Learning data science with COVID19

I confess: during these days of lock down, I have toying with dashboards and infographics about COVID19 outbreak, just like everyone else. And I am not ashamed to admit that I am proud of the results I have achieved: I have made an interactive dashboards with nice plots, some formatted tables and even some (basic) maps. Everything with a few lines of code¹. And yet, it is flawed. And yet, I will keep improving it. And yet, I am afraid it will always be flawed and, therefore, it will never be useful. Just like everyone else’s (or almost). But I know it is not me, it’s the data.

Why, then, am I persisting on keeping working on it if I know I cannot change its fate? A short answer could be “I do it because I can”, but that would not be completely honest. Admittedly, at some point I asked myself that very question and I even considered quiting. Not only I didn’t want to lose my time (even in these days where we are locked down at home there are plenty of things we can do), but I didn’t want to contribute to generate noise, missinformation and even more dramatism ti an already important drama. Because that’s what flawed graphics do. But in the end, I realised that working on a dashboard like that could be a great opportunity of learning by doing.

Starting from the most obvious: at a personal level, I have learnt a big deal of new techinical skills that I didn’t have just a week ago. To be more precise, I have learnt:

1. To create a nice dashboard with some interactions within a single rmd file using R's flexdashboard
2. To host and deploy the dashboard using github pages, just by committing and pushing to my github’s repo
3. To use plot.ly for interactive plots instead my good old ggplot2, which I have come to love. This is something I had wanting to do for quite a long time but I had never had the chance nor the time for testing it.
4. To create nice interactive tables in order to make them more readable.

Something here