I have a potential solution, if I'm understanding your question correctly. I am going to provide you some code that should be fully reproducible.
## Get some data...
data(iris)
## The data contains four continuous and one categorical variable.
## Artificially produce missing values using the 'prodNA' function:
set.seed(81)
iris.mis <- prodNA(iris, noNA = 0.1)
## Impute missing values for just the first four columns of data
iris.mis[,1:4] <- missForest(iris.mis)$ximp[,1:4]
Let me know if an approach like this works. If it doesn't work, see if you can use some example code to show why.
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