We will use the `clusplot` package from the `cluster` library to plot the cluster assignments based upon the first two principal components. If you recall, principal components are a way to reduce the number of variables in this exercise to two, mainly so that we can graph it on a two-dimensional *x* and *y* axis.

If you run the following command, you will see that the first component is a measure of the price and number of units bought, and the second component is a measure of time since the last purchase. That is what is represented on the

*x*and*y*axis. This is trivial for this case since the cluster model only has three variables as input, but becomes more important as the number of variables you use increases:`prcomp(append.clust[,c(3,4,6)], ...`