R help - Apr 2020 - Error using R caret package (train) with C5.0 decision tree to do K-fold cross validation

I'm trying to use the caret package to do repeated k-fold cross validation
with C5.0 decision trees.

The following code generates a working C5.0 decision tree (68% accuracy on
confusion matrix):

    > model <- C5.0(as.factor(OneGM) ~., data=OneT.train)
    > results <- predict(object=model, newdata=OneT.test,
type="class")

The caret package code gives these errors:

    > train_control <- trainControl(method="repeatedcv",
number=10, repeats=10)

    > model <- train(as.factor(OneGM) ~., data=OneT.train,
trControl=train_control, method="C5.0")
    Error in na.fail.default(list(`as.factor(OneGM)` = c(1L, 1L, 1L, 1L,  :
missing values in object

    > model <- train(OneGM ~., data=OneT.train, trControl=train_control,
method="C5.0")
    Error in na.fail.default(list(OneGM = c(FALSE, FALSE, FALSE, FALSE,  :
missing values in object

The data is loaded from a .csv file, and OneGM is either TRUE or FALSE (a text
column in the .csv).

I would like to use the one-line caret package approach above (which I've
seen used in multiple places), and I'm not looking for solutions that do
cross validation manually.

Thanks for any help.
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