Once again we found a chart worth mentioning here. It shows the self-assessment of women in different age groups regarding their risk of a heart disease according to a current study. At first sight we actually saw only a number of seemingly random columns. So we thought about a clearer representation.
In the first chart using clustered bars it is the viewer’s job to summarize and contrast the figures of the age groups. A stacked column chart seems to be better suited to convey the statistics. Apparently, older women have a far more negative view about their risk of a heart disease (a more realistic one according to the story). Visually adding up “high” and “medium” values by stacking and displaying connector lines seem to help a lot to understand the data.
Today we want to take a look at a real-life example of a chart. There may always be an alternative approach to display the same data and potentially make it easier to read and understand.
Here is a chart that we saw in a magazine which we reproduced using Aploris. It depicts the development of employee headcount in the German retail industry (March 2012 to March 2013, no source stated).
Is there a better way to display the data? First of all, we are seeing a composition of figures meaning that the total is the sum of the influencing factors. The chart does not help the viewer to understand this relationship. Next, one can see that the increase in part time jobs outweighs the reduction in full time headcount leading to a positive total change. However, it takes good vision and some deduction to develop this understanding.
In this case we would propose to utilize a waterfall chart that charting tools like Aploris support. Here is a how the same data can be used in a waterfall.
Now it should be very clear that a reduction of full time employees is more than compensated by increased part time workers leading to an increase in overall industry headcount.