I have data in a "long" format where each row is a student and each
student occupies multiple rows with multiple observations. I need to
subset these data based on a condition which I am having difficulty
defining.
The dataset I am working with is large, but here is a simple data
structure to illustrate the issue
tmp <- data.frame(id = 1:3, matrix(rnorm(30), ncol=10) )
long <- reshape(tmp, idvar='id', varying=list(names(tmp)[2:11]),
v.names=('item'),timevar='position' , direction='long')
long <- long[order(long$id) , ]
long <- long[c(-2,-13),]
What I need to do is subset these data so I have the first 6 rows for
each unique ID. The problem is that the data are unbalanced in that each
ID has a different number of observations (which I why I removed obs 2
and 13).
If the data were balanced, the subset would be trivial and I could just
do
long <- subset(long, position < 7)
However, the data are not balanced. Consequently, if I were to do this
for the unbalanced data I would not have the first 6 obs for the first
ID. I would only have the first 5. Theoretically, what I want for
id1(and for each unique id) is this
ID1 <- subset(long, id==1)
ID1[1:6,]
However, the goal is to subset the entire dataframe at once such that
the subset returns a new dataframe with the first 6 rows for each unique
id. Is there a feasible method for doing this subset that anyone can
suggest? My actual dataset has more than 24,000 unique ids, so I am
hoping to avoid looping through this if possible.
Thanks,
Harold
[[alternative HTML version deleted]]
Apologies, but there were some word wrap issues in the prior email it
seems. So, here is code for the sample data to avoid confusion
tmp <- data.frame(id = 1:3, matrix(rnorm(30), ncol=10) )
long <- reshape(tmp, idvar='id', varying=list(names(tmp)[2:11]),
v.names=('item'),timevar='position' , direction='long')
long <- long[order(long$id) , ]
long <- long[c(-2,-13),]
> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Doran, Harold
> Sent: Tuesday, June 06, 2006 5:08 PM
> To: r-help at stat.math.ethz.ch
> Subject: [R] Subset data in long format
>
> I have data in a "long" format where each row is a student
> and each student occupies multiple rows with multiple
> observations. I need to subset these data based on a
> condition which I am having difficulty defining.
>
> The dataset I am working with is large, but here is a simple
> data structure to illustrate the issue
>
> tmp <- data.frame(id = 1:3, matrix(rnorm(30), ncol=10) ) long
> <- reshape(tmp, idvar='id', varying=list(names(tmp)[2:11]),
> v.names=('item'),timevar='position' ,
direction='long') long
> <- long[order(long$id) , ] long <- long[c(-2,-13),]
>
> What I need to do is subset these data so I have the first 6
> rows for each unique ID. The problem is that the data are
> unbalanced in that each ID has a different number of
> observations (which I why I removed obs 2 and 13).
>
> If the data were balanced, the subset would be trivial and I
> could just do
>
> long <- subset(long, position < 7)
>
> However, the data are not balanced. Consequently, if I were
> to do this for the unbalanced data I would not have the first
> 6 obs for the first ID. I would only have the first 5.
> Theoretically, what I want for id1(and for each unique id) is this
>
> ID1 <- subset(long, id==1)
> ID1[1:6,]
>
> However, the goal is to subset the entire dataframe at once
> such that the subset returns a new dataframe with the first 6
> rows for each unique id. Is there a feasible method for doing
> this subset that anyone can suggest? My actual dataset has
> more than 24,000 unique ids, so I am hoping to avoid looping
> through this if possible.
>
> Thanks,
> Harold
>
>
> [[alternative HTML version deleted]]
>
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>
Try this: subset(long, seq(id) - match(id,id) < 6) On 6/6/06, Doran, Harold <HDoran at air.org> wrote:> I have data in a "long" format where each row is a student and each > student occupies multiple rows with multiple observations. I need to > subset these data based on a condition which I am having difficulty > defining. > > The dataset I am working with is large, but here is a simple data > structure to illustrate the issue > > tmp <- data.frame(id = 1:3, matrix(rnorm(30), ncol=10) ) > long <- reshape(tmp, idvar='id', varying=list(names(tmp)[2:11]), > v.names=('item'),timevar='position' , direction='long') > long <- long[order(long$id) , ] > long <- long[c(-2,-13),] > > What I need to do is subset these data so I have the first 6 rows for > each unique ID. The problem is that the data are unbalanced in that each > ID has a different number of observations (which I why I removed obs 2 > and 13). > > If the data were balanced, the subset would be trivial and I could just > do > > long <- subset(long, position < 7) > > However, the data are not balanced. Consequently, if I were to do this > for the unbalanced data I would not have the first 6 obs for the first > ID. I would only have the first 5. Theoretically, what I want for > id1(and for each unique id) is this > > ID1 <- subset(long, id==1) > ID1[1:6,] > > However, the goal is to subset the entire dataframe at once such that > the subset returns a new dataframe with the first 6 rows for each unique > id. Is there a feasible method for doing this subset that anyone can > suggest? My actual dataset has more than 24,000 unique ids, so I am > hoping to avoid looping through this if possible. > > Thanks, > Harold > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html >