  The $r$-index (Gagie et al., JACM 2020) represented a breakthrough in
compressed indexing of repetitive text collections, outperforming its
alternatives by orders of magnitude. Its space usage, $\mathcal{O}(r)$ where
$r$ is the number of runs in the Burrows-Wheeler Transform of the text, is
however larger than Lempel-Ziv and grammar-based indexes, and makes it
uninteresting in various real-life scenarios of milder repetitiveness. In this
paper we introduce the $sr$-index, a variant that limits the space to
$\mathcal{O}(\min(r,n/s))$ for a text of length $n$ and a given parameter $s$,
at the expense of multiplying by $s$ the time per occurrence reported. The
$sr$-index is obtained by carefully subsampling the text positions indexed by
the $r$-index, in a way that we prove is still able to support pattern matching
with guaranteed performance. Our experiments demonstrate that the $sr$-index
sharply outperforms virtually every other compressed index on repetitive texts,
both in time and space, even matching the performance of the $r$-index while
using 1.5--3.0 times less space. Only some Lempel-Ziv-based indexes achieve
better compression than the $sr$-index, using about half the space, but they
are an order of magnitude slower.
