What does rotation in PCA do?




While doing PCA on some data I read about rotating the loadings.What does this rotation do?
The loadings matrix from PCA:

And after I applied pca.rotated <- varimax(pca.data$loadings) gives:

What does this loading signify and which one do we take?


PCA -is a mathematical procedure that uses an orthogonal transformation to convert a set of values of possibly M correlated variables into a set of K uncorrelated variables called principal components.

Varimax rotation-It changes the coordinates that maximize the sum of square loadings.
The input of varimax is the rotated value.

In this problem, the result of varimax rotation says that in component one the major contributor is Total.Evening.Minutes and Total.Evening.The charge which are in the same direction and the rest of variables are tending towards to zero.

Hope this helps!



thanks a lot @hinduja1234