# Binomial Random Numbers Generation in R

## Table of Contents

We will learn how to generate Bernoulli or Binomial Random Numbers (Binomial distribution) in R with the example of a flip of a coin. This tutorial is based on how to generate random numbers according to different statistical probability distributions in R. Our focus is on binomial random numbers generation in R.

### Binomial Random Numbers in R

We know that in Bernoulli distribution, either something will happen or not such as a coin flip has two outcomes head or tail (either head will occur or head will not occur i.e. tail will occur). For an unbiased coin, there will be a 50% chance that the head or tail will occur in the long run. To generate a random number that is binomial in R, use the rbinom(n, size, prob) command.

rbinom(n, size, prob) #command has three parameters, namey

where
‘$n$’ is the number of observations
‘$size$’ is the number of trials (it may be zero or more)
‘$prob$’ is the probability of success on each trial for example 1/2

### Examples of Generation Binomial Random Numbers

• One coin is tossed 10 times with a probability of success=0.5
the coin will be fair (unbiased coin as p=1/2)
rbinom(n=10, size=1, prob=1/2)
OUTPUT: 1 1 0 0 1 1 1 1 0 1
• Two coins are tossed 10 times with a probability of success=0.5
• rbinom(n=10, size=2, prob=1/2)
OUTPUT: 2 1 2 1 2 0 1 0 0 1
• One coin is tossed one hundred thousand times with a probability of success=0.5
rbinom(n=100,000, size=1, prob=1/2)
• store simulation results in $x$ vector
x <- rbinom(n=100000, size=5, prob=1/2)
count 1’s in x vector
sum(x)
find the frequency distribution
table(x)
creates a frequency distribution table with frequency
t = (table(x)/n *100)
plot frequency distribution table
plot(table(x),ylab = "Probability",main = "size=5,prob=0.5")

View the Video tutorial on rbinom command

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