for each group summarise means for all variables in dataframe (ddply? split?)

Question

A week ago I would have done this manually: subset dataframe by group to new dataframes. For each dataframe compute means for each variables, then rbind. very clunky ...

Answer 1

Given the format you want for the result, the reshape package will be more efficient than plyr.

test_data <- data.frame(
var0 = rnorm(100),
var1 = rnorm(100,1),
var2 = rnorm(100,2),
var3 = rnorm(100,3),
var4 = rnorm(100,4),
group = sample(letters[1:10],100,replace=T),
year = sample(c(2007,2009),100, replace=T))

library(reshape)
Molten <- melt(test_data, id.vars = c("group", "year"))
cast(group + variable ~ year, data = Molten, fun = mean)

The result looks like this

   group variable         2007         2009
1      a     var0  0.003767891  0.340989068
2      a     var1  2.009026385  1.162786943
3      a     var2  1.861061882  2.676524736
4      a     var3  2.998011426  3.311250399
5      a     var4  3.979255971  4.165715967
6      b     var0 -0.112883844 -0.179762343
7      b     var1  1.342447279  1.199554144
8      b     var2  2.486088196  1.767431740
9      b     var3  3.261451449  2.934903824
10     b     var4  3.489147597  3.076779626
11     c     var0  0.493591055 -0.113469315
12     c     var1  0.157424796 -0.186590644
13     c     var2  2.366594176  2.458204041
14     c     var3  3.485808031  2.817153628
15     c     var4  3.681576886  3.057915666
16     d     var0  0.360188789  1.205875725
17     d     var1  1.271541181  0.898973536
18     d     var2  1.824468264  1.944708165
19     d     var3  2.323315162  3.550719308
20     d     var4  3.852223640  4.647498956
21     e     var0 -0.556751465  0.273865769
22     e     var1  1.173899189  0.719520372
23     e     var2  1.935402724  2.046313047
24     e     var3  3.318669590  2.871462470
25     e     var4  4.374478734  4.522511874
26     f     var0 -0.258956555 -0.007729091
27     f     var1  1.424479454  1.175242755
28     f     var2  1.797948551  2.411030282
29     f     var3  3.083169793  3.324584667
30     f     var4  4.160641429  3.546527820
31     g     var0  0.189038036 -0.683028110
32     g     var1  0.429915866  0.827761101
33     g     var2  1.839982321  1.513104866
34     g     var3  3.106414330  2.755975622
35     g     var4  4.599340239  3.691478466
36     h     var0  0.015557352 -0.707257185
37     h     var1  0.933199148  1.037655156
38     h     var2  1.927442457  2.521369108
39     h     var3  3.246734239  3.703213646
40     h     var4  4.242387776  4.407960355
41     i     var0  0.885226638 -0.288221276
42     i     var1  1.216012653  1.502514588
43     i     var2  2.302815441  1.905731471
44     i     var3  2.026631277  2.836508446
45     i     var4  4.800676814  4.772964668
46     j     var0 -0.435661855  0.192703997
47     j     var1  0.836814185  0.394505861
48     j     var2  1.663523873  2.377640369
49     j     var3  3.489536343  3.457597835
50     j     var4  4.146020948  4.281599816