In my last post, I started cleaning some openly accessible data to look at whether we can see if there was geographical variation in Critical Care beds.
We ended up with a couple of choropleths which didn’t look that great.
I realise because of the continuous scale, it is quite hard to distinguish between the areas with high density of Critical Care beds vs. those with lower densities. Therefore I have decided to bin the Critical Care beds per 100,000 population into some arbitrarily selected ranges
Assuming we have already loaded the data we needed from the previous post, we just need to make some categorical variables from the continuous variable of number of Critical Care beds per 100,000 population using the cut() function.
This yields a 5 category scale, which should be easier for the human eye to distinguish.
Finally we replot the map.
I think this map is much better than the previous ones we had.