In my home disciplines of economics and law, teachers need not worry much about reusing the same exam questions every few years, as both tend to change the answers regularly. In the same vein, I have noticed that many aspect of R programming and the R community evolve rapidly. I say this to justify my asking a question that I asked before, a few years ago, because I didn’t like the answer I got then, and I am hoping for a different one now.
I am writing a package that I hope will be widely useful to academics in the social sciences, students, and government officials, but the primary community I am hoping to serve consists of small public interest advocacy nonprofits, and maybe journalists. It is possible that somewhere down the line, after it is finished and published (I hope in the next six months) I will be seeking grant funding for outreach and training and the like (not for programming which will already have occurred). At that point, it would be very helpful if I could produce at least a very rough approximation of the total number of people who have downloaded it from all the CRAN mirrors – not the raw number of downloads, which I understand to be much higher than the number of downloaders, and not the downloaders from a single mirror, which I would expect to be much smaller than the downloaders from all mirrors. Though if I had estimates of total downloads of a package from a single widely used mirror like CRAN or RStudio, the approximate ratio of downloaders to total downloads for the site, and the ratio of total downloads from that site to total downloads from all U.S. mirrors, I could use total downloads of the package by the other ratios as a somewhat defensible estimate.
So my question is, has anyone done this, i.e. produced an estimate of downloaders for any package? If so, could someone point me to it or describe the methodology? Or suggest an alternative methodology to the one I suggest above that would be more feasible, given the data that I could in principle lay my hands on? Thanks!