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