Retrieving PCA variables coefficients from PCA components

Question

I am trying to explain a final score in the final assessment (predicted variable) via the scores of continuous assessment in seven subjects (predictive variables), of course I proceed with an ACP.

I get two principal components, the first one is explained largely with 3 subjects and the second is explained with two other subjects

##       eigenvalue variance.percent cumulative.variance.percent
## Dim.1 6.30728154       90.1040220                    90.10402
## Dim.2 0.25481341        3.6401916                    93.74421
## Dim.3 0.14504576        2.0720823                    95.81630
## Dim.4 0.10560431        1.5086330                    97.32493
## Dim.5 0.08744117        1.2491595                    98.57409
## Dim.6 0.05931222        0.8473174                    99.42141
## Dim.7 0.04050159        0.5785942                   100.00000

The first two components explain 93% of the variance, but I want to know the contribution of each subject to each component, so I make this chart:

So as you can see, the ACP is not reducing the dimension as much as I expected, because we can see the only two subjects could be eliminated, from 7 variables to 5 variables isn't good enough, is it because of a problem with the data, its containing only 95 observations ??

Please help out, thanks