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
A place for me to share my thoughts on science, data and the world