Charts (bar charts, histograms, pie charts, graphs)
3.6 Issues with pie charts
CHARTS (BAR CHARTS, HISTOGRAMS, PIE CHARTS, GRAPHS) 55 Essentially, Figure 3.24 puts a magnifying glass on the area of Figure 3.23 that is the most interesting. The aim is clearly met and the users can understand that the people committing property offences are much more likely to reoffend than other offence groups.
Principle 3.7: Ensure that a pie chart is the most appropriate type of chart to meet the aim.
Consider the top segment in Figure 3.25: is that above or below a quarter of the pie?
It is actually just less than one quarter – but hard to see from this chart. So what is the aim of the chart? If it is to monitor the reduction in car usage to get to work, it would be appropriate to have that segment starting at the 12 o’clock position and going clockwise. If the aim is from the bus transport authority and they want to monitor the uptake of bus transport to work, it would be appropriate to have the bus segment starting from the 12 o’clock position. Let’s just see the latter in Figure 3.26.
Figure 3.26 Starting at 12 o’clock.
Bus
Car Train
Bicycle Other
It is now easy to see that the bus usage is just under a quarter – the message one wants to give. However, if the changes over the time periods of measurement are small, that is, of the order of one or two per cent, then another pie chart side by side with this one would not allow the user to distinguish adequately any change – and the producer would have to think of some other way of presenting the information (e.g. going back to a table of changes?).
In the literature, an alternative start for the segments, at the 3 o’clock position, is sometimes suggested. For me – and the majority of users – a start at the 12 o’clock position is easier to measure from and interpret. This is because all of us are used to looking at a clock and measuring time past or to the 12 o’clock position.
It is clear that the aim should drive the chart’s design and bring clarity to the pre- sentation of data. If the chart is produced without an aim, it will not generally be productive in communicating a message.
Principle 3.8: Always start the main segment of the pie chart at the 12 o’clock position, progressing clockwise.
The second issue is the sorting of the segments of the pie chart. If one is looking at the policy of travel to work and seeking to write a report on the current position
CHARTS (BAR CHARTS, HISTOGRAMS, PIE CHARTS, GRAPHS) 57 and then describe policy to change the position, it would be appropriate to produce a chart with the aim of showing the current position in a pie chart. For this aim, the data would be sorted by size with the largest segment being the first from the position of 12 o’clock. Figure 3.27 illustrates this: note that the percentages of the various modes are included – these help the user to distinguish the actual data where segment sizes are similar (like for train and bicycle).
Figure 3.27 Sorted data.
Car, 58%
Bus, 23%
Bicycle, 8%
Train, 7%
Other, 4%
Sometimes it is not appropriate to sort the data by size because there is a more important ordering. For example, if one is looking at a certain characteristic by age, then it is more important to keep the data and ordering of segments in age order. Let us look at a distribution of the married population of England and Wales by age in 2010 (Figure 3.28).
Figure 3.28 Married population by age.
15 to 29 years, 4%
30 to 44 years, 27%
45 to 59 years, 33%
60 to 74 years, 27%
75 years and over, 9%
It would be nonsense here to order the segments by size as the order would be: 45 to 59 years, 60 to 74 years, 30 to 44 years, 75 and over and 15 to 29 years.
Principle 3.9: Where appropriate, sort the segments of the pie into size order.
The third issue with pie charts is the inclusion of depth of pie by adding a third dimension. Many times the reason for presenting pie charts in three dimensions is that they are ‘more realistic’ with depth and they ‘look better’. The difficulty here is that the third dimension actually makes it much more difficult to assess the relative sizes of the segments because of the angle of tilt and the blurring of the relative segment sizes by the additional depth in the front segments of the pie. Let me illustrate this with an example.
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Figure 3.29 Interventions.
First, 46%
Second, 12%
Third, 4%
Fourth, 19%
Fifth, 5%
Sixth, 14%
If we look at the pie chart in Figure 3.29 without looking at the data labels, the second intervention proportion of the pie looks considerably larger than the sixth intervention segment. In data terms, it is the other way round: similarly for the comparison between the third and fifth intervention segments.
In order to reinforce the argument, I have counted the pixels in each of the segments and set them, as percentages of the whole, in the same table as the actual data: these are shown in Table 3.2.
Table 3.2 Interventions.
Actual percentage Percentage of from data visible area
First 46 43
Second 12 22
Third 4 6
Fourth 19 17
Fifth 5 3
Sixth 14 9
So the eye leads the user to one conclusion but the wrong one! Comparing the actual percentages, it is clear that the segments to the front of the pie chart are given greater weight than those without any apparent depth.
Thus, when asking a user to judge the differences in data, do not ask them to adjust what is visible for tilt and depth when doing a comparison. In plain English, this would be expressed as keep two-dimensional charts in two dimensions so that the chart represents the data effectively and accurately.
Principle 3.10: Use only two-dimensional charts in two-dimensional media.
The fourth issue is the number of segments. Many presentations of pie charts try to get too much information across in the same chart. Figure 3.30 is a good example of how not to do one. Essentially, the producer has taken a table and converted the whole table into a pie chart. But what was the aim of the chart? If it really was to show each of the values, a bar chart or table would have been better. If the purpose was to show the relative sizes of Agriculture and Manufacturing segments, a pie chart with just four segments would have been better, combining everything from Financial activities to Other services into one segment, labelled ‘Other groups’.
One obvious issue in this chart is the labelling of the segments: the descriptions are long and lines to their segments cross text. The maximum number of segments should be 6 for clarity. If the data has more categories than this, consider grouping and recheck the aim. Another possibility, as in this case, is to do a bar chart instead – or just a simple table with one column of percentages (with the percentage symbol in the column heading, not against each number)!
Figure 3.30 Too many segments.
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Principle 3.11: The maximum number of segments in a pie should be 6.
The next issue is colour. Two aspects need to be considered. First, bright (primary) colours are dominant in the brain of the user. So putting primary colours in a chart can draw the user’s brain to them as opposed to giving equal consideration to each part of the chart. Let us re-present Figure 3.31 with a couple of primary colours in the segments.
Figure 3.31 Interventions with some primary colours.
First, 46%
Second, 12%
Third, 4%
Fourth, 19%
Fifth, 5%
Sixth, 14%
The user’s brain is drawn to the red and yellow segments and the ratio between the second and sixth segments appears to have changed, possibly because the brain is more focussed on the red and the top part of the second intervention segment, not its depth.
Principle 3.12: Avoid use of bold primary colours in charts.
The second aspect of colour use is where multicoloured charts are shown when, in fact, the categories are part of the same variable distribution. In the mode of travel to work chart (Figure 3.27), the categories are independent.
Whereas, in Figure 3.28, we are just looking at proportions of the same variable (age of the married population): and, in this case, the segments should be of different densities of the same colour. Figure 3.28 is one where the age classification has trumped the distribution and the same colour is used from deepest to lightest. In this case, as with all other charts, the producer must go back to the aim and check the
chart against the aim and ensure that the age distribution is more important than the proportion of the age group that is married.
Principle 3.13: Use shades of the same colour when presenting proportions of one variable.
If the data could be put into a bar chart, use different colours for the segments; if the data should be put into a histogram, use shades of the same colour.
As an example of where the age classification is not the most important, let us think of a chart’s aim to show which age group needs targeting with the messages of AIDS/HIV. In Figure 3.32, the chart has segments of the pie for each age group, their size relative to the numbers known to have HIV (the prevalence). The shading could then represent the incidence of HIV in the last year and the colour of the segments could be sorted on incidence – with the highest having the darkest shade.
Checking against the aim here, we need to ensure that it is more important to have the age ordering predominate rather than either the population in the segments or the incidence.
This and the next younger group would then be where the message targeting would be directed. The figure shows the 20–29 age group to have the highest incidence in 2011 so the advertising could be targeted at the under 20 and 20–29 age groups.
Figure 3.32 Prevalence of HIV in US by age, 2010, overlaid with incidence in 2011 (the darker the colour, the higher the incidence).
Under 20: 11,567
Ages 20-29: 82,223
Ages 30-39: 165,682
Ages 40-49: 318,398 Ages 50-59: 230, 304
60 & over: 83,684
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