On Sat, Jun 16, 2012 at 8:19 AM, stef salvez <loggy...@googlemail.com> wrote:
> I have a panel data set (in MS excel) like the one below
>
>
> 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
>
>
>
>
> 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”.
>
> My data on prices are available every 28 days (or equivalently every 4
> weeks). 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. So,
> to achieve this I want to apply the following solutions
>
> 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.
>
> Please, I would be grateful to you if you could provide the exact code
> for doing this
>
>
Here is a solution using zoo and aggregate.zoo:
# dat is from Ken's post
dat <- data.frame("country" = c(rep(1,8), rep(2, 8)),
"date" = c("23/11/08","28/12/08","25/01/09","22/02/09",
"29/03/09","26/04/09","24/05/09", "28/06/09",
"26/10/08","23/11/08","21/12/08","18/01/09",
"15/02/09","16/03/09","12/04/09","10/05/09"),
"price" = c(2,3,4,5,6,32,23,32,45,46,90,54,65,77,7,6))
library(zoo)
# split into columns by country
z <- read.zoo(dat, index = 2, format = "%d/%m/%y", split = 1)
# by month for each country
aggregate(z, format(time(z), "%m"), mean, na.rm = TRUE)
# by year and month for each country
aggregate(z, as.yearmon, mean, na.rm = TRUE)
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