R Lagged Variables with Time-Series Cross-Sectional Data
If you want to create a lagged variable in R for time-series cross-sectional data the usual time series packages (i.e. *zoo* and *xts*) don't really do the job. So use the *plyr* package. Imagine we have a data frame (`Data`) with three variables: `Country`, `Year` and `Variable`. We want to lag `Variable` one year for each country. Let's call the lagged variable `VariableLag1`. Use the `ddply` command like this:
library(plyr) Data <- ddply(Data, .(country), transform, VariableLag1 = c(NA, Variable[-length(Variable)]))
That's it. Just remember to have the variables in time order. Thanks to [this post](http://stackoverflow.com/a/8910583/1705044) on StackOverflow.
