From what I hear, this is a pretty common story whenever someone tries to use conda R. Theoretically, I'm sure it's possible to get it to work right, but I suspect it'd take a non-negligible amount of work that nobody who knows the requisite turf appears inclined to do.
If you can get it to do what you want without going crazy, great. Otherwise, it's worth asking why you want to use conda. If you want a programmatic way to install and update, there are more general package managers that can install CRAN R (built-in in Linux, Homebrew Cask on Mac, maybe choco on Windows). If you want package checkpointing to avoid conflicts (not that I've seen...any, really) using MRAN or pacman or similar may help. For self-contained environments, the rocker docker images are handy. If you want to enable R kernels in Jupyter, you don't actually need conda R for that.
Regardless of your installation, installing some things—e.g. RJava—will likely still be a pain. (It does enable some cool packages, though.)