What is the use of runif function?
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# What is the use of runif function?

This recipe explains what is the use of runif function

0

## 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. ​

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. ​

runif() function generates random numbers from uniform distribution. ​

In this recipe, you will learn how to generate a random uniform distribution using runif. ​

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: 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) round(uniform_dist) ```
```26 23 9 16 29 29 4 3 11 12 22 23 1 23 7 14 10 4 9 25 15 2 14 3 9 3 27 30 3 21 11 14 25 6 16 15 15 27 20 8 18 2 14 15 23 14 20 23 13 20 3 15 9 29 5 16 1 14 9 2 13 4 28 2 28 2 26 18 5 18 2 13 6 18 27 12 17 15 20 9 7 27 3 28 22 14 11 16 5 2 16 10 25 29 25 26 26 23 11 2
```

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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