# Logistic Regression using Non Numeric data

I am trying to understand how logistic regression can use multiple variables to predict an outcome that is non-numeric. For example I have a titanic data set with 14 variables, 4 variables are strings, 4 are numeric, 2 are ints, and 4 are categorical factors. Now I use the built in logistic regression model in R to predict Surviving passengers (0 or 1 factor):

lr_model <- glm(Survived ~ int variable + categorical variable + ... different types of variables, data=trainData)


But when I predict on the new data .. I get numeric values returned, not 1,0 predictions like the Survived predictor should have modeled it. If I could get an explaination of how the model works on numeric and non numeric data that would clear things up, and if it does spit out numeric values, should I just write a function to see if it is higher than 0.5 to see if a given observation is Survived, and below 0.5 as they didn't survive?

Any thoughts?