Converting a algorithm to a runtime function I need to find an upper limit for the runtime of $f(n)$.

f(n):
{
 g(n,1)
}

g(n,k):
{
 if n&lt;=0 return;
 for(i=1; i&lt;=n; i++)
 {
  print "I love data structures!";
  k++;
  g(n-1, k);
 }
 return;
}


I tried to think of it this way:

for(i=1; i&lt;=n; i++) $\rightarrow (n+1)c_2$

g(n-1, k) $\rightarrow ng(n-1)$

$$f(n) = g(n,1) + c_f = c_1 + (n+1)c_2 + nc_3 + nc_4 + ng(n-1) + c_f$$

I am not sure about the recursion runtime analysis:

g(n-1,k) $\rightarrow ng(n-1)$

Thank you!
The recursion for $g$'s running time is $$T(n)=n(T(n-1)+1)$$ where $1$ is the (constant) work done from the print statement and incrementing $k$. $g(n-1,k)$ and the constant work are called $n$ times from the for loop.
The recursion stops at $g(0,k)$ which has running time $T(0)=1$.

Solving this recursion on Mathematica (I suspect doing it by hand would involve induction or inspection) gave the result:
$$T(n)=\Gamma(n+1)+en\Gamma(n,1)$$
The complexity of this is
$$T(n)=O(\Gamma(n+1)+en\Gamma(n,1))$$
$$T(n)=O(n\Gamma(n,1))$$
Since $n$ is an integer, then we can reduce this to
$$T(n)=O(n!)$$