Wow.. thanks for the deluge of responses!
Aggregate seems like the way to go here.
But, suppose that instead of integers in column V2, I actually have
dates (and instead of keeping the minimum integer, I want to keep the
earliest date):
> df =
> data.frame(V1=c(1,1,2,3,1,2),V2=c('2002-03-13','1989-03-10','1988-01-20','1997-05-15','1996-11-18','2000-01-12'));
> aggregate(df$V2,df['V1'],min) #thanks, Phil
Error in Summary.factor(c(6L, 2L, 3L), na.rm = FALSE) :
min not meaningful for factors
So I checked:
> min(df$V2)
Error in Summary.factor(c(6L, 2L, 1L, 4L, 3L, 5L), na.rm = FALSE) :
min not meaningful for factors
This can be fixed with:
> min(as.Date(df$V2))
[1] "1988-01-20"
However, I get a strange response when I try:
> aggregate(as.Date(df$V2),df['V1'],min)
V1 x
1 1 7008
2 2 6593
3 3 9996
What I'm looking for, of course, is:
> aggregate(as.Date(df$V2),df['V1'],min)
V1 V2
1 1 1996-11-18
2 2 1988-01-20
3 3 1997-05-15
Any idea how to produce the desired output?
Thanks,
Jonathan
On Mon, Feb 8, 2010 at 12:11 PM, Gabor Grothendieck
<ggrothendi...@gmail.com> wrote:
> 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("select V1, min(V2) V2 from DF group by V1")
> V1 V2
> 1 a 2
> 2 b 9
> 3 c 4
>
>> # 4. summaryBy in the doBy package
>> library(doBy)
>> summaryBy(V2 ~., DF, FUN = min, keep.names = TRUE)
> V1 V2
> 1 a 2
> 2 b 9
> 3 c 4
>
> On Mon, Feb 8, 2010 at 11:39 AM, Jonathan <jonsle...@gmail.com> 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 to do (perhaps using the
>> "merge" function?). I figured somebody out there might've already
>> figured this out:
>>
>> I have a dataframe with two columns (let's call them V1 and V2). All
>> rows are unique, although column V1 has several redundant entries.
>>
>> Ex:
>>
>> V1 V2
>> 1 a 3
>> 2 a 2
>> 3 b 9
>> 4 c 4
>> 5 a 7
>> 6 b 11
>>
>>
>> What I'd like is to return a dataframe cut down to have only unique
>> entires in V1. V2 should contain a vector, for each V1, that is the
>> minimum of all the possible choices from the set of redundant V1's.
>>
>> Example output:
>>
>> V1 V2
>> 1 a 2
>> 2 b 9
>> 3 c 4
>>
>>
>> If somebody could (relatively easily) figure out how to get closer to
>> a solution, I'd appreciate hearing how. Also, I'd be interested to
>> hear how you came upon the answer (so I can get better at searching
>> the R resources myself).
>>
>> Regards,
>> Jonathan
>>
>> ______________________________________________
>> 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.
>>
>
______________________________________________
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.
