Appendix G. Working with large datasets

R holds all of its objects in virtual memory. For most of us, this design decision has led to a zippy interactive experience, but for analysts working with large datasets, it can lead to slow program execution and memory-related errors.

Memory limits will depend primarily on the R build (32 versus 64-bit) and for 32-bit Windows, on the OS version involved. Error messages starting with cannot allocate vector of size typically indicate a failure to obtain sufficient contiguous memory, while error messages starting with cannot allocate vector of length indicate that an address limit has been exceeded. When working with large datasets, try to use a 64-bit build if at all possible. For all builds, the number of elements in a vector is limited to 2,147,483,647 (see ?Memory for more information).