clustering before or after PCA?
This is often done to visualize if there is any structure in the data. Often you color the clustering differently to check if samples from the same cluster are close.
Often data contains a lot of redundant information. With many dimensions, you get the curse of dimensionality. This can lead to a few large and many small clusters. By reducing the dimensionality to a few informative features the clustering solution often improves. This is very dependent on the dataset. Perhaps all of your dimensions are informative then dimensionality reduction won't help much. Look at scoring metrics such as silhouette analysis/score, gap statistic, or elbow method.


In your case, most of the dimensions seem informative. Perhaps you can remove one or two dimensions, but it likely won't have much effect on the clustering solution.

You can choose whatever PC's you'd like but they are ordered according to the amount of variance they explain. You can look at how much the different original features contribute to the different PC's.