I'm attempting to dig out some metrics that look at how reliably clients connect to a service.
The raw data is in the form of "client A, came online|offline at time X". The connection is highly unreliable, and I want some type of moving average to show whether the connection is improving or not over time. Clients are not always connected, so simply going offline does not mean it's a fault.
So far, I've taken then data and applied some assumptions to help simplify it, I assume that if a client reconnects within a minute of disconnecting then that is a fault. These I've modelled as a simple impluses, ie. "client A had fault at time X".
The part I'm struggling with is how to turn this plot into a moving average (I'm playing with R to crunch the numbers).
I believe I should be able to do this with a low pass filter, or use the zoo package and rollmean. However, I don't know how to handle the cases where the client simply didn't want to be online.