@daroczig and I started working on our logging packages around roughly the same time without being aware of each other and we had some discussion on this in the past.
It's true that there is an obscene amount of logging packages for R now. Most of them are ports of the basic idea behind log4j/python logging. I might be biased here but I feel lgr is the most complete python-logging clone yet.
This happened more or less by accident. Usually I would add a feature, revise it a few times, and then realize the final solution is the same path python logging choose. So at some point I decided just to go for it and use the python logging doc as design inspiration. The plus of this is that there is a lot of documentation/howtos/best practice guides for python logging that apply 1:1 to lgr (if you wrap your head around the differing terminology)
lgr has a pretty intuitive support for arbitrary data fields in log events, so it's a good choice if you want to produce machine readable logs (which you is something that you should want). The R6 approach makes pretty complex Appenders possible and provides a pretty clear api for extending lgr (creating your own appenders and loggers).
If you don't need any of the more advanced features it doesn't really matter which logging package you choose. If you have already used python logging / log4j and are missing some of its features in other logging packages, chances are good lgr will have them (if not, file an issue).