I recently had to loop through 1.3Gb of data (5000 files) and merge just one column from each file into a new dataset. It did so in ~2 hours. Yet the loop was just ~5 lines of code.
I wonder if you tried doing things like:
* preallocate a list, then do.call(cbind, your_data) * Same as above, but with some of the faster alternatives to cbind like dplyr::bind_cols or data.table::cbind * Use data.table, which has far faster joins than base R (so does dplyr) if you were doing a true merge/join
If it was truly just adding a column rom each file together into a file, these kinds of tasks are much better using UNIX tools, in my experience.
Another example is the immutable structure that causes R to be a memory hog. Creating copies of data everywhere. But, again if you plan well and execute the 'best' solutions you can avoid the giant pitfalls but will rarely ever beat a equally well written python equivalent.