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

You can do this with by(). First set up some data:

R> set.seed(42)
R> testdf <- data.frame(var1=rnorm(100), var2=rnorm(100,2), var3=rnorm(100,3),  
                        group=as.factor(sample(letters[1:10],100,replace=T)),  
                        year=as.factor(sample(c(2007,2009),100,replace=T)))
R> summary(testdf)
      var1              var2              var3          group      year   
 Min.   :-2.9931   Min.   :-0.0247   Min.   :0.30   e      :15   2007:50  
 1st Qu.:-0.6167   1st Qu.: 1.4085   1st Qu.:2.29   c      :14   2009:50  
 Median : 0.0898   Median : 1.9307   Median :2.98   f      :12            
 Mean   : 0.0325   Mean   : 1.9125   Mean   :2.99   h      :12            
 3rd Qu.: 0.6616   3rd Qu.: 2.4618   3rd Qu.:3.65   d      :11            
 Max.   : 2.2866   Max.   : 4.7019   Max.   :5.46   b      :10            
                                                    (Other):26  

Use by():

R> by(testdf[,1:3], testdf$year, mean)
testdf$year: 2007
   var1    var2    var3 
0.04681 1.77638 3.00122 
--------------------------------------------------------------------- 
testdf$year: 2009
   var1    var2    var3 
0.01822 2.04865 2.97805 
R> by(testdf[,1:3], list(testdf$group, testdf$year), mean)  
## longer answer by group and year suppressed

You still need to reformat this for your table but it does give you the gist of your answer in one line.

Edit: Further processing can be had via

R> foo <- by(testdf[,1:3], list(testdf$group, testdf$year), mean)  
R> do.call(rbind, foo)
          var1   var2  var3
 [1,]  0.62352 0.2549 3.157
 [2,]  0.08867 1.8313 3.607
 [3,] -0.69093 2.5431 3.094
 [4,]  0.02792 2.8068 3.181
 [5,] -0.26423 1.3269 2.781
 [6,]  0.07119 1.9453 3.284
 [7,] -0.10438 2.1181 3.783
 [8,]  0.21147 1.6345 2.470
 [9,]  1.17986 1.6518 2.362
[10,] -0.42708 1.5683 3.144
[11,] -0.82681 1.9528 2.740
[12,] -0.27191 1.8333 3.090
[13,]  0.15854 2.2830 2.949
[14,]  0.16438 2.2455 3.100
[15,]  0.07489 2.1798 2.451
[16,] -0.03479 1.6800 3.099
[17,]  0.48082 1.8883 2.569
[18,]  0.32381 2.4015 3.332
[19,] -0.47319 1.5016 2.903
[20,]  0.11743 2.2645 3.452
R> do.call(rbind, dimnames(foo))
     [,1]   [,2]   [,3]   [,4]   [,5]   [,6]   [,7]   [,8]   [,9]   [,10] 
[1,] "a"    "b"    "c"    "d"    "e"    "f"    "g"    "h"    "i"    "j"   
[2,] "2007" "2009" "2007" "2009" "2007" "2009" "2007" "2009" "2007" "2009"

You can play with the dimnames some more:

R> expand.grid(dimnames(foo))
   Var1 Var2
1     a 2007
2     b 2007
3     c 2007
4     d 2007
5     e 2007
6     f 2007
7     g 2007
8     h 2007
9     i 2007
10    j 2007
11    a 2009
12    b 2009
13    c 2009
14    d 2009
15    e 2009
16    f 2009
17    g 2009
18    h 2009
19    i 2009
20    j 2009
R> 

Edit: And with that, we can create a data.frame for the result without resorting to external packages using only base R:

R> data.frame(cbind(expand.grid(dimnames(foo)), do.call(rbind, foo)))
   Var1 Var2     var1   var2  var3
1     a 2007  0.62352 0.2549 3.157
2     b 2007  0.08867 1.8313 3.607
3     c 2007 -0.69093 2.5431 3.094
4     d 2007  0.02792 2.8068 3.181
5     e 2007 -0.26423 1.3269 2.781
6     f 2007  0.07119 1.9453 3.284
7     g 2007 -0.10438 2.1181 3.783
8     h 2007  0.21147 1.6345 2.470
9     i 2007  1.17986 1.6518 2.362
10    j 2007 -0.42708 1.5683 3.144
11    a 2009 -0.82681 1.9528 2.740
12    b 2009 -0.27191 1.8333 3.090
13    c 2009  0.15854 2.2830 2.949
14    d 2009  0.16438 2.2455 3.100
15    e 2009  0.07489 2.1798 2.451
16    f 2009 -0.03479 1.6800 3.099
17    g 2009  0.48082 1.8883 2.569
18    h 2009  0.32381 2.4015 3.332
19    i 2009 -0.47319 1.5016 2.903
20    j 2009  0.11743 2.2645 3.452
R>