R and probability noob here. I'm looking to create a histogram that shows the distribution of how many attempts it took to return a heads, repeated over 1000+ simulated runs on the equivalent of an unfairly weighted coin (0.1 heads, 0.9 tails).

From my understanding, this is not a geometric distribution or binomial distribution (but might make use of either of these to create the simulated results).

The real-world (ish) scenario I am looking to model this for is a speedrun of the game Zelda: Ocarina of Time. One of the goals in this speedrun is to obtain an item from a character that has a 1 in 10 chance of giving the player the item each attempt. As such, the player stops attempting once they receive the item (which they have a 1/10 chance of receiving each attempt). Every run, runners/viewers will keep track of how many attempts it took to receive the item during that run, as this affects the time it takes the runner to complete the game.

This is an example of what I'm looking to create:

(though with more detailed labels on the x axis if possible). In this, I manually flipped a virtual coin with a 1/10 chance of heads over and over. Once I got a successful result I recorded how many attempts it took into a vector in R and then repeated about 100 times - I then mapped this vector onto a histogram to visualise what the distribution would look like for the usual amount of attempts it will take to get a successful result - basically, i'd like to automate this simulation instead of me having to manually flip the virtual unfair coin, write down how many attempts it took before heads, and then enter it into R myself).