I have written two functions which do useful things with panel data
a.k.a. longitudinal data, where one unit of observation (a firm or a
person or an animal) is observed on a uniform time grid:
- The first function makes lagged values of variables of your choice.
- The second function makes growth rates w.r.t. q observations ago,
for variables of your choice.
These strike me as bread-and-butter tasks in dealing with panel
data. I couldn't find these functions in the standard R
libraries. They are presented in this email for two reasons. First,
it'll be great if R gurus can look at the code and propose
improvements. Second, it'll be great if some package-owner can adopt
these orphans :-) and make them available to the R community.
The two functions follow:
library(Hmisc) # Am using Lag() in this.
# Task: For a supplied list of variables (the list `lagvars'),
# make new columns in a dataset denoting lagged values.
# You must supply `unitvar' which identifies the unit that's
# repeatedly observed.
# You must supply the name of the time variable `timevar'
# and you must tell a list of the lags that interest you (`lags')
# Example:
# paneldata.lags(A, "person", "year", c("v1","v2"), lags=1:4)
paneldata.lags <- function(X, unitvar, timevar, lagvars, lags=1) {
stopifnot(length(lagvars)>=1)
X <- X[order(X[,timevar]),] # just in case it's not sorted.
innertask <- function(Y, lagvars, lags) {
E <- labels <- NULL
for (v in lagvars) {
for (i in lags) {
E <- cbind(E, Lag(Y[,v], i))
}
labels <- c(labels, paste(v, ".l", lags, sep=""))
}
colnames(E) <- labels
cbind(Y, E)
}
do.call("rbind", by(X, X[,unitvar], innertask, lagvars, lags))
}
# Task: For a supplied list of variables (the list `gvars'),
# make new columns in a dataset denoting growth rates.
# You must supply `unitvar' which identifies the unit that's
# repeatedly observed.
# You must supply the name of the time variable `timevar'
# and you must tell the time periods Q (vector is ok) over which
# the growth rates are computed.
paneldata.growthrates <- function(X, unitvar, timevar, gvars, Q=1) {
stopifnot(length(gvars)>=1)
X <- X[order(X[,timevar]),]
makegrowths <- function(x, q) {
new <- rep(NA, length(x))
for (t in (1+q):length(x)) {
new[t] <- 100*((x[t]/x[t-q])-1)
}
return(new)
}
innertask <- function(Y, gvars, Q) {
E <- labels <- NULL
for (v in gvars) {
for (q in Q) {
E <- cbind(E, makegrowths(Y[,v], q))
}
labels <- c(labels, paste(v, ".g", Q, sep=""))
}
colnames(E) <- labels
cbind(Y, E)
}
do.call("rbind", by(X, X[,unitvar], innertask, gvars, Q))
}
Here's a demo of using them:
# A simple panel dataset
A <- data.frame(year=rep(1980:1982,4),
person=factor(sort(rep(1:4,3))),
v1=round(rnorm(12),digits=2), v2=round(rnorm(12),digits=2))
# Demo of creating lags for both variables v1 and v2 --
paneldata.lags(A, "person", "year", c("v1","v2"), lags=1:2)
# Demo of creating growth rates for v2 w.r.t. 1 & 2 years ago --
paneldata.growthrates(A, "person", "year", "v2", Q=1:2)
Finally, I have a question. In a previous posting on this subject,
Gabor showed me that my code:
# Blast this function for all the values that A$person takes --
new <- NULL
for (f in levels(A$person)) {
new <- rbind(new,
make.additional.variables(subset(A, A$person==f),
nlags=2, Q=1))
}
A <- new; rm(new)
can be replaced by one do.call() (as used above). It's awesome, but I
don't understand it! :-) Could someone please explain how and why this
works? I know by() and I see that when I do by(A,A$x), it gives me a
list containing as many entries as are levels of A$x. I couldn't think
of a way to force all this into one data frame; the best I could do
was to do for (f in levels (A$person)) {} as shown here. The two
functions above are using do.call() as Gabor used them, and they're
awesome, but I don't understand why they work! The man page ?do.call
was a bit too cryptic and I couldn't comprehend it. Where can I learn
this stuff?
--
Ajay Shah Consultant
[EMAIL PROTECTED] Department of Economic Affairs
http://www.mayin.org/ajayshah Ministry of Finance, New Delhi
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