Communicating PCA results

This exercise will check your understanding of the PCA results, in particular the loadings and variance explained. The loadings, represented as vectors, explain the mapping from the original features to the principal components. The principal components are naturally ordered from the most variance explained to the least variance explained.

The variables you created before—wisc.data, diagnosis, wisc.pr, and pve—are still available.

For the first principal component, what is the component of the loading vector for the feature concave.points_mean? What is the minimum number of principal components required to explain 80% of the variance of the data?