How to create a uniform distribution in R?
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How to create a uniform distribution in R?

How to create a uniform distribution in R?

This recipe helps you create a uniform distribution in R

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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 uniform distribution.. ​

Uniform distribution is a type of probability distribution in which all the numeric variables have an equal probability to occur. The are the most popular type of distribution in generating random numbers. ​

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 uniform distribution by generating 100 random numbers from a uniform distribution by seeting a seed

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

Syntax: runif(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 uniform_dist = runif(100, min = 1, max = 30) #plotting a histogram of the generated numbers using hist() function hist(uniform_dist, breaks = 3)

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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