very quick summary of PCA

quick-PCA.R

## I don't know if any of this will make sense
## but if you can't wait to get started doing PCA, take a look at the below

## all R code is right below. annoted code is below that
dataset.PCAcor <- princomp(dataset,cor=T)
summary(dataset.PCAcor)
loadings(dataset.PCAcor)

biplot(dataset.PCAcor)
biplot(dataset.PCAcor,col=c("azure4","black"), cex=c(0.8,1), expand=0.9)
title("Biplot based on correlation matrix")



## Id look at bivariate  correlations first (ie, pairwise correlations between variables)
## your dataframe should only have the variables you want to summarize, in columns



## to get pca

## if the variables are standardized (or all have the same units)
## then cor = F
## if variables have different units (like lbs, miles, hrs, etc)
## then cor = T
dataset.PCAcor <- princomp(dataset,cor=T)
summary(dataset.PCAcor)
loadings(dataset.PCAcor)


### one of these will show you "proportion of variance". this is variance explained by each principal component (each component is a summary of a few variables. loadings show which variables are in each component)
### max # of components = the # of variables

### you want a low# of components to explain a large % of the variance (look at cumulative variance)


# a 2-d projection of first two components
biplot(dataset.PCAcor)
# biplot(dataset.PCAcor,col=c("azure4","black"), cex=c(0.8,1), expand=0.9)
title("Biplot based on correlation matrix")