Hello,
You're right, thanks.
In my solution, I had tried to keep to the op as much as possible. A
glance at it made me realize that one change only would do the job, and
that was it, no performance worries.
I particularly liked the interaction/droplevels trick.
Rui Barradas
Em 25-07-2012 22:13, William Dunlap escreveu:
Rui,
Your solution works, but it can be faster for large data.frames if you
compute
the indices of the desired rows of the input data.frame and then using one
subscripting call to select the rows instead of splitting the input data.frame
into a list of data.frames, extracting the desired row from each component,
and then calling rbind to put the rows together again. E.g., compare your
approach, which I've put into the function f1
f1 <- function (dataFrame) {
retval <- with(dataFrame, sapply(split(dataFrame, list(PTID,
Year)), function(x) if (nrow(x))
x[which.max(x$Count), ]))
retval <- do.call(rbind, retval)
rownames(retval) <- 1:nrow(retval)
retval
}
with one that computes a logical subscripting vector (by splitting just the
Counts vector, not the whole data.frame)
f2 <- function (dataFrame) {
keep <- as.logical(ave(dataFrame$Count,
droplevels(interaction(dataFrame$PTID,
dataFrame$Year)), FUN = function(x) if (length(x)) seq_along(x) ==
which.max(x)))
dataFrame[keep, ]
}
The both compute the same thing, aside from the fact that the rows
are in a different order (f2 keeps the order of the original data.frame)
and f2 leaves the original row label with the row.
f1(df1)
PGID PTID Year Visit Count
1 6755 53122 2008 3 1
2 6755 53121 2009 1 0
3 6755 53122 2009 3 2
f2(df1)
PGID PTID Year Visit Count
1 6755 53121 2009 1 0
6 6755 53122 2008 3 1
9 6755 53122 2009 3 2
When there are a lot of output rows the f2 can be quite a bit faster.
(I put the call to droplevels(interaction(...)) into the call to ave because ave
can waste a lot of time calling FUN for nonexistent interaction levels.)
Bill Dunlap
Spotfire, TIBCO Software
wdunlap tibco.com
-----Original Message-----
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of Rui Barradas
Sent: Wednesday, July 25, 2012 10:24 AM
To: kborgmann
Cc: r-help
Subject: Re: [R] Select rows based on matching conditions and logical operators
Hello,
Apart from the output order this does it.
(I have changed 'df' to 'df1', 'df' is an R function, the F distribution
density.)
df1 <- read.table(text="
PGID PTID Year Visit Count
6755 53121 2009 1 0
6755 53121 2009 2 0
6755 53121 2009 3 0
6755 53122 2008 1 0
6755 53122 2008 2 0
6755 53122 2008 3 1
6755 53122 2009 1 0
6755 53122 2009 2 1
6755 53122 2009 3 2", header=TRUE)
df2 <- with(df1, sapply(split(df1, list(PTID, Year)),
function(x) if (nrow(x)) x[which.max(x$Count), ]))
df2 <- do.call(rbind, df2)
rownames(df2) <- 1:nrow(df2)
df2
which.max(9, not which().
Hope this helps,
Rui Barradas
Em 25-07-2012 18:10, kborgmann escreveu:
Hi,
I have a dataset in which I would like to select rows based on matching
conditions and return the maximum value of a variable else return one row if
duplicate counts exist. My dataset looks like this:
PGID PTID Year Visit Count
6755 53121 2009 1 0
6755 53121 2009 2 0
6755 53121 2009 3 0
6755 53122 2008 1 0
6755 53122 2008 2 0
6755 53122 2008 3 1
6755 53122 2009 1 0
6755 53122 2009 2 1
6755 53122 2009 3 2
I would like to select rows if PTID and Year match and return the maximum
count else return one row if counts are the same, such that I get this
output
PGID PTID Year Visit Count
6755 53121 2009 1 0
6755 53122 2008 3 1
6755 53122 2009 3 2
I tried the following code and the output is almost correct but duplicate
values were included
df2<-with(df, sapply(split(df, list(PTID, Year)),
function(x) if (nrow(x)) x[which(x$Count==max(x$Count)),]))
df<-do.call(rbind,df)
rownames(df)<-1:nrow(df)
Any suggestions?
Thanks much for your responses!
--
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on-matching-conditions-and-logical-operators-tp4637809.html
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
