the first and last observation for each subject
library(plyr) # ddply is for splitting up data frames and combining the results # into a data frame. .(ID) says to split up the data frame by the
subject
# variable ddply(DF, .(ID), function(one) with(one, y[length(y)] - y[1])) ...
The above is much quicker than the versions based on aggregate and
plyr does make some optimisations to increase speed and decrease memory usage (mainly by passing around lists of indices, rather than lists of the original objects) but it's unlikely ever to approach the speed of a pure vector approach (although I hope to put some time into rewriting the slow parts in C to do better with performance).
easy to understand. Another approach is more specialized but useful when you have lots of ID's (e.g., millions) and speed is very important. It computes where the first and last entry for each ID in a vectorized computation, akin to the computation that rle() uses:
I particularly this solution to the problem - it's a very handy technique, and while it takes a while to get your head around how it works, it's worthwhile spending the time to do so because it crops up as a useful solution to many similar types of problems. (It can be particularly useful in excel too, as a quick way of locating boundaries between groups) Hadley