How to create a normal distribution in R?

How to create a normal distribution in R?

How to create a normal distribution in R?

This recipe helps you create a normal distribution in R


Recipe Objective

Random numbers are generated in quite a few cases in statistics to carry out sampling and simulation. Mostly, a data scientist is in a need of a set of random numbers which are mostly taken from two types of distribution: ​

  1. Uniform distribution
  2. Normal distribultion

These random numbers generated mimic the properties of uniform or normal distribution in a certain interval. ​

In this recipe, you will learn how to create a random normal distribution.. ​

Normal distribution is a type of probability distribution which looks like a bell with co-inciding median, mode and mean. In this type of distribution, 68% observations are within 1 standard deviation from it's mean. ​

Note: Whenever we are generating random numbers, you are using an algorithm that requires a seed whose function is to initialise. These numbers are actually pseudorandom numbers which can be predicted if we know the seed and the generator. Setting a seed means iniltialising a pseudorandom generator. We set a seed when we need the same output of numbers everytime you want to generate random numbers. If we don't set a seed, the generated pseudorandom numbers are different on each execution. ​

Example:Creating a normal distribution by generating 100 random numbers from a normal distribution by seeting a seed

We use rnorm() function to carry out this task. ​

Syntax: rnorm(n, min = , max = ) ​

where: ​

  1. n = size of the distribution
  2. min, max = specifies the interval in which you would like the distribution to be

Additionally, use set.seed() function to set a seed. We specify any integer in the function as a seed. ​

# setting a seed set.seed(20) # using random numbers from normal distribution between 1 and 30 random_dist = rnorm(10000, mean = 0, sd = 1) #plotting a histogram of the generated numbers using hist() function hist(random_dist, breaks = 100)

Note: ​

  1. The distribution remains constant even after multiple execution.
  2. You can see that the mean, mode and median co-incides in the above plot indicating a normal distribution

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