I designed this chart to combine the benefits offered by a Bar chart and a Pie chart. Its closest known alternative is the Pareto chart. With this (new?) chart, the Line graph is replaced by a Waterfall chart, for two main reasons:
- It better reflects the discrete nature of the horizontal axis.
- It makes it easier to visualize the contribution of each value to the whole.
Also, the Bar chart is replaced by a Level chart in order to reduce the impact of visual collisions between the levels (the colored horizontal ticks) and the bars.
Much like the Pareto chart, this chart uses two vertical axes, one for the value itself, and one for the sum of values. Therefore, these two axes are congruent but use different scales. A more detailed explanation of its genesis is available on this article.
My question is the following: is there any prior art for this chart, and if so, what is it called? Right now, I call it a K chart, because it looks like the uppercase K
letter when values are sorted in decreasing order and the "All" bar is displayed on the right.
Benefits over Pie Chart:
- The display of labels and values is greatly simplified and much more space efficient.
- The horizontal axis can be epochal (temporal with an epoch, like dates).
- The layout can be rotated by 90° (in order to support a portrait output for example).
- There is a clear starting point for reading (left or right depending on language).
- There is a natural place to display the sum of values.
- There is a natural place to display deltas or rates of growth.
- The use of colors is perfectly optional (great for accessibility).
- The horizontal dimension can never be confused for a directional variable.
References:
- Principia Data (Unified Typology of Statistical Variables).
- Principia Pictura (Unified Grammar of Charts).
Appendix:
The K chart is nothing more than the superimposition of a Level chart and Waterfall chart:
Bonus Question:
Can we attribute any meaning to the point where the two legs of the K chart intersect? If we had a continuous probability distribution, this would be the solution $i$ to the following equation, with $f(x)$ being a monotonically decreasing function and $\alpha$ being the scaling factor between the two axes:
$\int _{0}^{i}f(x)\,dx = \alpha f(i)$
Of course, we can express $\alpha$ in relation to $f(x)$:
$\alpha = \frac{\int _{0}^{Max(x)}f(x)\,dx}{f(0)}$
Therefore, the equation we have to solve is:
$\int _{0}^{i}f(x)\,dx = \frac{f(i)\int _{0}^{Max(x)}f(x)\,dx}{f(0)}$
This is as far as I could take it...