# Solution to the PCA exercise

pc = prcomp(quas[,-1])
summary(pc)
screeplot(pc)

**prcomp**

does not compute the projections for us. So we need to
carry out the matrix multiplication to find them ourselves.
scr=as.matrix(quas[,-1])%*%pc$rot[,1:2]

There are a couple of points in order here. First, both the
arguments in a matrix multiplication (**%*%**

) must be
matrices (or vectors). Here `quas[,-1]`

is a
dataframe, which needs to be converted to a matrix using the
**as.matrix**

function. Also, we are computing the
projections along the first two principal componnt only. Hence
we are using `pc$rot[,1:2]`

.
Now it is just a matter of plotting.
plot(scr)

The scale would be somewhat different here than for
**princomp**

, however the absolute scale is of no importance
here. It's the directions and the relative scatter along them
that matter.