Efficient functional programming (using mapply) in R for a “naturally” procedural problem

Question

A common use case in R (at least for me) is identifying observations in a data frame that have some characteristic that depends on the values in some subset of other observa

Answer 1

The "most natural way" IMO is the split-lapply-rbind method. You start by split()-ting into a list of groups, then lapply() the processing rule (in this case removing the last row) and then rbind() them back together. It's all doable as a nested set of function calls. The inner two steps are illustrated here and the final one-liner is presented at the bottom:

> lapply( split(raw, raw$WorkerId), function(x) x[-NROW(x),] )
$`1`
  WorkerId Iteration
1        1         1
2        1         2
3        1         3

$`2`
  WorkerId Iteration
5        2         1
6        2         2
7        2         3

$`3`
   WorkerId Iteration
9         3         1
10        3         2
11        3         3

do.call(rbind,  lapply( split(raw, raw$WorkerId), function(x) x[-NROW(x),] ) ) 

Hadley Wickham has developed a wide set of tools, the plyr package, that extend this strategy to a wider variety of tasks.