R Lang

# R Program to Generate Random Number from Standard Distributions2 min read

Here is an example of an R program that generates a random number from the standard normal distribution (also known as a Gaussian or normal distribution) using the `rnorm()` function from the `stats` library:

This program uses the `rnorm()` function to generate a single random number from the standard normal distribution with a mean of 0 and a standard deviation of 1. The `n` argument specifies the number of random numbers to generate, which in this case is 1. The result is stored in the variable `x`

You can generate random numbers from other distributions, like uniform, exponential and etc. For example, the `runif()` function to generate random numbers from a uniform distribution with a specified range, the `rexp()` function to generate random numbers from an exponential distribution.

Here’s an example for the uniform distribution:

And an example for the exponential distribution

In R, the `rnorm()` function generates random numbers from a normal distribution with a specified mean and standard deviation. By default, `rnorm()` generates random numbers from the standard normal distribution, which has a mean of 0 and a standard deviation of 1.

The `runif()` function generates random numbers from a uniform distribution, it take 3 arguments `n`, `min`, and `max` where `n` is the number of random numbers you want to generate, `min` and `max` specifies the range of the uniform distribution.

The `rexp()` function generates random numbers from an exponential distribution, it takes 2 arguments `n` and `rate` where `n` is the number of random numbers you want to generate and `rate` is the inverse of the mean of the distribution.

The `rnorm()`, `runif()` and `rexp()` are all part of the R base package and are used to generate random numbers from specific probability distributions. These functions are often used in simulations and statistical modeling. When you call these functions, R uses a built-in random number generator to produce a sequence of random numbers that follow the specified probability distribution.

You can also specify a seed for the random number generator using the `set.seed()` function which allows you to reproduce the same sequence of random numbers for a given seed value. This can be useful for debugging and for comparing results between different runs of a simulation or statistical model.