Differential privacy is an area that makes some guarantees about not letting personal information about individuals escape. Might be a useful technique as well.
We've tried using docker to put R applications into production. I'm no docker expert, and I found it pretty easy to do.
A couple of pointers:
- If you have a dependency on a library like blast that takes a long time to compile, you can make a base docker image that already has that library installed. That makes iteration quicker, as you'll only need to build that base image once.
- If you put a web interface on the image using shiny, then it is straightforward to deploy it for your users to interact with.
That is exactly the method they use. Calculate the centre of economic gravity, then project it onto the closest point on the surface of the globe.
Here is a nice analysis that does the same process in a more intuitive way. It calculates the centre of economic gravity in 2D map coordinates. This gives results that are easier to understand, because everyone's used to looking at 2D maps.
It might be better for the upper map to select a colour scale that has a zero point on it - this would represent countries that have the same price as the dollar.
A diverging colour scale would make it easier to distinguish which countries are cheaper and which are more expensive. For example:
Note that this doesn't apply retrospectively to your data, so it will only apply to data collected after you make a change. You might want to create an advanced segment which filters out these domains, or only includes the ones that you want.
https://en.wikipedia.org/wiki/Differential_privacy
Agree that strong guarantees about privacy aren't achievable.