart titles and subtitles

A line chart showing the gender pay gap fell to 8.6% among full-time employees in 2018.

A line chart showing that motor trades continued a decline seen over the past 2 years to July to September 2019.

Line chart showing crime rates that homicides have increased over the last four years to year ending June 2018, following a long-term downward trend.

Your title can refer to a shorter period than shown on the chart. You can highlight an important short-term trend and give broader context by using a longer timeframe in your chart and analysis.

If your chart has more than one message

If a chart has more than one narrative, choose the one that will be most relevant to users for the main title. Use annotations to draw attention to secondary messages, but do not try and explain every nuance in the chart when your analysis can provide more detail.

Example of how to use annotations to draw attention to secondary messages

Line chart showing that the number of police recorded offences involving firearms has decreased in the latest year to year ending June 2018.

Titles for other visual elements

Other types of visual content can communicate information. If you are using a flow chart or a map, the same titling principles apply. Use a descriptive title to tell the user what the story behind the image is, and use a statistical subtitle if appropriate.

Example of how to write a descriptive title for visual content

An interactive data visualisation map showing that disposable incomes tend to be highest in the South East between 1997 and 2017.

Sometimes a graphic may genuinely be one you wish your user to explore – there is no immediate story or message on display. For example, some of the interactive graphics coming from the Data Visualisation team may be in this space. In these rare cases, it is acceptable to use a title that encourages the reader to explore the graphic.

Example of how to write a title for an exploratory tool

A data visualisation explorer tool that allows users to explore how well-being ratings have changed in their area.

Data order in tables

Group the data into meaningful subsets and make it clear in what order it should be read. Hierarchy and grouping can be shown by using white space and indenting column headings.

A table grouping together data for sub-regions of the UK underneath headings for England, Wales, Scotland and Northern Ireland.

A table is made up of classification variables and data values, either of these can be used to order your table depending on the context.

If the table is not ranked by data value and the classification variables have a natural order, like age or geography, keep this order in the table.

Additional guidance is available on the recommended standard presentation order of statistics.

Put the variables that are most likely to be compared in columns, with the units, tens or hundreds beneath one another.

Where data are most likely to be compared between years

Example of how to label a table with years written down the left-hand column and ages across the rows towards the right-hand side.

Where data are most likely to be compared between ages

Example of how to label a table with ages written down the left-hand column and years across the rows towards the right-hand side.

Time should run from left to right or top to bottom.

Number rounding

Always use a consistent level of precision, but use the lowest level possible for the intended user.

For the “inquiring citizen”, that is, a broader, less statistical audience :

260,000

For the “information forager”, for example, a local politician making decisions about future council tax charges:

264,300

For the “expert analyst”, for example, in a statistical journal discussing the detailed methodology behind the estimation, or in a situation where reproducibility is important:

264,337