Analysis of hamster wheel rotational data Before posting this question, I had a browse through other questions on this Stack, and this is probably going to be a walk in the park for anyone who reads it! But the description of this Stack includes data analysis and visualisation, so I hope this is in the right place!
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I'm using a Raspberry Pi with a magnet and reed sensor to record what my hamster gets up to at night. The Pi records each rotation as follows:
2014-09-04 00:20:04.987819,1
2014-09-04 00:20:16.219891,2
2014-09-04 00:20:17.260086,3
2014-09-04 00:20:20.031204,4
2014-09-04 00:20:20.907755,5
...

It's when it comes to doing something with the data that I'd like some help with. Here's an example of a daily analysis the Pi produces:

Hello daddy!
Last night, I got on my wheel at 00:20 and got off to go to bed at
  04:51 (with breaks of course!). My longest break away from the wheel
  was for 26 minutes from 03:05 - 03:31.
During the 4 hours and 31 minutes, I went round the wheel 11,903
  times! As the wheel is 18cm in diameter, this means that I travelled
  18π⋅11903/100 ≈ 6,731 metres. That's 4.18 miles!
Love Biscuit x

I've also produced a simple graph of the data, which looks as follows:

I've also thought of doing an hourly breakdown to see which times he's most active.
Given my non-existent statistical knowledge, that's all I can think of. Is there anything else I could be doing? (In terms of both analysis and visualisation). If it requires capturing additional data, that's fine.
Edited to show the start of actigraphy tracking

 A: In a lab setting this is very often done to study circadian (i.e. ~24 hours long) rhythms.
If you can record the activity for long periods (say a few days) you can do some periodicity analysis.
The usual representation of this type of data is the actogram, which also allows you to see whether there are day to day differences.
In this example after day 8 the mouse was put in constant darkness and, as you can see, although the rhythm is always there, it shortens revealing what is usually called the "free-running rhythm".
Note that this picture is particularly clean. Sometimes you do have activity during the day!

From: Measuring Circadian and Acute Light Responses in Mice using Wheel Running Activity - LeGates and Altimus, JOVE 2011
This papers gives a nice introduction on how to analyse wheel-running data:
A guideline for analyzing circadian wheel-running behavior in rodents under different lighting conditions - Jud et al. Biol Proced Online. 2005
You can also do some frequency analysis, for instance using chi-square periodograms.
See: The chi square periodogram: its utility for analysis of circadian rhythms. - Sokolove and Bushell, J Theor Biol. 1978 (sorry, behind a pay wall, but see also the refs here)
One thing you may do is to see whether these rhythms change during the week. For instance, I assume you wake up later (and make less noise) during weekends.
Does that affect the end of wheel running activity?
Do the rhythms change between summer and winter?  
Also, if you hook up a camera to the system you can do some motion tracking. Here is an example of "hamster tracking" using Python and OpenCV.
A: What questions would you ask about the data?


*

*How long did the hamster spend on the wheel last night?

*Was it longer or shorter than the night before that?

*Is there any day per week that the hamster runs longer on the wheel? Eg does a hamster have a week day or weekend day %^)?

*How long does the hamster stay on the wheel in a continuous stretch, on average?

*Does different type of food affect the time the hamster spends on the wheel?

*Do my activities change the hamster's wheel behaviour?


All of these would suggest different types of plots. Aggregate time on wheel by day, plot over days. 
See http://journal.r-project.org/archive/2013-1/hofmann-unwin-cook.pdf for how to plot data in different ways to explore different things, answer different questions. One plot is almost never enough.
