You should not use split in production code

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Recently I have stumbled on a problem with split function applied on list of factors. The issue is that it might produce wrong splits when splitting factors contain dots.
Here is the example of the problem. Invoking the following code:

df <- data.frame(x = rep(c(“a”, “a.b”), 3),
                 y = rep(c(“b.c”, “c”), 3),
                 z = 1:6)
split(df, df[,3])


    x   y z
1   a b.c 1
2 a.b   c 2
3   a b.c 3
4 a.b   c 4
5   a b.c 5
6 a.b   c 6
[1] x y z
<0 rows> (or 0-length row.names)
[1] x y z
<0 rows> (or 0-length row.names)

And we can see that incorrect splits were produced. The issue is that split uses interaction to combine list of factors passed to it. One can see this problem by invoking:

> interaction(df[,3])
[1] a.b.c a.b.c a.b.c a.b.c a.b.c a.b.c
Levels: a.b.c a.b.b.c a.c

The problem might be not a huge issue in interactive mode, but in production code such behavior is a problem. There are three obvious ways to improve how split works:

  1. Rewriting split internals to avoid this problem;
  2. Allow passing  sep parameter to split that would be further passed to  interaction;
  3. Warning if resulting number of levels in combined factor does not equal the multiplication of number of levels of combined factors (assuming drop = F option).

Until this issue is solved there is a workaround using split and two other options using by and dlply (from plyr package):

split(df, lapply(df[,3], as.integer))
#Alternative 1
by(df, df[,3], identity)
#Alternative 2

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