Re: [igraph] R versus Python

[Tamás Nepusz]

> Seems like Python might not object to nested for, plus it has the igraph 
> routines available.
> Anyone got any advice?
Just my two cents:

A "for" loop in an interpreted language (like R, Python, Ruby or anything like 
that) will never be as fast as a "for" loop in plain C, period. Python might be 
a bit better in looping than R, but I don't expect the difference to be very 
significant. Even in Python, efficient numeric computations usually rely on 
NumPy, which pulls the same vectorization trick to do things efficiently as R 
or Matlab does. The same trick is done in Python when one translates a "for 
loop" into a list comprehension: the looping in the list comprehension is done 
in the C layer, thus it is much faster than the equivalent for loop. However, 
since not all for loops can be converted into list comprehensions, there are 
cases where one simply cannot avoid using loops. I guess the same applies for R.

Depending on the task you are trying to achieve, it might be the case that it 
is not the "for" loop that is the bottleneck in your program but something 
else. My opinion is that since you are already familiar with the R interface 
(definitely more than with the Python interface), it might be better to profile 
your R code to see where the bottlenecks are. I don't know how profiling works 
exactly in R, but once you have identified which call(s) or loop(s) are taking 
the most amount of time, you will at least know where to focus your efforts on.

If your code is concise, you can also try posting it here in case some of the R 
experts on this list (not me :)) are able to spot some obvious opportunities 
for speedups.

Best,
Tamas