Calculate the Median Absolute Deviation in R Programming – mad() Function

The Median Absolute Deviation is calculated in R Language using the mad() function. It is a statistical measurement of a dataset’s dispersion or variability. Due to its resistance to outliers and extreme values, the MAD is a reliable substitute for the standard deviation.

The Median Absolute Deviation (MAD) is calculated using the following formula:

MAD is equal to median(|xi – x)|.

where:

Each observation in the dataset is represented by xi.
The dataset’s median is represented as median(x).

Syntax: mad(x) Parameters: x: Vector

Calculate MAD for vectors :

We can calculate the Median Absolute Deviation for vectors.

Example 1:

R

# R Program to calculate
# Median Absolute Deviation

# Creating a vector
x <- c(1:9)

# Calling mad() Function
mad(x)

Output:

[1] 2.9652

Example 2:

R

# R Program to calculate
# Median Absolute Deviation

# Creating a vector
x <- c(1, 4, 2, 3, 7, 3, 8, 9, 2)

# Calling mad() Function
mad(x)

Output:

[1] 1.4826

Calculate MAD for a single column in a data:

We can calculate MAD for a single column in a data set so we can take the iris dataset.

R

#load the dataset
data(iris)

#calculate the mad for single columns.
mad(iris$Sepal.Width)

Output:

[1] 0.44478

Calculate MAD for multiple columns in a data:

With the help of apply function, we can calculate the Median absolute deviation for multiple columns.

R

# load library
library(dplyr)

# remove Species column from dataset
data=select(iris,-('Species'))

# calculate the mad for all columns
sapply(data,mad)

Output:

Sepal.Length  Sepal.Width Petal.Length  Petal.Width 
     1.03782      0.44478      1.85325      1.03782 

We can calculate the mad for multiple columns in a dataset with the help of sapply function. we remove the species column from the data because the mad function only works on numerical columns.