# Why are my eigenvalues so huge?

I'm running a PCA using prcomp in R and I get a table like this from the summary function:

Importance of components:
PC1     PC2     PC3     PC4
Standard deviation     227.5998 86.2614 6.76700 3.29498
Proportion of Variance   0.8736  0.1255 0.00077 0.00018
Cumulative Proportion    0.8736  0.9990 0.99982 1.00000


Which gives me eigenvalues of:

 PC1      PC2      PC3      PC4
51801.68  7441.03  45.79    10.86


My understanding was that any variable with eigenvalues greater than 1 are considered important but these seem ridiculously high. And I wouldn't think a principal component with a proportion of variance of 0.00018 to be too important. All my data seems fine.

Thanks!

• A tell-tale sign of using variables as they come is that the eigenvalues don't sum to the number of variables (implying that the mean eigenvalue is 1). As the fine answer by @Mark L. Stone tells you, you didn't scale values first, so your first PC1 is just dominated by variable(s) with the highest variance(s) (which may be a side-effect of particular units of measurement). – Nick Cox Feb 26 '18 at 21:07