Peter Dalgaard wrote:
>
>>
>> times = 3:4
>> do.call(rbind, by(data, data$id, function(data)
>> with(data, {
>> rows = (time == times[which(times %in% time)[1]])
>> if (is.na(rows[1])) data.frame(id=id, time=NA, x=NA) else
>> data[rows,] })))
>>
>> # id
Wacek Kusnierczyk wrote:
gallon li wrote:
Suppose I have a long format for a longitudinal data
id time x
1 1 10
1 2 11
1 3 23
1 4 23
2 2 12
2 3 13
2 4 14
3 1 11
3 3 15
3 4 18
3 5 21
4 2 22
4 3 27
4 6 29
I want to select the x values for each ID when time is equal to 3. When that
observation is
gallon li wrote:
> Suppose I have a long format for a longitudinal data
>
> id time x
> 1 1 10
> 1 2 11
> 1 3 23
> 1 4 23
> 2 2 12
> 2 3 13
> 2 4 14
> 3 1 11
> 3 3 15
> 3 4 18
> 3 5 21
> 4 2 22
> 4 3 27
> 4 6 29
>
> I want to select the x values for each ID when time is equal to 3. When that
> obse
Let's tackle the bigger problem of doing this not just for time = 3 but for all
times.
First we start with your data frame:
> dat
id time x
1 11 10
2 12 11
3 13 23
4 14 23
5 22 12
6 23 13
7 24 14
8 31 11
9 33 15
10 34 18
11 35 2
Try this. 'by' splits up the data frame into one data frame
per id and then f acts separately on each such sub-dataframe
returning a ts series with NAs for the missings. cbind'ing
those all together gives us this series with one column
per id:
> tt
Time Series:
Start = 1
End = 6
Frequency = 1
one way is the following:
dat <- read.table(textConnection("id time y
1 1 10
1 2 12
1 3 15
1 6 18
2 1 8
2 3 9
2 4 11
2 5 12
3 1 8
3 4 16
4 1 9
4 5 13
5 1 7
5 2 9
5 6 11"), header = TRUE)
closeAllConnections()
val <- 4
dat. <- data.frame(id = unique(dat$id), time = val)
out <- merge(dat, dat., al
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