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Personally I'm in love with R's data.frame. It allows very concise, robust and elegant manipulation and subsetting of a data set.

I wish every language would have such a built-in object type, I definitely feel its loss when I manipulate data in other languages such as Javascript or Mathematica.

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> Personally I'm in love with R's data.frame. It allows very concise, robust and elegant manipulation and subsetting of a data set.

The performance is terrible though. For data of more than ~10,000 observations SQL is much better performance wise, is more robust, and is as good at subsetting. Although it's maybe not as elegant for everyone's definition of elegant.

If you haven't tried it, the data.table package is great. Mostly like a data.frame (in fact a subclass), but fast.

What dataframe operations do you find to be slow? Usually I'm able to get huge performance wins by rewriting my slow R code in a loop free way (*apply and friends).

Python acquired that capability with Pandas in the last two years. Same nomenclature, tens of times faster.

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