Rendering graphics typically takes R some time, so if you’re going to be producing a large number of similar graphics, it makes sense to leverage R's parallel processing capabilities. However, if you’re looking to collect and return the graphics together in a sorted object – as we were in the previous post on animated choropleths – there’s a catch. R has to keep the whole object in random access memory (RAM) during parallel processing. As the number of graphics files increases, you risk exceeding the available RAM, which will cause parallel processing to slow dramatically (or crash). In contrast, a good, old-fashioned sequential for loop can write updates to the object to the global environment after each iteration, clearing RAM for the next iteration. Paradoxically, then, parallel processing can take longer than sequential processing in this situation. In the case of the animated choropleths in the previous post, parallel processing took 21 minutes, whereas sequential processing took 11 minutes.
This post presents code to combine the efficiency and speed of parallel processing with the RAM-clearing benefits of sequential processing when generating graphics.Read More