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