Blackjack simulator: A gamified R package

R Package Development · C++ Integration · Simulation

Blackjack is a game of probability. Every decision has a mathematically optimal answer depending on your hand and the dealer’s visible card. So when our team was tasked with building a gamified R package, Blackjack felt like a natural fit. It’s simple enough to explain in one sentence, but deep enough to keep a statistician busy for hours.

The result is blackjackr: an interactive R package that lets you play Blackjack, simulate thousands of rounds, and test strategies, all from your R console.

My contributions

This was a team project. My specific contributions across both parts of the project were:

• C++ programming for the deal_hand() function for performance
• Building core functions and unit tests
• Writing the introduction vignette and README
• Debugging errors during R CMD checks
• Refining roxygen documentation comments

Installing the package

install.packages("devtools")
devtools::install_github("MonashARP/game-package-bilbies")

Playing a round

Once installed, you can play a full interactive round of Blackjack straight from the console. The game prompts you to hit, stand, double down, or split depending on your hand and balance.

library(blackjack)

# Play a single round with a starting bet of $100
play_blackjack(bet = 100, n_decks = 4)

You can also run a multiplayer session with up to several players each taking turns against the dealer:

play_blackjack_multi(num_players = 3, bet = 50)

Working with cards

The package includes a set of utility functions for inspecting card properties. These are useful for building game logic or debugging:

library(blackjack)

deck <- create_shuffled_deck()
card <- deck[1]

card_name(card)

[1] "5♥"

card_rank(card)

[1] "Five"

card_value(card)

[1] 5

card_is_face(card)

[1] FALSE

Simulating strategy

The most analytically interesting part of the package is simulation_blackjack(). It lets you simulate thousands of rounds automatically and observe outcomes. This is where the probability really comes to life.

results <- simulation_blackjack(
  n_simulations = 1000,
  bet = 100,
  n_decks = 4
)

summary(results)

Number of cases in table: 1000 
Number of factors: 1 

From script to package

There’s a significant gap between writing a script that works on your machine and building a package that someone else can install, run, and trust. Closing that gap meant documentation for every function, unit tests for edge cases, and a Git workflow with a collaborator branching, reviewing, merging, and catching each other’s oversights before they became problems. The constraints of proper package development are uncomfortable at first, but they produce better software.

View on GitHub Package documentation