Verbosity of programming languages

It is not often that one sees the same functionality being implemented in more than one language outside toy examples and for mere bragging rights. However, the AI challenge's starter packages:

1. Each implement the same algorithm in different languages
2. With the same I/O infrastructure
3. Were not written intentionally to be better than the other (no bias)

So I thought it would be a good data-set to look at how verbose the languages were while controlling for the task at hand:

Lines of code it takes to implement the same functionality
in different programming languages.
(Red bars likely to be higher than shown)

Humble "FrozenSynapse" Bundle

Humble Bundle came back again, this time with just one new game FrozenSynapse and the previous FrozenByte bundle with it, if you paid more than the average.

This run of the Bundle was not as peppered with events as the previous bundles were: there were only two mid-air additions to the bundle, instead of half a score of interesting events happening the last time and nothing (to the best of my knowledge) was made open source:

Humble "FrozenSynapse" Bundle's economic performance

R: Going faster than vectorization

Introduction

Update: The by function of R can be used for the same task since data for me is stored in the same data.frame. I have tested that out later on in the post.


Recently I had to run though a large data.frame in R, selecting values from a given column based on an equality constraint on another column, in this format:

for(i in 1:length(values)) { 
  t <- my.var$row2[ my.var$row1 == value[i] ]; 
  do.something.interesting(t); 
}

This is a fairly common operation and I had thought that beyond the vectorization I had done (borrowed term from MATLAB), R would take care of optimizations under-the-hood.

At this point, I thought how I would do it on a larger database and it struck me that perhaps I can do better than R if I manually index the data and find relevant intervals myself. How much faster can that be?