Add function to convert NLP col to 3,4, or 5 numeric cols

[levithatcher]

Based on PCA, we want to be able to convert text (using scikit-learn package) to a varying number of numeric columns.

Have an argument letting user specify which is the text col (since we'll expect a dataframe).

Two routes for this (and we can use an argument to specify which way the user wants):

1. We have a method argument that specifies the number of resultant columns, and we just grab that many eigenvalues (from the large matrix that arises after vectorization)

2. We have an argument such as percent.var.kept, which tells us how many columns (or eigenvectors) to grab that would make sure we get that much variance (from the super large matrix that, of course, contains all the variance).

So if percent.var.kept=10 and we grab three eigenvectors and have three cols in the resulting dataframe, then percent.var.kept=90 would pull back 30-40 columns. Of course, the number of columns will differ based on how much text is in the input text column.