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How to Find Standard Deviation in R?
• Last Updated : 07 Apr, 2021

In this article, we will discuss how to find the Standard Deviation in R Programming Language. Standard deviation is the measure of the dispersion of the values. It can also be defined as the square root of variance.

Formula of sample standard deviation:

where,

• s = sample standard deviation
• N = Number of entities
•  = Mean of entities

Basically, there are two different ways to calculate standard Deviation in R Programming language, both of them are discussed below.

### Method 1: Naive approach

In this method of calculating the standard deviation, we will be using the above standard formula of the sample standard deviation in R language.

Example 1:

## R

 v <- c(12,24,74,32,14,29,84,56,67,41)  s<-sqrt(sum((v-mean(v))^2/(length(v)-1)))  print(s)

Output:

[1] 25.53886

Example 2:

## R

 v <- c(1.8,3.7,9.2,4.7,6.1,2.8,6.1,2.2,1.4,7.9)  s<-sqrt(sum((v-mean(v))^2/(length(v)-1)))  print(s)

Output:

[1] 2.676004

### Method 2: Using sd()

The sd() function is used to return the standard deviation.

Syntax: sd(x, na.rm = FALSE)

Parameters:

• x: a numeric vector, matrix or data frame.
• na.rm: missing values be removed?

Return: The sample standard deviation of x.

Example 1:

## R

 v <- c(12,24,74,32,14,29,84,56,67,41)  s<-sd(v)  print(s)

Output:

[1] 25.53886

Example 2:

## R

 v <- c(71,48,98,65,45,27,39,61,50,24,17)  s1<-sqrt(sum((v-mean(v))^2/(length(v)-1)))print(s1)  s2<-sd(v)print(s2)

Output:

[1] 23.52175

Example 3:

## R

 v <- c(1.8,3.7,9.2,4.7,6.1,2.8,6.1,2.2,1.4,7.9)  s1<-sqrt(sum((v-mean(v))^2/(length(v)-1)))print(s1)  s2<-sd(v)print(s2)

Output:

[1] 2.676004

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