I used Principal Component Analysis technique (PCA) under R to reduce the number of explanatory (independent) variables in my model (i.e PCA was used for variable reduction only). After running PCA, I got the components (10 components). What I want to do know is return these components back to the original variables(i.e I want to know what are the variables inside each of these components). My original data matrix contains 35,000 rows and 500 columns.
I don't believe this is possible. Principal components are derived from projecting the data to a vector that maximizes the spread or variance along that vector - see here mostly the visualizations. Asking which variables contributed most to this projection is a difficult question, similar to asking which points in this linear fit contribute most to the slope of a linear fit:
Which points would you pick and why? The principal components reconstruct the relationships in the data, but are derived from the data in a way that doesn't directly relate to any individual features of the data.
I think you are looking for the loadings. Depending on which method you used in R, these could be in the "loadings" or "rotation" slot in the object returned from the PCA routine. The loadings tell you how the original variables are weighted to form each principal component.