How to use both categorical and continuous predictors in a multiple imputation [r]

Question

I have a large dataset of a couple of categorical (nominal) variables and a number of continuous variables. Most of the continuous variables have missing data.

I have been using the mice package (pmm and rf) to impute the missing data, however, I realised that the method is ignoring the categorical data. The categorical data could be useful for prediction.

Therefore, I am looking for a multiple imputation code (ideally Random Forest because there is a large share of missing data) in R which allows considers both continuous and categorical predictors to impute multiple continuous variables.

Answer 1

It turns out I needed to convert my categorical variables into vectors