Suppose you have following kind of time series data in the data.frame:

> dtm[1:15, ]
   year month LPMVTUZ LPMVTVC LPMVTXC
1  1993     3    4829    4897  397808
2  1993     4    4496    4727  399425
3  1993     5    4997    4528  400922
4  1993     6    5444    4636  402512
5  1993     7    4899    4521  403940
6  1993     8    4636    4428  405716
7  1993     9    4548    4498  407348
8  1993    10    4687    4460  409435
9  1993    11    4419    4352  410844
10 1994     0    3410    4547  412513
11 1994     1    3787    4915  413913
12 1994     2    5180    4862  416117
13 1994     3    4729    5003  417992
14 1994     4    4871    4875  419575
15 1994     5    5323    4739  421933 

The variables are in the columns and you have two columns for year and
month. Suppose you need to aggregate this data, i.e. get annual values
for the variables. It is possible to do this using loops, but here is
one line solution:

> library(reshape)
> recast(dtm, year ~ variable, fun.aggregate = sum, id.var = c("year", 
+     "month"))
   year LPMVTUZ LPMVTVC  LPMVTXC
1  1993   42955   41047  3637950
2  1994   57885   57949  5075675
3  1995   57285   57349  5392784
4  1996   71659   71249  5692667
5  1997   77228   77074  6079579
6  1998   89375   88936  6517787
7  1999  114708  113565  7081522
8  2000  119794  120147  7748582
9  2001  160123  159500  8480626
10 2002  220737  219763  9517652
11 2003  277342  277167 10794708
12 2004  291258  289876 12256691
13 2005  288280  287910 13543842
14 2006  345355  345648 14822438
15 2007  362632  362596 16232798
16 2008  253198  255364 17327344
17 2009  117936  116994 14586582 

If you want to get average instead of sum, just use
fun.aggregate=mean.

This function actually combines two functions melt and cast. First one
melts your data.frame:

> mdtm <- melt(dtm, id.var = c("year", "month"))
> mdtm[1:15, ]
   year month variable value
1  1993     3  LPMVTUZ  4829
2  1993     4  LPMVTUZ  4496
3  1993     5  LPMVTUZ  4997
4  1993     6  LPMVTUZ  5444
5  1993     7  LPMVTUZ  4899
6  1993     8  LPMVTUZ  4636
7  1993     9  LPMVTUZ  4548
8  1993    10  LPMVTUZ  4687
9  1993    11  LPMVTUZ  4419
10 1994     0  LPMVTUZ  3410
11 1994     1  LPMVTUZ  3787
12 1994     2  LPMVTUZ  5180
13 1994     3  LPMVTUZ  4729
14 1994     4  LPMVTUZ  4871
15 1994     5  LPMVTUZ  5323 

Note that all the names of the variables are now in the column named
variable, and the corresponding values are in the column surprisingly
named value. You can control the names of these columns by the way.

After melting the data.frame now we need to cast it:

> cdtm <- cast(mdtm, year ~ variable, fun.aggregate = sum)
> cdtm
   year LPMVTUZ LPMVTVC  LPMVTXC
1  1993   42955   41047  3637950
2  1994   57885   57949  5075675
3  1995   57285   57349  5392784
4  1996   71659   71249  5692667
5  1997   77228   77074  6079579
6  1998   89375   88936  6517787
7  1999  114708  113565  7081522
8  2000  119794  120147  7748582
9  2001  160123  159500  8480626
10 2002  220737  219763  9517652
11 2003  277342  277167 10794708
12 2004  291258  289876 12256691
13 2005  288280  287910 13543842
14 2006  345355  345648 14822438
15 2007  362632  362596 16232798
16 2008  253198  255364 17327344
17 2009  117936  116994 14586582 

Here we use formula to tell cast function, how to pick data. You can
read more details in the help. What is in the right hand side of the
formula will become columns, and the left hand side will be rows. Try
to exchange them, this is what you will get:

> cast(mdtm, variable ~ year, fun.aggregate = sum)
  variable    1993    1994    1995    1996    1997    1998    1999    2000
1  LPMVTUZ   42955   57885   57285   71659   77228   89375  114708  119794
2  LPMVTVC   41047   57949   57349   71249   77074   88936  113565  120147
3  LPMVTXC 3637950 5075675 5392784 5692667 6079579 6517787 7081522 7748582
     2001    2002     2003     2004     2005     2006     2007     2008
1  160123  220737   277342   291258   288280   345355   362632   253198
2  159500  219763   277167   289876   287910   345648   362596   255364
3 8480626 9517652 10794708 12256691 13543842 14822438 16232798 17327344
      2009
1   117936
2   116994
3 14586582 

We got the same result, just in transposed form.

So here it is. For more info read the documentation of reshape
package. Also look into plyr package by the same author. And if you
ever meet him, please buy him a beer🙂