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How to create a matrix with random values in R?

  • Last Updated : 16 May, 2021

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 observations
min, 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 Size
lambda: 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 frame
size: indicates size of sample to be taken
replace: 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:




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