I have a dataset of dimensions 1500x200 where the predictors are both quantitative (discrete and continuous), as well as qualitative (categorical and ordinal) and the dependent variable is continuous. I've also performed standardization on these features.
I'm learning about PCA and visualization of PCA using biplots, and wanted to see if I could try it out on this dataset and see if I notice anything interesting. I found some code online that does this. About half way through I made a plot of the number of principal components versus explained variance. I expected that the first few principal components would explain much of the variance, but instead the plot looks sort of logarithmic where it takes over 30 principal components just to explain over 50% of the variance. Here's the plot:
So what I'm wondering is, is there something wrong methodologically with attempting to perform PCA on a dataset with mixed types such as the one I'm using? If so, is there a better visualization alternative than to create a biplot for this sort of data?