# How to create a matrix with random values in R?

In this article, we will discuss hw to create a matrix with random values in the R programming language.

**The functions which are used to generate the random values are:**

- rnorm()
- runif()
- rexp()
- rpois()
- rbinom()
- sample()

We will use all these functions one by one to create the matrix with random values.

**Method 1: Using rnorm()**

rnorm() function basically creates the random values according to the normal distribution.

Syntax:rnorm(n, mean, sd)

So, we give 25 as an argument in the rnorm() function, after that put those values in the matrix function with the row number and create the matrix.

## R

`# matrix create with the help` `# of matrix function random values` `# generated with the help of rnorm()` `m<-` `matrix` `(` `rnorm` `(25) , nrow = 5)` ` ` `# print the matrix` `print` `(m)` |

**Output:**

**Method 2: Using ****runif()**** function**

runif() function basically creates the random values according to the uniform distribution. So, we give 25 as an argument in the runif() function.

Syntax:runif(n, min, max)

Parameters:n:represents number of observationsmin, max:represents lower and upper limits of the distribution

**Code:**

## R

`# matrix create with the help ` `# of matrix function random values ` `# generated with the help of runif()` `m <- ` `matrix` `( ` `ruif` `(25), nrow = 5)` ` ` `# print the matrix` `print` `(m)` |

**Output:**

**Method 3: Using rexp() function**

rexp() function basically creates the random values according to the exponential distribution. So, we give 25 as an argument in the rexp() function.

Syntax:rexp(N, rate )

**Code:**

## R

`# matrix create with the help ` `# of matrix function random values` `# generated with the help of runif()` `m <- ` `matrix` `( ` `runif` `(25), nrow = 5)` ` ` `# print the matrix` `print` `(m)` |

**Output:**

**Method 4: Using ****rpois()**** function**

** **In this example, we will try to create the random values using the rpois(). rpois() function basically creates the random values according to the Poisson distribution x ~ P(lambda). So, we give 25 and 5 as an argument in the rpois() function.

Syntax:rpois(N, lambda)

Parameters:N:Sample Sizelambda:Average number of events per interval

**Code:**

## R

`# matrix create with the help ` `# of matrix function random values ` `# generated with the help of rpois()` `m <- ` `matrix` `(` `rpois` `( 25, 5), nrow = 5)` ` ` `# print the matrix` `print` `(m)` |

**Output:**

**Method 5: Using rbinom() function**

In this example, we will try to create the random values using the rbinom(). rbinom() function basically creates the random values of a given probability.

rbinom(n, N, p)

Where n is the number of observations, N is the total number of trials, p is the probability of success. So, we give 25, 5, and .6 as an argument in the rbinom() function.

**Code:**

## R

`# matrix create with the help` `# of matrix function random values` `# generated with the help of rbinom()` `m <- ` `matrix` `(` `rbinom` `( 25, 5, .6), nrow = 5)` ` ` `# print the matrix` `print` `(m)` |

**Output:**

**Method 6: Using ****sample()**** function**

In this example, we will try to create random values using the sample(). sample() function basically creates the random values of given elements.

Syntax:sample(x, size, replace)

Parameters:x:indicates either vector or a positive integer or data framesize:indicates size of sample to be takenreplace:indicates logical value. If TRUE, sample may have more than one same value

So, we give 1:20 and 100 as an argument in the sample() function.

**Code:**

## R

`# matrix create with the help` `# of matrix function random values` `# generated with the help of sample()` `m <- ` `matrix` `(` `sample` `(` ` ` `1 : 20, 100, replace = ` `TRUE` `), ncol = 10)` ` ` `# print the matrix` `print` `(m)` |

**Output:**