Just index back into the original data using the cluster ID of your choice. Then you can easily do whatever further processing you want to the subset. Here is an example:
library(fpc)
n = 100
set.seed(12345)
data = matrix(rnorm(n*3), nrow=n)
data.ds = dbscan(data, 0.5)
> data.ds
dbscan Pts=100 MinPts=5 eps=0.5
0 1 2 3
seed 0 1 3 1
border 83 4 4 4
total 83 5 7 5
> colMeans(data[data.ds$cluster==0, ])
[1] 0.28521404 -0.02804152 -0.06836167