Jan T. Kim wrote:
Generally, I fully agree -- modular coding is good, not only in R. However, with regard to execution time, modularisation that involves passing of large amounts of data (100 x 1000 data frames etc.) can cause problems.
I've just tried a few simple examples of throwing biggish (3000x3000) matrices around and haven't encountered any pathological behaviour yet. I tried modifying the matrices within the functions, tried looping a few thousand times to estimate the matrix passing overhead, and in most cases the modular version run pretty much as fast as - or occasionally faster than - the inline version. There was some variability in CPU time taken, probably due to garbage collection.
Does anyone have a simple example where passing large data sets causes a huge increase in CPU time? I think R is pretty smart with its parameter passing these days - anyone who thinks its still like Splus version 2.3 should update their brains to the 21st Century.
Baz
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