50 Days Of Food
A short study in data visualisation
It’s been a few years since I played around with data visualisation for fun, and I recently completed a 50 day streak using MyFitnessPal. There is an export feature and a reasonably interesting data set to explore, so I decided to do a short experiment this week aiming to produce an interesting static infographic with no more than five hours effort.
I tried to strike a balance between creating something interesting of reasonable quality, without over-engineering and falling into the Data Exploration and Visualisation Vortex which has a powerful gravity.
The approach
Hour 1: Set up
It was easy enough to export the data from the app. It is well formatted for analysis, with proper date formatting and no real need for any cleaning. However, I was disappointed to learn that the export data only contains technical nutritional information about the food consumed, not the names of the food! This was disappointing as I wanted to do some text visualisations and look at the variety of my diet.
I spent some time looking at the data and determined I would focus on nutrition (not exercise or weight tracking) then set up a project in R and installed Photoshop.
Hour 2: Data exploration and visualising in R
I spent this hour playing around with exploratory plots and decided to focus on three hero visuals.
- A summary of the 200 meals, where I would try and cram as much info as possible into one visual
- Cumulative salt
- Energy consumed by day
As well as these visuals, I planned to take an assortment of facts and sprinkle them around the infographic. This ended up being overly-ambitious and I didn’t get this done, but I have a few ideas if I ever attempt this again.
Hour 3: More playing with the visuals in R and a quick look at Illustrator and photoshop
The thing about making charts in R, is that it is addictive and a potential black hole time sink. Maybe it’s just me, but one hour goes by really quickly. I used this time to play with the colours of my hero visual and export it to view in Illustrator and Photoshop. I did some Photoshop training many years ago which didn’t really help me at all. I tried Illustrator first but was using the wrong format. I learned much later in the week that R can export plots in .svg format which means very granular editing can be done in Illustrator, I can’t wait to try this another time.
Hour 4: Refining into the finished deliverable
At this point I knew I was going to need to reduce my scope, in fact I was feeling a bit overwhelmed about having chosen this experiment. I had my a-ha moment when I was able to layer the three visuals on a canvas in Photoshop and add a bit of text. I tried using a non-default font for the title (Questa Grande) and tweaked the colours to be complementary with my website. This was fun and the time absolutely rocketed by. No idea how my real designer friends do anything else with their time.
Hour 5: Polish
The final hour was spent writing this post as well as putting the data exploration work on GitHub. I resisted the urge to revisit the infographic itself, obviously there are a million ways to make it better, but overall I’m feeling pretty good about what I achieved. I also think spending time on chronicling the approach will be useful for my Future Self if I need to bang out some kind of bespoke static visual, and I hope you will find it interesting as well.
The result
Here it is, my five hour infographic:
Visual design is definitely not a skill of mine, but this is making me think about taking some time to develop a bit. It is very rewarding to get immediate visual feedback when working on something, as opposed to crunching loads of data where the payoff is greatly delayed.
My most important piece of feedback to myself is to bump everything up to make it easier to see at small screen sizes - I fell into the trap of designing in full screen and not really considering where this will be published.
What I learned
I learned how to do a few basic things in Photoshop - I prefer learning like this to following tutorials, although structured learning definitely has a place - I just need to get my hands dirty first. Now I know what I want to learn and why.
It was good to see how my muscle memory with R could perform under pressure. This is definitely my strongest skillset in this experiment, years go I would have spend five hours just to get the very basic plots represented.
I also learned a few things about my eating habits, which is obviously good -or else why analyse and visualise data in the first place? My meal times are very steady, which makes sense given the high amount of routine in my life - working international hours and helping to raise three year old forces that. My snacks are more all over the place, and some are early in the morning which is not great. My salt intake is pretty good, if I had more time I would have added the recommended thresholds and how I was performing against those. This is just one of the many ideas I had, I may revisit this data set in a few months for a 100 Day review.