## 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.

## Comparing data sets – shared horizons

Start data that are likely to be compared from the same point on a chart – a shared horizon. Use a clustered chart to compare values; only the first category is easily comparable in stacked bar charts.

### Do

#### Example of chart allowing easy comparisons

England and Wales, 1998 to 2012

### Do not

#### Example of stacked bar chart where comparability is more difficult

England and Wales, 1998 to 2012

## 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

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

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).

## Time series

Rather than over-emphasising month-to-month or point-to-point comparisons of estimates a time series can show:

• change
• trend
• fluctuation
• growth
• decline
• increase
• decrease

### Time series axes

Time should always run from left to right along the horizontal axis.

### Time series charts

A time series with regular intervals can be presented using line charts, bar charts or a combination of both.

## Bar charts for time series

Bars should be used to emphasize individual values at distinct points in time. Use them when data points are at equal intervals.

## Line charts for time series

A line chart will emphasise the overall pattern of the data and highlight trends. Use them when you have lots of data points or just a few. Multiple times series should always use line charts.

## Dot plot with line for time series

Use a dot plot with a line when there are lots of data points or the interval between data points is not equal. Show if data are irregular.

## Multiple time series

Multiple time series should not be presented using bar charts. Use a line chart to make sure the trends in the series are clear. Use points on a line to highlight individual data points, to read specific values or highlight when the data were sampled.

## Small changes over time, not starting the y-axis at zero

Time series charts do not have to begin at zero, if a chart does not start at zero this must be indicated by breaking the y-axis in an obvious way.

### Scale of the axes

A chart can tell a very different story depending on the scale of the axes.

This chart gives the impression that the measles, mumps and rubella (MMR) vaccination level has remained high and fairly stable.

#### An overview

MMR vaccination uptake at age 1 year
UK, 1992 to 2012

When the y-axis is altered a different picture emerges showing that the measles, mumps and rubella vaccination has dropped considerably since 1997.

#### A more focused view

MMR and Diphtheria vaccination uptake at age 1 year
UK, 1992 to 2012

You can use two charts with different axes scales to ensure that the data are represented without bias whilst highlighting the important message.

## Ranking

To show:

• greater than
• less than
• equal to
• from lowest to highest

Use bar charts to show data that are ranked, in either ascending or descending order. Horizontal bars should be used.

A bar chart should always be ranked by value, unless there is a natural order to the data (for example, age or time).

#### Example of chart with no ranking order

To highlight the highest values the largest value should be at the top of the chart.

#### Example of chart with a clear ranking order from highest value

Descending, largest values highlighted

To highlight the lowest values the smallest value should be at the top of the chart.

#### Example of chart with clear ranking order from lowest value

Ascending, smallest values highlighted

If you are talking about data in terms of first, second or third, or “the top 10”, they should always be in descending order.

#### Example of chart ranking the top 10 in descending order

Top 10 girls’ baby names
England, 2013

### Plotting a change in rank

Use a slope chart to highlight a change in rank.

### Ranking multiple series

Rank the most important or recent data if there are multiple series and the other data sets should be ordered correspondingly.

## Part-to-whole

### Part to whole relationships

To show:

• ratio
• percentage
• proportion
• share
• breakdown
• make up
• hierarchy

Bar charts and pie charts should be used to show part to whole relationships.

Pie charts should only be used when there are less than six categories, otherwise use a bar chart or, if appropriate, combine categories.

Rank the categories in a pie chart and start the first segment at the 12 o’clock position.

Segments of a pie chart must sum to 100%. If the categories do not sum to a meaningful whole, do not use a pie chart. Where appropriate categories can be combined to highlight a certain message but should never be removed.

#### Example of pie chart ranked by size of category

All main categories included

Religion categories combined

#### Example of incorrect use of pie chart with categories removed

No religion and not stated categories removed

If no categories are dominant use a bar chart to illustrate your data.

### Multiple part to whole

Use bars to enable comparisons to be made across multiple part to whole charts.

## 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

#### Example of deviation where the amount of change is most important

GDHI per head index comparison with England average
England, 2011

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

## 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

### For two variables

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

### 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

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

#### Example of small multiples chart

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

## 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

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