Yeah, data.table provides already significant speedup vs dplyr - so much that the "better" syntax of dplyr makes no sense anymore when you have to deal with very large datasets. But maybe FastR can somewhat change that?
I spend hours cleaning up data and only have to run the code once (I normally save the output to a feather and then work with a separate file from there).
I still believe that the 'tidyverse' is hands down the best thing that has happened to R and is the whole reason why R has grown so fast.
In the event that the data doesn't fit into memory, it's better to preprocess w/ SQL at the data-store level. There hasn't been a case where I'd need to feed massive amounts of data into a ggplot2 visualization unaggregated.