Benjamin, A more elegant "R-style" solution would be to use one of R's "apply"/aggregation routines, of which there are many. For example, the "by" function can split a data.frame by some factor/categorical variable(s), and then apply a function to each "slice". The result can then be pieced back together. See below for an example in which this factor is simply a parallel vector of pure dates:
# extract pure date component of time and date dates <- format(serv$datum, "%Y-%m-%d") # write auxilliary function to aggregate a "slice" of the data.frame # x will be a "slice" of data from a single day aggregateDf <- function(x) { # return a one-row data.frame data.frame(datum = format(x$datum[1], "%Y-%m-%d"), write = sum(x$write), read = sum(x$read) ) } # now process each "slice" of the serv data.frame using "by" splitVals <- by(serv, dates, aggregateDf ) # bind back into a single data.frame values <- do.call(rbind, splitVals) The difference in execution speed is pretty negligible on my machine, so it's a more concise solution but I don't know if it is much faster. HTH, Francisco On Thu, Mar 10, 2011 at 1:23 PM, Benjamin Stier < benjamin.st...@ub.uni-tuebingen.de> wrote: > Hello list! > > I have a data.frame which looks like this: > > serv > datum op.read op.write read write > 1 2011-01-29 10:00:00 0 0 0 0 > 2 2011-01-29 10:00:01 0 0 0 0 > 3 2011-01-29 10:00:02 0 0 0 0 > 4 2011-01-29 10:00:03 0 4 0 647168 > 5 2011-01-29 10:00:04 0 0 0 0 > 6 2011-01-29 10:00:05 0 14 0 1960837 > 7 2011-01-29 10:00:06 0 0 0 0 > ... > 115 2011-01-30 10:00:54 0 0 0 0 > 116 2011-01-30 10:00:55 0 0 0 0 > 117 2011-01-30 10:00:56 0 0 0 0 > 118 2011-01-30 10:00:57 54 0 29184 0 > 119 2011-01-30 10:00:58 204 0 122880 0 > 120 2011-01-30 10:00:59 0 0 0 0 > ... > > I want to compare read/write from each day. I already have a solution, but > it > is pretty slow. > > # read the data > serv <- read.delim("cut.inp") > > # Reformat the dates from the file > serv$datum <- strptime(serv$datum, "%Y-%m-%d %H:%M:%S") > > # select all single days > dates.serv <- unique(strptime(serv$datum, format="%Y-%m-%d")) > > # create a data.frame > values <- data.frame(row.names=1, datum=numeric(0), write=numeric(0), > read=numeric(0)) > for(i in as.character(dates.serv)) { > # build up a values for a day-range > searchstart <- as.POSIXlt(paste(i, "00:00:00", sep=" ")) > searchend <- as.POSIXlt(paste(i, "23:59:59", sep=" ")) > # select all values from a specific day > day <- serv[(serv$datum >= searchstart & serv$datum <= searchend),] > write <- as.numeric(sum(as.numeric(day$write))) > read <- as.numeric(sum(as.numeric(day$read))) > # add to the data.frame > values <- rbind(values, data.frame(datum=i, write=write, read=read)) > } > > This is my first try using R for statistics so I'm sure this isn't the best > solution. > The for-loop does it's job, but as I said is really slow. My data is for 21 > days and 1 line per second. > Is there a better way to select the date-ranges instead of a for-loop? The > line where I select all values for "day" seems to be the heaviest. Any > idea? > > Kind regards, > > Benjamin > > PS: I attached some sample data, in case you want to try for yourself. > > ______________________________________________ > R-help@r-project.org 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. > > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org 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.