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I’ve applied PCA on my data using the function prcomp in R.
This function returns the following:
My question is: How can I reconstruct the reduced version of the data after choosing, for example, two principle components?
Going back and forth between PCA and ‘normal’ space is done using the rotation matrix. Just take a close look at the matrix algebra in the chapter on PCA in your favorite multivariate statistics book. To truncate (or reduce) your dataset, just limit the rotation matrix to the PC axes you want, e.g. the first two.