Ooops.

I learned a new thing about R!

That is not at all what I’d intended. I wanted to compare two related datasets, each a column in a separate data frame.

Here’s a reproducible example.

Files and memory

I’m still working with that 6TB of data, and likely will be for a long time. The data are divided up by time: full spatial extent for a few time periods. The research group would also like to have time series for a particular location, but I can’t load all the data into memory. My current approach is to load a dataset, then save it as smaller chunks (expect more on file formats and save/load options later). The chunks are small enough in spatial extent that I can open them all and assemble a time series.

Looping over a large number of files in R, doing things with them, then writing them out again can lead to slow memory leaks, even if files are over-written. Hadley Wickham talks about memory management in R in Advanced R. I spent some time poking around with the pryr package, just out of curiousity, but there’s an easier solution: stick all the heavy lifting into a function. As long as the function doesn’t return something that includes its environment, the memory is freed upon exit.

All the file handling (reading and writing) goes into the function.

Then the function is called for the full list of possible patterns.

No clean-up, no memory leaks. The operating system no longer kills my process every time I leave it.