Here is a start on what you want to do. This generates some test data and
then does a couple of summaries:
> # generate some data
> N <- 1000
> x <- data.frame(date=as.character(20070000 + sample(1:4, N, TRUE) * 100 +
sample(1:31, N, TRUE)),
+ value=runif(N))
> head(x) # display the data
date value
1 20070124 0.07904540
2 20070117 0.17864565
3 20070109 0.86078870
4 20070205 0.93952259
5 20070112 0.87904425
6 20070323 0.01717623
> # assuming you read it in as character, convert to Date for processing
> x$date <- as.Date(strptime(x$date, "%Y%m%d"))
> x <- x[order(x$date), ] # order by date for plotting
> plot(x$date, x$value, type='l') # plot the data
> # show counts by month
> table(months(x$date))
April February January March
238 236 253 237
> # average by month
> aggregate(x$value, list(months(x$date)), mean)
Group.1 x
1 April 0.4791387
2 February 0.5010831
3 January 0.5114135
4 March 0.4695668
>
On 2/15/07, Sérgio Nunes <[EMAIL PROTECTED]> wrote:
>
> Hi,
>
> I have several files with data in this format:
>
> 20070102
> 20070102
> 20070106
> 20070201
> ...
>
> The data is sorted and each line represents a date (YYYYMMDD). I would
> like to analyze this data using R. For instance, I would like to have
> a histogram by year, month or day.
>
> I've already made a simple Perl script that aggregates this data but I
> believe that R can be much more powerful and easy on this kind of
> work.
>
> Any suggestions on where to start?
>
> Thanks in advance,
> Sérgio Nunes
>
> ______________________________________________
> R-help@stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
--
Jim Holtman
Cincinnati, OH
+1 513 646 9390
What is the problem you are trying to solve?
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