Principal Components for categorical Variables

Question

I have data that contains both continuous and categorical variables. I want to find principal components as one can find using prcomp function (in R) for continuous variables. I've seen the function MFA in the FactoMineR package. I grouped all categorical variables as one group and continuous variables as the other group in MFA(). After running the function and trying to print the result res = MFA(...), I get:

       name                 description                                           
1  "$eig"               "eigenvalues"                                         
2  "$separate.analyses" "separate analyses for each group of variables"       
3  "$group"             "results for all the groups"                          
4  "$partial.axes"      "results for the partial axes"                        
5  "$inertia.ratio"     "inertia ratio"                                       
6  "$ind"               "results for the individuals"                         
7  "$quanti.var"        "results for the quantitative variables"              
8  "$quali.var"         "results for the categorical variables"               
9  "$quanti.var.sup"    "results for the quantitative supplementary variables"
10 "$summary.quanti"    "summary for the quantitative variables"              
11 "$summary.quali"     "summary for the categorical variables"               
12 "$global.pca"        "results for the global PCA"  

And I don't know where the principal components are, all I can see are the eigenvalues using res$eig, I'm trying to reduce the dimensions of data but I'm heavily out of luck as I can't understand where to check for the eigenvectors(PC) or the components of original data along the PCs. Doing a ls(res$ind) gives me "coord", "cos2", "contrib", I can't make out what these are or even if I need these ...