At least from a historical perspective, dplyr introduced the `%.%` at a time when Hadley was not aware of the existence of magrittr; later, it became clear that the work had already been done, so dplyr was retrofitted to use magrittr and %>%, which was easier to type because you don't have to let go of the shift key..

That said, the hoops you have to jump through when interfacing with stock R code and dplyr make me think that having an operator that is less generic than `%>%` makes sense -- `X %>% foo()` is equivalent to `foo(X)`; except if it isn't -- `X %>% plot(x~y, data=.)` is not equivalent to `plot(X, x~y, data=.)`. And sometimes you need tricks like `X %>% { plot(.$x, .$y) }` to work around the nonobvious behaviors.

I find most impressive the work that this project did to emulate the lazy evaluation that is a hallmark of dplyr. I wasn't aware that python had anything like this ability.

Well Python and Panda has been making an R equivalent for a while now, Pandas. Pandas and other work has made a Python a good choice for doing data science work (Though I prefer R and started in Python years ago). The Pandas and Python Data Science developers have been awesome. Now the Python Community not so much (In regards to flaming R).