I performed two principal components analyses: in R and in SPSS - using the same dataset and the same variables. I got the same results - at least to some point. The eigenvalues are the same (I used the correlation matrix in both cases, no rotation), but the loadings are different.
I decided to take a look at some plots, so below you can see two plots of those R and SPSS PCA loadings. The first plot presents a little bit changed loadings of PCA in R. Namely, these are the opposite signs of R loadings. The second plot presents the original loading of PCA in SPSS. These plots are the same, but I can't figure out why they different in numbers.
Does R or SPSS do any implicit loadings transformation?