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This vignette explores three alternative types of decks offered by the ‘mmcards’ package that may better suit your specific use case compared to a standard 52-card deck. First, load the ‘mmcards’ library, and then we’ll play simple games to illustrate how each deck works.
Suppose you’re teaching a statistical concept and want to simulate drawing samples from a population. In that case, you can define a custom AnonymousDeck and play a game to drive the idea home.
# Define the Players
p1 <- NULL
p2 <- NULL
# Create a shuffled anonymous deck of 10 cards using a random number seed for reproducibility
game_deck <- shuffle_deck(deck_of_cards = function(x){rnorm(10, 0, 5)},
seed = 147)
# Deal a card to each player
game_deck <- deal_card(game_deck)
p1 <- game_deck$dealt_card
game_deck <- deal_card(game_deck)
p2 <- game_deck$dealt_card
# Display the cards and determine the winner
paste("p1 has:", p1$card, "with a value of", p1$value)
#> [1] "p1 has: 8 with a value of 2.21542965936812"
paste("p2 has:", p2$card, "with a value of", p2$value)
#> [1] "p2 has: 1 with a value of -9.39463929631786"
paste0("The winner is: ", ifelse(abs(p1$value) < abs(p2$value), "p1", "p2"))
#> [1] "The winner is: p1"
# Here are the remaining 8 cards in the deck
print(game_deck$updated_deck)
#> card value
#> 5 5 -2.519066
#> 3 3 -2.771481
#> 4 4 -2.678679
#> 6 6 -1.461480
#> 2 2 -3.768717
#> 9 9 4.658202
#> 10 10 8.255931
#> 7 7 1.201079If you want to simulate drawing samples from two distinct populations, an InterleavedDeck can be useful.
# Define the Players
p1 <- NULL
p2 <- NULL
# Create a shuffled interleaved deck of 10 cards using a random number seed for reproducibility
game_deck <- shuffle_deck(deck_of_cards = function(x){list(rnorm(5, 0, 5),
rnorm(5, 0, 3))},
seed = 157)
# Deal a card to each player
game_deck <- deal_card(game_deck)
p1 <- game_deck$dealt_card
game_deck <- deal_card(game_deck)
p2 <- game_deck$dealt_card
# Display the cards and determine the winner
paste("p1 has:", p1$card, "with a value of", p1$value)
#> [1] "p1 has: A_3 with a value of 2.5268783375614"
paste("p2 has:", p2$card, "with a value of", p2$value)
#> [1] "p2 has: B_2 with a value of -1.45153422895941"
paste0("The winner is: ", ifelse(abs(p1$value) < abs(p2$value), "p1", "p2"))
#> [1] "The winner is: p2"
# Here are the remaining 8 cards in the deck
print(game_deck$updated_deck)
#> card value
#> 2 A_2 2.58087475
#> 41 B_4 3.56098941
#> 4 A_4 9.75742338
#> 51 B_5 2.71402888
#> 1 A_1 0.06059832
#> 31 B_3 -3.18706595
#> 5 A_5 8.46972493
#> 11 B_1 -0.79511543A PairedDeck is ideal for games that require maintaining a natural pairing between cards, such as in a paired t-test scenario.
# Define the hands
LH <- NULL
RH <- NULL
# Create a shuffled paired deck of 20 cards using a random number seed for reproducibility
game_deck <- shuffle_deck(deck_of_cards = function(x){list(rpois(10, 30),
rnorm(10, 40))},
seed = 232,
paired = TRUE)
# For the student whose cards came out on top, get the hang time for each hand
game_deck <- deal_card(game_deck)
LH <- game_deck$dealt_card
game_deck <- deal_card(game_deck)
RH <- game_deck$dealt_card
# Display the cards and determine which hand was stronger
paste("This student's left hand card is:",
LH$card, "with a value of", LH$value)
#> [1] "This student's left hand card is: A_6 with a value of 32"
paste("This student's right hand card is:", RH$card, "with a value of", RH$value)
#> [1] "This student's right hand card is: B_6 with a value of 40.8653697233691"
paste0("Did this student hang for longer with left hand?: ",
ifelse(LH$value > RH$value, "Yes", "No"))
#> [1] "Did this student hang for longer with left hand?: No"
# Here are the remaining students' hang times
print(game_deck$updated_deck)
#> card value
#> 2 A_2 23.00000
#> 21 B_2 41.56952
#> 9 A_9 30.00000
#> 91 B_9 39.67030
#> 5 A_5 32.00000
#> 51 B_5 39.52775
#> 7 A_7 23.00000
#> 71 B_7 40.55928
#> 4 A_4 21.00000
#> 41 B_4 41.16555
#> 3 A_3 36.00000
#> 31 B_3 41.39362
#> 10 A_10 31.00000
#> 101 B_10 37.64896
#> 8 A_8 21.00000
#> 81 B_8 40.62200
#> 1 A_1 39.00000
#> 11 B_1 40.82121Notice that the students’ order is shuffled but the LH and RH pairs are intact.
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