From: Ajay Narottam Shah <ajayshah_at_mayin.org>

Date: Sun 14 Aug 2005 - 20:33:36 EST

# 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.

colnames(E) <- labels

cbind(Y, E)

}

colnames(E) <- labels

cbind(Y, E)

}

A <- new; rm(new)

Date: Sun 14 Aug 2005 - 20:33:36 EST

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.# Example:

# 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')

# 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 ajayshah@mayin.org Department of Economic Affairs http://www.mayin.org/ajayshah Ministry of Finance, New Delhi ______________________________________________ R-help@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.htmlReceived on Sun Aug 14 20:41:28 2005

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