| 02 May 2017 |
Ranking Plots: Illustrating Data with Different Magnitudes
A Ranking Plot, also known as a Rank Flow Plot, is particularly useful for comparing data that differs in magnitude.
Interesting information can be hard to find when data differs in magnitude
The table below show causes of death by age in the USA for 2014. We can easily see from the table that death is most common among the elderly. Not such a helpful conclusion. The real story has been overwhelmed by the higher magnitude of death rates among the elderly.
A more insightful way to look at the data is in terms of relative differences in causes of death by age. The differences do not leap out at you when you look at this table. It is precisely for data like this, that the Ranking Plot is excellent.
A Ranking Plot quickly highlights the differences
The Ranking Plot below allows us to quickly see lots of interesting results that would have taken a long time to extract from the complex table.
Reading across the top row of the Ranking Plot we can see how the main causes of death vary until 45 years of age. After 45 years of age coronary heart disease dominates the statistics. We can also see that the age of 45 is the key change point. Before that, misadventures of various kinds play a prominent role. After that, the coronary heart diseases is the number one cause of death.
If you want to play around with the cause of death data and the Ranking Plot used to create them, you can do so here.
Author: Tim Bock
Tim Bock is the founder of Displayr. Tim is a data scientist, who has consulted, published academic papers, and won awards, for problems/techniques as diverse as neural networks, mixture models, data fusion, market segmentation, IPO pricing, small sample research, and data visualization. He has conducted data science projects for numerous companies, including Pfizer, Coca Cola, ACNielsen, KFC, Weight Watchers, Unilever, and Nestle. He is also the founder of Q www.qresearchsoftware.com, a data science product designed for survey research, which is used by all the world’s seven largest market research consultancies. He studied econometrics, maths, and marketing, and has a University Medal and PhD from the University of New South Wales (Australia’s leading research university), where he was an adjunct member of staff for 15 years.