[R] Temporal disaggregation

[stef salvez]

Dear R users,
I have a panel data set (in MS excel)  on prices across countries and time

country    time                 price
 1         "23/11/08"            2
1   "28/12/08"                   3
1    "25/01/09"                   4
1   "22/02/09"                   5
1    "29/03/09"                  6
1  "26/04/09"                   32
1  "24/05/09"                   23
1  "28/06/09"                   32
2   "26/10/08"                45
2  "23/11/08"                 46
2  "21/12/08"               90
2  "18/01/09"                54
2  "15/02/09"                 65
2   "16/03/09"               77
2  "12/04/09"                    7
2   "10/05/09"                   6


As you can see,

1)the start and end date of the time series for countries 1 and 2 are
different. For example, for country 1 the time series begins on
"23/11/08" while for country 2 the time series begins on "26-10-2008”.

2)My data on prices are available every 28 days (or equivalently every 4
weeks). So, each observation is a 4-week average. But in some cases I
have jumps (35 days or 29 days instead of
28 days). For example from the above table we have such jumps: from
"28/12/08" to "28/12/08" , from 22/02/09" to "29/03/09", etc

My goal is to have a unified sequence of dates across countries.
Otherwise I will not be able to do further data/econometric analysis,
Unless you have different suggestion, I want  to take what I have and
calculate monthly average prices and also report how many prices those
averages are based on. I suppose that I will still have gaps and may
well need to interpolate.

Put differently, I want to interpolate the 4-week average prices to
monthly average prices.
The problem is also the jumps where I have 5 weeks in some cases and I
want to find the monthly average of it.
 I do not want something like

 country  yearmon avg.price freq
1        1 Nov 2008         2    1
2        1 Dec 2008         3    1
3        1 Jan 2009         4    1
4        1 Feb 2009         5    1
5        1 Mar 2009         6    1
6        1 Apr 2009        32    1
7        1 May 2009        23    1
8        1 Jun 2009        32    1
9        2 Oct 2008        45    1
10       2 Nov 2008        46    1
11       2 Dec 2008        90    1
12       2 Jan 2009        54    1
13       2 Feb 2009        65    1
14       2 Mar 2009        77    1
15       2 Apr 2009         7    1
16       2 May 2009         6    1


Please, I would be grateful to you if you could provide the exact code
for doing this

thank you