  We study the positive occupation time of a run-and-tumble particle (RTP)
subject to stochastic resetting. Under the resetting protocol, the position of
the particle is reset to the origin at a random sequence of times that is
generated by a Poisson process with rate $r$. The velocity state is reset to
$\pm v$ with fixed probabilities $\rho_1$ and $\rho_{-1}=1-\rho_1$, where $v$
is the speed. We exploit the fact that the moment generating functions with and
without resetting are related by a renewal equation, and the latter generating
function can be calculated by solving a corresponding Feynman-Kac equation.
This allows us to numerically locate in Laplace space the largest real pole of
the moment generating function with resetting, and thus derive a large
deviation principle (LDP) for the occupation time probability density using the
Gartner-Ellis theorem. We explore how the LDP depends on the switching rate
$\alpha$ of the velocity state, the resetting rate $r$ and the probability
$\rho_1$. In particular, we show that the corresponding LDP for a Brownian
particle with resetting is recovered in the fast switching limit
$\alpha\rightarrow \infty$. On the other hand, the behavior in the slow
switching limit depends on $\rho_1$ in the resetting protocol.
