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[Exploring my physical activity data: Step 3] What can I learn from my 2019 physical activity data?

This is part 3 in a series of posts focusing on an exploration of my own physical activity data. You can read the whole series here. This time, I'll be looking at what I can learn from my data, how my physical activity data can be combined with other data sets and some more powerful data visualisations.  You can see the full datasets and interactive versions of the visualisations on Tableau Public.

The first thing that struck me from my initial exploration was that there seem to be a number of extreme values that relate to days when I'm outside of my usual routine. 

I looked back and categorised my data for each day in 2019 as working days, weekends, holidays and travel for work days. As you can see from the animation below: workdays have a fairly tight distribution with most values falling from 4,000–8,000 steps; weekends are very lazy at 0-4,000 steps; holidays show a much wider and higher spread of values than a usual working day; and travel for work sees the widest spread of values, as well as the highest value at 22,380 steps. This extreme value could have had something to do with me travelling the the UK to Australia that day, so perhaps Google captured data from more than 24 hours, but categorised it all as 4th July as I was passing through several time zones. 





If we take a closer look at the graph above and focus just working days and weekends, we can see that there is a lot of low-hanging fruit to improve my daily steps. This is mostly due to the lack of commute, which includes about 25 minutes of walking each way. 


So, judging by this visual I should try to up my physical activity on weekends, as the majority of my below average days in 2019 fell on a weekend. I think this view is slightly inaccurate, as I am often not as tethered to my phone during weekends, and will often leave my phone on a table while I'm walking around the house, or out in the garden. In my next post I'll look at how my physical activity may have been affected by the weather and also talk about surprisingly how hard it seems to be to find rainfall data for Manchester! 


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