Here are 3 solutions assuming DF contains the data frame:
> # 1. aggregate
> aggregate(DF[2], DF[1], min)
V1 V2
1 a 2
2 b 9
3 c 4
> # 2. aggregate.formula - requires R 2.11.x
> aggregate(V2 ~ V1, DF, min)
V1 V2
1 a 2
2 b 9
3 c 4
> # 3. SQL using sqldf
> library(sqldf)
> sqldf("se
On Mon, Feb 8, 2010 at 10:39 AM, Jonathan wrote:
> Hi all,
> I'm feeling a little guilty to ask this question, since I've
> written a solution using a rather clunky for loop that gets the job
> done. But I'm convinced there must be a faster (and probably more
> elegant) way to accomplish what
You could try aggregate:
If we call your data frame df:
aggregate(df[2], by=df[1], FUN=min)
will get you what you asked for (if not necessarily what you need ;-)
)
Switching the columns around is easy enough if you need to; proceeding
stepwise:
df.new<-aggregate(df[2], by=df[1], FUN=min)
df.new
On Feb 8, 2010, at 11:39 AM, Jonathan wrote:
Hi all,
I'm feeling a little guilty to ask this question, since I've
written a solution using a rather clunky for loop that gets the job
done. But I'm convinced there must be a faster (and probably more
elegant) way to accomplish what I'm looking
> x <- read.table(textConnection("V1 V2
+ 1a3
+ 2a2
+ 3b9
+ 4c4
+ 5a7
+ 6b11"), header=TRUE)
> closeAllConnections()
> # close; matrix with rownames - easy enough to change into a dataframe if you
> want
> cbind(tapply
Hi!
I'm definitely not an expert in R (and it's my first reply!), but if I
understand right, I think the aggregate function might do what you're
looking for.
Try ?aggregate to get more info. You might find what you need!
HTH
Ivan
Le 2/8/2010 17:39, Jonathan a écrit :
Hi all,
I'm feel
On Mon, Feb 8, 2010 at 11:39 AM, Jonathan wrote:
> Hi all,
> I'm feeling a little guilty to ask this question, since I've
> written a solution using a rather clunky for loop that gets the job
> done. But I'm convinced there must be a faster (and probably more
> elegant) way to accomplish what
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