Visualizing data has many uses. We often explore how charts can be used to convey data insights and tell stories. We talk less on this blog about how slicing and dicing data helps us form impressions about the structure of the data sets we're analyzing.
I have been digging around some payroll employment data recently. (You can find the data at the Bureau of Labor Statistics website.) I thought the following two charts are quite instructive.
The first one surfaces one type of recurring patterns: there is a seasonal pattern running from January to December that repeats every year. I use a small-multiples setup, with each chartlet indiced by year.
The second chart shows a different kind of regularity: there is a cyclical pattern running from 2002 to 2012, no matter which month we're looking at. Again, we have a small-multiples setup, this time with each chartlet indiced by a month of year.
This second chart is a simple form of "seasonal adjustment". The data used in this plot are unadjusted. The chart shows that there is a larger cyclical pattern during the period of 2002-2012 that affects every month of the year.
I already hear grumbling about using a line chart when there is no continuity from one dot to the next. In this chart, in fact, time runs left to right, top to bottom, then starts again at the first chartlet, and so on. This is a profile chart. As the name suggests, we should be focused on the shape of the line. It doesn't have to have physical meaning; we are only looking for regularity.
Statisticians love to find this kind of regular patterns because they are easy to describe. Of course, most data are much messier.
Please comment on the article here: Junk Charts