Tricks to manage the available memory in an R session

Question

What tricks do people use to manage the available memory of an interactive R session? I use the functions below [based on postings by Petr Pikal and David Hinds to the r-he

Answer 1

1. I'm fortunate and my large data sets are saved by the instrument in "chunks" (subsets) of roughly 100 MB (32bit binary). Thus I can do pre-processing steps (deleting uninformative parts, downsampling) sequentially before fusing the data set.

2. Calling gc () "by hand" can help if the size of the data get close to available memory.

3. Sometimes a different algorithm needs much less memory.
   Sometimes there's a trade off between vectorization and memory use.
   compare: split & lapply vs. a for loop.

4. For the sake of fast & easy data analysis, I often work first with a small random subset (sample ()) of the data. Once the data analysis script/.Rnw is finished data analysis code and the complete data go to the calculation server for over night / over weekend / ... calculation.