When dealing with large data sets, changing the shape and format of the data can be cumbersome.
Not everyone uses them, but much new R code makes use of pipes "%>%" . Elementary piping can be done
with dplyr, however there are more elaborate uses of them.
A central topic in Science is the reproducibility of research. This is not limited to wet-lab/field experiments
but code and analyses as well. To keep you code organized and easily presentable try:
Historically R was really bad at making interactive programs with GUIs. That's changed recently
R is reasonably good on the machine learning front, but gets less love than Scikit-Learn, Torch, etc. these
Writing short code bits is pretty straight-forward in R, but serious development is a different ball game