How to visualize common pattern of state changes over time? I have a process which checks workers in a service once a day.  When it checks them, they will have one of a few states:

*

*Unknown - Either brand new or the worker is offline

*Inprogress - When checked, an operation was blocking the machine

*Error - Something went wrong

*Complete - everything is perfect

*CorrectionWasRequired - everything is perfect but we had to do something to make it happen.

The data set looks like this:




machineID
StatusHistoryInOrder




First
Unknown,InProgress,Complete,Error,Error,CorrectionWasRequired,Complete


Second
Unknown,Complete,Complete,Complete,Complete...


ProblemChild
Unknown, Error, Error...


MissingMachine
Unknown....




Right now, they do not have the same number of data points but I can pad them if that makes it easier (repeating the last state X times until all are equal length)
The data points are in ascending timestamp order, so each represents one day, from oldest to newest.
My goal is to visualize and understand the most common sets of statuses that my devices end up in.
My questions are :

*

*Is this the right type of visualization to use?

*Is this the easiest approach to analyze my status histories to find the 5 most common patterns?

*Tips for rendering this?

I am trying to work up a ribbon view, like so.  In this mockup, I'm showing one machine only.


I was able to achieve this by transforming my data using a simple script.  First, I had to know that Sankey charts are made using just three columns, source, destination and weight.
So a chart to make a chart like this, showing how people move through countries for vacation:

I needed to convert the multiple states into just two columns.  So the key was to add a number for the iteration count that mattered, like so:

I just did the same thing to my data.  Thanks to @underminer for pointing out the type of chart to use, which lead me to my answer.
