This recipe meant to introduce and explain several useful arguments available at geom_histogram(). To spice things up, a problem context based on the game of chances is adopted. It is an optimal scenario to explain how histogram features can work both to display Monte Carlo simulations and aid hypothesis decisions. Once this context is explained, extrapolating it to challenges of your own won't be difficult.
In order to keep the focus on what matters, let's elaborate a very simple paradigma, design a game, and simulate it over and over again. The game rules can be summarized as following:
- Initial bet is fixed on $75
- Player decides before hand the number of rounds he is going through
- For each round, a fair coin is tossed (fifty-fifty chance for each outcome)
- Tail means current balance (positive or negative...
