Hi Tyler,
I don´t know if I understood well.
Try this. Case not work I try again and again :-)
df<-read.csv("http://www.nabble.com/file/p18018170/subdata.csv";)
df.min.diff<-aggregate(df["diff"], df[c("day")], min)
df.subset<-subset(df, paste(df$day, df$diff) %in% paste(df.min.diff$day,
df.min.diff$diff))
On 6/19/08, T.D.Rudolph <[EMAIL PROTECTED]> wrote:
>
>
> http://www.nabble.com/file/p18018170/subdata.csv subdata.csv
>
> I've attached 100 rows of a data frame I am working with.
> I have one factor, id, with 27 levels. There are two columns of reference
> data, x and y (UTM coordinates), one column "date" in POSIXct format, and
> one column "diff" in times format (chron package).
>
> What I am trying to do is as follows:
> For each day of the year (date, irrespective of time), select that row for
> each id which contains the smallest "diff" value, resulting in an output
> containing in general one value per id per day.
>
> "aggregate" has been suggested but it only produces the columns considered
> in the function and I need all columns intact. My data frame contains
> almost 70,000 entries so manual sorting is not an option. I know R is
> robust but my programming skills are elementary. The only way I know to
> approach it is to first separate every id, then filter, then recombine
> somehow. Is there not a more efficient way for this relatively
> straight-forward filtering exercise?
>
> Tyler
> --
> View this message in context:
> http://www.nabble.com/Advanced-Filtering-problem-tp18018170p18018170.html
> Sent from the R help mailing list archive at Nabble.com.
>
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