# tidydice

Simulates Dice Rolls and Coin Flips.

### Introduction

A basic understanding of probability and statistics is crucial for data understanding. A great way to teach probability and statistics is to start with an experiment, like rolling a dice or flipping a coin.

This package simulates rolling a dice and flipping a coin. Each experiment generates a tibble. Dice rolls and coin flips are simulated using sample(). The properties of the dice can be changed, like the number of sides. A coin flip is simulated using a two sided dice. Experiments can be combined with the pipe-operator.

## Installation

### CRAN

``install.packages("tidydice")``

### DEV version (github)

``````# install from github
if (!require(devtools)) install.packages("devtools")
devtools::install_github("rolkra/tidydice")``````

if you are behind a firewall, you may want to:

• Then install it with devtools::install_local
``````# install local
if (!require(devtools)) install.packages("devtools")
devtools::install_local(path = <path of local package>, force = TRUE)``````

## Basic example

Let’s roll 60 dice:

``````# load packages
library(tidydice)

# roll 60 dice (10 x 6 dice = 60)
data <- roll_dice(times = 10, rounds = 6)
data``````

We get tidy data, where each row is a dice. It is a success, if the result is a 6.

``````# A tibble: 60 × 5
experiment round    nr result success
<int> <int> <int>  <int> <lgl>
1          1     1     1      5 FALSE
2          1     1     2      6 TRUE
3          1     1     3      6 TRUE
4          1     1     4      1 FALSE
5          1     1     5      5 FALSE
6          1     1     6      1 FALSE
7          1     1     7      4 FALSE
8          1     1     8      5 FALSE
9          1     1     9      1 FALSE
10          1     1    10      2 FALSE
# … with 50 more rows``````

Now let’s plot it:

``data |> plot_dice()``

We got 13 six. Is this unlikely? The expected value is 10 (60 dice / 6 sides = 10). So 13 is more than expected, is it a sign of cheating? Let’s check using the binomial ditribution:

``````# binomial distribution
binom_dice(times = 60) |>
plot_binom(highlight = c(13:60))``````

The binomial distribution shows, that there is a 19% chance that you can get 13 or more six using a fair dice.

## Roll dice

``````# load packages
library(tidydice)

# roll a dice
roll_dice()

# roll a dice 6x
roll_dice(times = 6)

# roll a dice 6x and plot result
roll_dice(times = 6) |>
plot_dice()

# repeat 6x
roll_dice(times = 6, rounds = 6)  |>
plot_dice()

# count success per round
roll_dice(times = 6, rounds = 6, agg = TRUE)

# Binomial distribution
binom_dice(times = 6)

# Binomial distribution + plot
binom_dice(times = 6) |>
plot_binom()

# Binomial distribution + plot
binom_dice(times = 6) |>
plot_binom(highlight = 0:2)``````

To do more complex dice rolls use `roll_dice_formula()`:

``````library(tidydice)

roll_dice_formula(
dice_formula = "4d6e3", # 4 dice with 6 sides, explode on a 3
rounds = 5,             # repeat 5 times
success = 15:24,        # success is defined as sum between 15 and 24
seed = 123              # random seed to make it reproducible
)``````
• `1d6` = roll one 6-sided dice
• `1d8` = roll one 8-sided dice
• `1d12` = roll one 12-sided dice
• `2d6` = roll two 6-sided dice
• `1d6e6` = roll one 6-sided dice, explode dice on a 6
• `3d6kh2` = roll three 6-sided dice, keep highest 2 rolls
• `3d6kl2` = roll three 6-sided dice, keep lowest 2 rolls
• `4d6kh3e6` = roll four 6-sided dice, keep highest 3 rolls, but explode on a 6
• `1d20+4` = roll one 20-sided dice, and add 4
• `1d4+1d6` = roll one 4-sided dice and one 6-sided dice, and sum the results

## Flip coin

``````# load packages
library(tidydice)

# flip a coin
flip_coin()

# flip a coin 10x
flip_coin(times = 10)

# flip a coin 10x and plot result
flip_coin(times = 10) |>
plot_coin()

# repeat 10x and plot result
flip_coin(times = 10, rounds = 10) |>
plot_coin()

# count success per round
flip_coin(times = 10, rounds = 10, agg = TRUE)

# Binomial distribution
binom_coin(times = 10)

# Binomial distribution + plot
binom_coin(times = 10) |>
plot_binom()

# Binomial distribution + plot
binom_coin(times = 10) |>
plot_binom(highlight = 0:2)``````