[R] sum on column using apply

[robert lee]

I have two data frames ( x and y -- sample values below).  The rows have HH:MM:SS and columns have names of devices.

I am trying to find a list of 5 least used devices during recorded time period.  When apply function is used to sum on the column, I get the correct answer on data frame called x, but not for y.  The data type of return answer is different and I cannot figure out why.   Any insight into what is happening of possibly different simpler ways to do this would be appreciated.

> colnames(t(sort(apply(x,2,sum))[1:5]))
[1] "5x"   "6x"   "7x"   "4x"   "103x"

# the above returns the answer I am trying to get

> colnames(t(sort(apply(y,2,sum))[1:5]))
NULL

# the above does not

below is sample data and output of the apply for each

> x
         0x 1x 2x 3x 4x 5x 6x 7x 32x 33x 34x 35x 36x 37x 38x 39x 64x 65x 66x 67x 68x
13:55:24 21 18 18 18 17 16 16 17  29  29  25  23  19  18  18  21  24  22  22  21  20
13:55:54 16  3  3  4  4  1  1  6  40  29  16   9   6  23  19  21  27  19  29  15  19
13:56:24  3  2  6  2  1  1  4  1  33  40  28  13  10   2   4  15  25  17   8  14  11
 [ truncated .... ]

> apply(x,2,sum)
   0x    1x    2x    3x    4x    5x    6x    7x   32x   33x   34x   35x   36x   37x 
12042 11411 11343 11237 10937 10811 10909 10911 18341 16055 14406 13770 12252 12003 
  38x   39x   64x   65x   66x   67x   68x   69x   70x   71x   96x   97x   98x   99x 
12266 13450 15426 14163 13913 13615 12972 12656 13089 13329 12671 12562 12336 12045 
 100x  101x  102x  103x 
11476 11212 11066 10997 

> y
         nfs6 sd0 sd1 sd30 sd31 sd36 sd6 ssd100 ssd101 ssd102 ssd103 ssd104 ssd105
13:55:54    0   2   0    0    0    0   0      0      0      0      0      0      0
13:56:54    0   3   0    0    0    0   0      0      0      0      0      0      0
13:57:54    0   1   0    0    0    0   0      0      0      0      0      0      0
13:58:54    0   1   0    0    0    0   0      0      0      0      0      0      0
 [ truncated .... ]

> apply(y,2,sum)
  [1]    0  515    0    0    0    0    0   96    0    0    0   90    0    0    0    0
 [17]    0    1   13   96    0   31    0    0    0    0    0    0    0    0   11    0
 [33]    0    0   12    0    0    0    0    0    0    0   16    0    0    0    0    0
 [49]   31    0    0    0    0    0    0    0    0   10    0    0    0    0    0    0
 [65]    0    0    7    0    0   84  337  642 1005    0  605  518    0    0    5    0
 [81]    0   86  335  646 1014    0  606  513    0  737  418  306  277  607  301  410
 [97] 1690  445  432    0  738  424  315  283  608  302  411 1688  446  431    0    0
[113]    0    0    0   93    0    0    0    0    0    1   12    0    0
>