Big difference in randomTree accuracy with train and test sets
Overfitting is a common problem in random forest, you can use cross Validation methods (Cross validation is a technique to build models that are not prone to overfitting) example - K-fold cross validation, stratified k fold.. 

Try this using train package-
control &lt;- trainControl(method = "cv", number = 5)
and add this as a control parameter to the model.