ddply returning too many results

For some reason I'm getting more results than I expected since the upgrade to R-2.13.0 - and the upgrade to plyr_1.5.1.tar.gz... I tried this on an old version of plyr (version unsure unfortunately as I've just overwritten it...)

library(plyr)
dd <-data.frame(matrix(rnorm(216),72,3),c(rep("A",24),rep("B",24),
  rep("C",24)),c(rep("J",36),rep("K",36)))
colnames(dd) <- c("v1", "v2", "v3", "dim1", "dim2")

results1 <- ddply(dd, c("dim1","dim2"), function(df) c(m1=mean(df$v1)) )
results2 <- ddply(dd, c("dim1","dim2"), function(df) { c(m1=mean(df$v1),
    m2=mean(df$v2)) } )
results3 <- ddply(dd, c("dim1","dim2"), function(df) { c(m1=mean(df$v1),
    m2=mean(df$v2), m3=mean(df$v3)) } )

I don't understand why results 2 has twice the number of rows in results1 and results3 has three times as many - where the original results1 is just replicated twice or three times.

I had a handy copy of R version 2.11.0 Patched (2010-05-01 r51907) using an old version of plyr the results I was expecting were...

> results1
  dim1 dim2          m1
1    A    J  0.07312783
2    B    J -0.22428746
3    B    K -0.44205832
4    C    K  0.21421456
> results2
  d开发者_运维百科im1 dim2          m1         m2
1    A    J  0.07312783 -0.1130148
2    B    J -0.22428746  0.4394832
3    B    K -0.44205832 -0.1934018
4    C    K  0.21421456 -0.0178809
> results3
  dim1 dim2          m1         m2          m3
1    A    J  0.07312783 -0.1130148 -0.03175873
2    B    J -0.22428746  0.4394832  0.21581696
3    B    K -0.44205832 -0.1934018 -0.28313530
4    C    K  0.21421456 -0.0178809 -0.21948430

The results I get from R version 2.13.0 (2011-04-13)

> results1
  dim1 dim2         m1
1    A    J -0.2270726
2    B    J  0.5860493
3    B    K -0.5986129
4    C    K  0.3135809
> results2
  dim1 dim2         m1          m2
1    A    J -0.2270726 -0.19037813
2    B    J  0.5860493 -0.05385395
3    B    K -0.5986129  0.29404095
4    C    K  0.3135809 -0.26744010
5    A    J -0.2270726 -0.19037813
6    B    J  0.5860493 -0.05385395
7    B    K -0.5986129  0.29404095
8    C    K  0.3135809 -0.26744010
> results3
   dim1 dim2         m1          m2          m3
1     A    J -0.2270726 -0.19037813 -0.20448734
2     B    J  0.5860493 -0.05385395 -0.11190857
3     B    K -0.5986129  0.29404095 -0.27072101
4     C    K  0.3135809 -0.26744010 -0.03184949
5     A    J -0.2270726 -0.19037813 -0.20448734
6     B    J  0.5860493 -0.05385395 -0.11190857
7     B    K -0.5986129  0.29404095 -0.27072101
8     C    K  0.3135809 -0.26744010 -0.03184949
9     A    J -0.2270726 -0.19037813 -0.20448734
10    B    J  0.5860493 -0.05385395 -0.11190857
11    B    K -0.5986129  0.29404095 -0.27072101
12    C    K  0.3135809 -0.26744010 -0.03184949

why has results2 got 8 rows instead of 4 and results3 got 12 rows instead of 4?

Thanks, Sean

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This will be fixed shortly in plyr 1.5.2

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It's the c() function inside your ddply() that's causing the problem.

Here are three alternative ways that you can write your statement for results3, progressively getting simpler:

1. Use data.frame inside your function:

   ddply(dd, c("dim1","dim2"), function(df) {data.frame(m1=mean(df$v1), m2=mean(df$v2), m3=mean(df$v3)) } )

2. Use summarise:

   ddply(dd, .(dim1, dim2), summarise, m1=mean(v1), m2=mean(v2), m3=mean(v3))

3. Use numcolwise.

   ddply(dd, .(dim1, dim2), numcolwise(mean))

In each case the results are what you would expect:

  dim1 dim2          m1         m2          m3
1    A    J -0.04272659 -0.1468376  0.17902942
2    B    J -0.10133503 -0.1427358 -0.05241214
3    B    K  0.29698847 -0.0989732  0.14422812
4    C    K  0.04108324  0.2014864 -0.15893221