Hi,
I have a data set with variables that are _not_ missing at random. Now I
use a package for learning a Bayesian Network which won't accept NA as a
value. From a database I query data.frames with k,k+n,k+2n, ... variables
(there are always at least k variables as leftmost columns). Using
rbind.fill from the reshape package on two data frames I would get a data
frame like
trg_type child_type_1
1 Scientists NA
2 of used
Now to get rid of NA values I use the following function, which works for
data frames with only factor values:
substitute_na <- function(tok, na_factor_level = "NOT_REALIZED") {
for (i in 1:length(tok)) {levels(tok[,i]) <- c(levels(tok[,i]),
na_factor_level)}
tok[is.na(tok)] <- as.factor(na_factor_level)
return(tok)
}
Is there a better/faster way to do it? It would also be great to be able to
distinguish factor columns from numeric columns and use a special numeric
value there. The current version of rbind.fill makes no direct reference to
the fill value so that I could change its implementation for my purpose.
Thanks!
Ingmar
--
Ingmar Schuster
Natural Language Processing Group
Department of Computer Science
University of Leipzig
Johannisgasse 26
04103 Leipzig, Germany
Tel. +49 341 9732205
http://asv.informatik.uni-leipzig.de/en/staff/Ingmar_Schuster
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