I use prcomp to run PCA in r. When I output the summary, i.e. standard deviation, proportion of variance, cumulative proportion, the results are always ordered and the actual column name is replaced by PC1, PC2. Thus, I cannot tell the exact proportion of variance for each column.
Can anyone show me or give me some hint on how to display the column when outputting summary results. Two results pics are attached here:
It is not clear that you understand what principal components does. It reduces the dimensionality of the data. Assuming the rows are observations and the columns are variables, imagine plotting your rows in 35 dimensions (the columns). Most people have trouble visualizing more than 3 dimensions. Principal components creates a smaller set of axes that explains most the the variation in the data. The axes are Euclidian meaning they are at right angles to one another. Your plot and the result of the summary(res.pca5) and plot(res.pca5) functions show that the first dimension explains 28% of the variation in the 35 variables. Adding a second dimension gives you almost 38% and three gives you 44%. These new variables are combinations of your original variables, not the original variables. The first two components explain more of the variability than any other combination.
For some reason you did not try res.pca5 as a command (or the equivalent print(res.pca5)) which would show you the coefficients that pca used to create the components from the original variables or biplot(res.pca5) which plots the rows and columns in the new two dimensional space.
