Can PC1 explain more than 90% of variance? I've been running some analysis using morphological data. However, when I run a PCA, PC1 explains more than 90% of variance. I'm not sure if this is possible. If not, what should I need to do?


In case you need a non-simulated example: I did help.search("morpholog", agrep = FALSE) to find morphological data sets in the set of R packages I happen to have installed on my system.  A useful one is ade4::tortues, a data set on 48 painted turtles from Jolicoeur and Mosimann 1960.
long, larg, and haut are the length, width, and height of the turtles in mm; because they're all measured in the same units, it shouldn't be necessary to scale the variable before/while computing principal components.
library(ade4)
## column 4 is sex
pairs(tortues[,1:3], col = tortues[,4], gap = 0)
summary(prcomp(tortues[,1:3]))

PC1 has 98% of the variance. (Jolicoeur and Mosimann get 97.61%; I get 97.16% if I set scale= TRUE; haven't checked for other issues [transcription errors etc.]).
Importance of components:
                           PC1     PC2     PC3
Standard deviation     25.3100 2.40272 2.26449
Proportion of Variance  0.9833 0.00886 0.00787
Cumulative Proportion   0.9833 0.99213 1.00000


Jolicoeur, P. and Mosimann, J. E. (1960) Size and shape variation
in the painted turtle. A principal component analysis. Growth,
24, 339-354.
