**Principal component analysis (PCA)**is a mathematical transformation of possibly(correlated) variables into a number of uncorrelated variables called principal components. The resulting components from this transformation is defined in such a way that the first principal component has the highest variance and accounts for as most of the variability in the data (see http://wapedia.mobi/en/Scree_plot)

For each principal component you can see which variables contribute

most to that component. Depending on what you used to do PCA in R, you

can use loadings() function. Loadings function gives a matrix that

shows how each variable contribute to the principal components. You

can do barplots for each principal component. That will visualize what

contributes to which principal component. You should do the barplots

for absolute values in the loadings matrix.

Take a look at the sources below, especially the second one

check here basic usage of PCA function in R

http://www.ysbl.york.ac.uk/~

check here for detailed explanation on loadings()

especially check the section : "How do we know which species

contribute to which axes? We look at the component loadings (or

"factor loadings"): "

http://ordination.okstate.edu/

Great explanation. Other awesome ressources on PCA: http://www.sthda.com/english/wiki/factominer-and-factoextra-principal-component-analysis-visualization-r-software-and-data-mining

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