title: Logistic regression in R with millions of observations and rare events
body: <p>I'm working with a large data set of ~ 15 million observations in R. I'm trying to run a logistic regression to predict a binary dependant variable ("HasShared").
  As the event of sharing is very rare (less than 1%), I triedto use the logistf regression in order to handle the rare events issues. I have 48 variables in my data set, only 6 of them should participate in the regression. I have extra RAM as I'm working on a virtual machine on Google cloud platform, and changed my RAM to be around 100 GB.
  When I'm trying to run the logistf model - the run never ends. I waited for more than 2 hours and it didn't finish. So this seem infeasible.
  I read that there's a package which supposed to handle regressions of very large datasets (biglm), but I'm looking for a solution that will both handle my large dataset AND the rare event issue. Is there such a combined solution?</p>