Virtual valuation is the derivative of the expected revenue function with respect to the tail probability $q$ that is then evaluated at value $v$. The Revenue function is
$$R(q) = q\cdot v(q),\;\;\; q = 1-F(v),\;\;\; v(q) = F^{-1}(1-q) \tag{1}$$
and
$$r(v(q)): = \frac{dR(q)}{dq}= \frac{d}{dq}\big[q\cdot F^{-1}(1-q)\big] \tag{2}$$
written, after the calculations, in terms of $v$, so
$$r(v)=v - \frac{1-F(v)}{f(v)} \tag{3}$$
If $v$ is discrete we have
$$q_j = 1-F(v\leq v_j),\;\;\; j=1,...,k \tag{4}$$
where now $F$ is a distribution function of a discrete rv, an where $j$ counts the ordered discrete values that $v$ may take, $v \in \{v_1,...,v_k\}$.
Discretizing the relationship we could define
$$r(v(q_j, q_{j+1})):= \frac{R(q_{j+1})-R(q_j)}{q_{j+1}-q_j} \tag{5}$$
which in terms of $v$ becomes
$$r(v(q_j, q_{j+1})):= \frac{v_{j+1}\cdot [1- F(v\leq v_{j+1})] -v_{j}\cdot [1- F(v\leq v_{j})]}{1- F(v\leq v_{j+1})-1+F(v\leq v_{j})} $$
$$=\frac{v_{j+1}\cdot Pr(v > v_{j+1}) -v_{j}\cdot Pr(v > v_{j})}{-Pr(v=v_{j+1})}$$
Using
$$Pr(v > v_{j}) = Pr(v=v_{j+1}) + Pr(v>v_{j+1})$$
we get
$$r(v_j,v_{j+1}) = v_{j} - (v_{j+1}-v_j)\cdot \frac{Pr(v>v_{j+1})}{Pr(v=v_{j+1})}$$
or
$$r(v_j,v_{j+1}) = v_{j} - (v_{j+1}-v_j)\cdot \frac{1-F(v_{j+1})}{Pr(v=v_{j+1})} \tag{6}$$
Since to a first approximation, if $v$ is continuous, and $v_j,v_{j+1}$ are "close enough" we have
$$Pr[v \in (v_j,v_{j+1})] \approx f(v_{j+1}) \cdot (v_{j+1}-v_{j})$$
It is clear how the obtained relation for the discrete case can be seen as the analogue of the continuous case.
For practical applications, one could think also of using a "continuity correction" for the probability in the denominator of $(6)$.
