On Sat, 3 Sep 2016, Bert Gunter wrote:
Chuck et. al.:
As I said previously, my intuition about the relative efficiency of
tapply() and duplicated() in the context of this thread was wrong.
My `intuition' was wrong, too.
But tapply() uses split() which runs quite fast. So not a big surprise,
Chuck et. al.:
As I said previously, my intuition about the relative efficiency of
tapply() and duplicated() in the context of this thread was wrong. But
I wondered exactly how and to what extent. So I've fooled around a bit
more and think I understand. Using the example I gave, the key is to
repl
Chuck:
I think this is quite clever. But note that the which() is
unnecessary: logical indicing suffices, e.g.
df[!duplicated(df[,c("f","g")],fromLast = TRUE),]
I thought that your approach would be faster because it moves
comparisons from the tapply() to C code. But I was wrong. e.g. for 1e6
ro
On Fri, 2 Sep 2016, Bert Gunter wrote:
[snip]
The "trick" is to use tapply() to select the necessary row indices of
your data frame and forget about all the do.call and rbind stuff. e.g.
I agree the way to go is "select the necessary row indices" but I get
there a different way. See below.
Hi Bert,
This is the best method I have seen this year! do.call, rbind has just gone
to museum :)
It took ~30 second to get the results. You deserve a medal
Jun
On Fri, Sep 2, 2016 at 1:51 PM, Bert Gunter wrote:
> This is the sort of thing that dplyr or the data.table packages can
> proba
Hello,
Try ?aggregate, it's probably faster.
With a made up data.frame, since you haven't provided us with a dataset,
simout.s1 <- data.frame(SID = rep(LETTERS[1:2], 10),
DOSENO = rep(letters[1:4], each = 5),
value = rnorm(20))
res2 <- aggregate(simout.s1$value,
This is the sort of thing that dplyr or the data.table packages can
probably do elegantly and efficiently. So you might consider looking
at them. But as I use neither, let me suggest a base R solution. As
you supplied no data for a reproducible example, I'll make up my own
and hopefully I have unde
Dear list,
I have the following line of code to extract the last line of the split
data and put them back together.
do.call(rbind,lapply(split(simout.s1,simout.s1[c('SID','DOSENO')]),function(x)x[nrow(x),]))
the problem is when have a huge dataset, it takes too long to run.
(actually it's > 3 h
8 matches
Mail list logo
