Correlation Matrix for non-numeric features Currently, I have dataset with numeric as well non-numeric attributes. I am trying to remove the redundant features  in the dataset using R Programming Languages. Note: Non-numeric attributes cannot be turned into binary.

The Caret R package provides the findCorrelation which will analyze a correlation matrix of your data’s attributes report on attributes that can be removed. However, It only works numeric values of 'x'. I have been unable to find a package which does it for non-numeric attributes. 

Is there a function in Caret R Package that does that for non-numeric attributes as well? If not, any method/package that would help me achieve the same?
There are measures of association for categorical variables. If you are looking at two ordinal variables you may use Spearman's correlation coefficient. There are also many measures for association for purely categorical variables, such as gender and race. Yule's Q and Crammer's v are popular choices.