Hi all.
I'm looking for robust ways of building lagged variables in a dataset
with multiple individuals.
Consider a dataset with variables like the following:
##
set.seed(123)
d <- data.frame(id = rep(1:2, each=3), time=rep(1:3, 2), value=rnorm(6))
##
>d
id time value
1 1 1 -0.56047565
2 1 2 -0.23017749
3 1 3 1.55870831
4 2 1 0.07050839
5 2 2 0.12928774
6 2 3 1.71506499
I want to compute the lagged variable 'value(t-1)', taking subject id
into account.
My current effort produced the following:
##
my_lag <- function(dt, varname, timevarname='time', lag=1) {
vname <- paste(varname, if(lag>0) '.' else '', lag, sep='')
timevar <- dt[[timevarname]]
dt[[vname]] <- dt[[varname]][match(timevar, timevar + lag)]
dt
}
lag_by <- function(dt, idvarname='id', ...)
do.call(rbind, by(dt, dt[[idvarname]], my_lag, ...))
##
With the previous data I get:
> lag_by(d, varname='value')
id time value value.1
1.1 1 1 -0.56047565 NA
1.2 1 2 -0.23017749 -0.56047565
1.3 1 3 1.55870831 -0.23017749
2.4 2 1 0.07050839 NA
2.5 2 2 0.12928774 0.07050839
2.6 2 3 1.71506499 0.12928774
So that seems working. However, I was thinking if there is a
smarter/cleaner/more robust way to do the job. For instance, with the
above function I get dataframe rows re-ordering as a side-effect
(anyway this is of no concern in my current analysis)...
Any suggestion?
All the bests,
Fabio.
--
Antonio, Fabio Di Narzo
Ph.D. student at
Department of Statistical Sciences
University of Bologna, Italy
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