# 30 Basic Simulations

Random numbers have many applications in science and computer programming, especially when there are significant uncertainties in a phenomenon of interest. In this tutorial we’ll look at a basic problem that involves working with random numbers and creating simulations.

More specifically, let’s see how to use R to simulate basic chance processes like tossing a coin.

## 30.1 Let’s flip a coin

Chance processes, also referred to as chance experiments, have to do with actions in which the resulting outcome turns out to be different in each occurrence.

Typical examples of basic chance processes are tossing one or more coins, rolling one or more dice, selecting one or more cards from a deck of cards, and in general, things that can be framed in terms of drawing tickets out of a box (or any other type of container: bag, urn, etc.).

You can use your computer, and R in particular, to simulate chances processes. In order to do that, the first step consists of learning how to create a virtual coin, or die, or box-with-tickets.

### 30.1.1 Creating a coin

The simplest way to create a coin with two sides, `"heads"`

and `"tails"`

, is
with an R character vector via the *combine* function `c()`

You can also create a *numeric* coin that shows `1`

and `0`

instead of
`"heads"`

and `"tails"`

:

Likewise, you can also create a *logical* coin that shows `TRUE`

and `FALSE`

instead of `"heads"`

and `"tails"`

:

## 30.2 Tossing a coin

Once you have an object that represents the *coin*, the next step involves
learning how to simulate tossing the coin. One way to simulate the action of
tossing a coin in R is with the function `sample()`

which lets you draw
random samples, with or without replacement, from an input vector.

To toss the coin use `sample()`

like this:

with the argument `size =`

, specifying that we want to take a sample of size 1
from the input vector `coin`

.

### 30.2.1 Function `sample.int()`

Another function related to `sample()`

is `sample.int()`

which simulates
drawing random integers. The main argument is `n`

, which represents the maximum
integer to sample from: `1, 2, 3, ..., n`

### 30.2.2 Random Samples

By default, `sample()`

draws each element in `coin`

with the same probability.
In other words, each element is assigned the same probability of being chosen.
Another default behavior of `sample()`

is to take a sample of the specified
`size`

**without replacement**. If `size = 1`

, it does not really matter whether
the sample is done with or without replacement.

To draw two elements WITHOUT replacement, use `sample()`

like this:

What if we try to toss the coin three or four times?

```
# trying to toss coin 3 times
sample(coin, size = 3)
#> Error in sample.int(length(x), size, replace, prob): cannot take a sample larger than the population when 'replace = FALSE'
```

Notice that R produced an error message. This is because the default behavior
of `sample()`

cannot draw more elements that the length of the input vector.

To be able to draw more elements, we need to sample WITH replacement, which is
done by specifying the argument `replace = TRUE`

, like this:

## 30.3 The Random Seed

The way `sample()`

works is by taking a random sample from the input vector.
This means that every time you invoke `sample()`

you will likely get a different
output.

In order to make the examples replicable (so you can get the same output as me),
you need to specify what is called a **random seed**. This is done with the
function `set.seed()`

. By setting a *seed*, every time you use one of the random
generator functions, like `sample()`

, you will get the same values.

```
# set random seed
set.seed(1257)
# toss a coin with replacement
sample(coin, size = 4, replace = TRUE)
#> [1] "tails" "heads" "heads" "tails"
```

All computations of random numbers are based on deterministic algorithms, so the sequence of numbers is not truly random. However, the sequence of numbers appears to lack any systematic pattern, and we can therefore regard the numbers as random.

Every time you use one of the random generator functions in R, the call
produces different numbers. For replication and debugging purposes, it is
useful to get the same sequence of random numebrs every time we run the script.
This functionality is obtained by setting a **seed** before we start generating
the numebrs. The seed is an integer and set by the function `set.seed()`

If we set the seed to `123`

again, the sequence of uniform random numbers is
regenerated:

If we don’t specify a seed, the random generator functions set a seed based on the current time. That is, the seed will be different each time we run the script and consequently the sequence of random numbers will also be different.

## 30.4 Sampling with different probabilities

Last but not least, `sample()`

comes with the argument `prob`

which allows you
to provide specific probabilities for each element in the input vector.

By default, `prob = NULL`

, which means that every element has the same
probability of being drawn. In the example of tossing a coin, the command
`sample(coin)`

is equivalent to `sample(coin, prob = c(0.5, 0.5))`

. In the
latter case we explicitly specify a probability of 50% chance of heads, and
50% chance of tails:

```
#> [1] "heads" "tails"
#> [1] "heads" "tails"
```

However, you can provide different probabilities for each of the elements in
the input vector. For instance, to simulate a **loaded** coin with chance of
heads 20%, and chance of tails 80%, set `prob = c(0.2, 0.8)`

like so:

## 30.5 Simulating tossing a coin

Now that we have all the elements to toss a coin with R, let’s simulate flipping
a coin 100 times, and use the function `table()`

to count the resulting number
of `"heads"`

and `"tails"`

:

```
# number of flips
num_flips <- 100
# flips simulation
coin <- c('heads', 'tails')
flips <- sample(coin, size = num_flips, replace = TRUE)
# number of heads and tails
freqs <- table(flips)
freqs
#> flips
#> heads tails
#> 56 44
```

In my case, I got 56 heads and 44 tails. Your results will
probably be different than mine. Some of you will get more `"heads"`

, some of
you will get more `"tails"`

, and some will get exactly 50 `"heads"`

and 50
`"tails"`

.

Run another series of 100 flips, and find the frequency of `"heads"`

and `"tails"`

:

## 30.6 Tossing function

Let’s make things a little bit more complex but also more interesting.
Instead of calling `sample()`

every time we want to toss a coin, we can
write a `toss()`

function:

```
#' @title coin toss function
#' @description simulates tossing a coin a given number of times
#' @param x coin object (a vector)
#' @param times number of tosses
#' @return vector of tosses
toss <- function(x, times = 1) {
sample(x, size = times, replace = TRUE)
}
# basic call
toss(coin)
#> [1] "tails"
# toss 5 times
toss(coin, 5)
#> [1] "tails" "tails" "tails" "heads" "tails"
```

We can make the function more versatile by adding a `prob`

argument that let
us specify different probabilities for `heads`

and `tails`

```
#' @title coin toss function
#' @description simulates tossing a coin a given number of times
#' @param x coin object (a vector)
#' @param times number of tosses
#' @param prob vector of probabilities for each side of the coin
#' @return vector of tosses
toss <- function(x, times = 1, prob = NULL) {
sample(x, size = times, replace = TRUE, prob = prob)
}
# toss a loaded coin 10 times
toss(coin, times = 10, prob = c(0.8, 0.2))
#> [1] "tails" "heads" "tails" "heads" "heads" "heads" "heads" "heads" "heads"
#> [10] "heads"
```

## 30.7 Counting Frequencies

The next step is to toss a coin several times, and count the frequency of
`heads`

and `tails`

```
# count frequencies
tosses <- toss(coin, times = 100)
table(tosses)
#> tosses
#> heads tails
#> 56 44
```

We can also count the relative frequencies:

To make things more interesting, let’s consider how the frequency of `heads`

evolves over a series of `n`

tosses.

In this case, we can make a plot of the relative frequencies: