?duplicated
Clint Bowman INTERNET: cl...@ecy.wa.gov
Air Quality Modeler INTERNET: cl...@math.utah.edu
Department of Ecology VOICE: (360) 407-6815
PO Box 47600 FAX: (360) 407-7534
Olympia, WA 98504-7600
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On Wed, 26 Sep 2012, Rui Barradas wrote:
Sorry, but in my previous post I've confused the columns. It's by REQ.NR, not
by date
REQ.NR <- 1:4
REQ.NR <- c(REQ.NR, sample(REQ.NR, 2))
dat <- data.frame(date = Sys.Date() + 1:6, REQ.NR = REQ.NR, value = rnorm(6))
aggregate(dat, by = list(dat$REQ.NR), FUN = tail, 1)
Rui Barradas
Em 26-09-2012 16:19, wwreith escreveu:
I have several thousand rows of shipment data imported into R as a data
frame, with two columns of particular interest, col 1 is the entry date,
and
col 2 is the tracking number (colname is REQ.NR). Tracking numbers should
be
unique but on occassion aren't because they get entered more than once.
This
creates two or more rows of with the same tracking number but different
dates. I wrote a for loop that will keep the row with the oldest date but
it
is extremely slow.
Any suggestions of how I should write this so that it is faster?
# Creates a vector of on the unique tracking numbers #
u<-na.omit(unique(Para.5C$REQ.NR))
# Create Data Frame to rbind unique rows to #
Para.5C.final<-data.frame()
# For each value in u subset Para.5C find the min date and rbind it to
Para.5C.final #
for(i in 1:length(u))
{
x<-subset(Para.5C,Para.5C$REQ.NR==u[i])
Para.5C.final<-rbind(Para.5C.final,x[which(x[,1]==min(x[,1])),])
}
--
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and provide commented, minimal, self-contained, reproducible code.
