Principal Component Analysis in R

There are, at least :), two ways to compute the principal component analysis of a data set in R. The first one is from scratch computing eigenvectors and eigenvalues. It works as follows

# 
# From scratch 
# 
cbind(1:10,1:10 + 0.25*rnorm(10)) -> myData 
myData - apply(myData,2,mean) -> myDataZM 
cov(myDataZM) -> cvm 
eigen(cvm,TRUE) -> eCvm 
t(eCvm$vector%*%t(myDataZM)) -> newMyData

This simple code just transforms the data to align it with the principal components obtained. Of couse, the second way to compute them is using some of the functions that R provides in the stats package.

# 
# Using the stats package 
# 
cbind(1:10,1:10 + 0.25*rnorm(10)) -> myData 
myData - apply(myData,2,mean) -> myDataZM 
prcomp(myData) -> pcaMyData 
t(pcaMyData[[2]]%*%t(myDataZM)) -> newMyData