How to Calculate Minkowski Distance in R

The Minkowski distance is a metric in a normed vector space that can be considered as a generalization of both the Euclidean distance and the Manhattan distance. Named after Hermann Minkowski, this distance metric is widely used in fields like data mining, machine learning, and clustering. The versatility of the Minkowski distance lies in the fact that it can become the Euclidean or Manhattan distance based on the parameter provided, offering a wide range of utility for different contexts.

This guide will illustrate how to calculate the Minkowski distance in R.

Understanding Minkowski Distance

The Minkowski distance between two variables X and Y is defined as:

((sum(|x - y|^p))^(1/p))

where:

• x and y are two vectors of length n.
• p is the power parameter:
  • p = 1, the distance is known as Manhattan distance.
  • p = 2, the distance is known as Euclidean distance.

Calculating Minkowski Distance Using Base R

You can calculate the Minkowski distance in base R using the dist function with method = "minkowski" and specifying the p parameter. Note that each row is considered as a vector.

For instance, consider two vectors A and B, where A = (1, 2) and B = (3, 6). The following is how you can calculate the Minkowski distance:

# Define the vectors
A <- c(1, 2)
B <- c(3, 6)

# Combine vectors into a matrix
data <- rbind(A, B)

# Calculate Minkowski distance
minkowski_distance <- dist(data, method = "minkowski", p = 3)
print(minkowski_distance)

In the code above, the dist function is used to calculate the Minkowski distance. The parameter p is set to 3 for this example.

Using the proxy Package

The ‘proxy’ package in R provides a flexible framework for the computation of distances and includes a wide variety of distance measures. To compute the Minkowski distance, you can use the dist function with method = "Minkowski" and specifying the p parameter.

First, install and load the ‘proxy’ package, then calculate the Minkowski distance:

# Install the proxy package
if (!"proxy" %in% rownames(installed.packages())) {install.packages("proxy")}

# Load the proxy package
library(proxy)

# Define the vectors
A <- c(1, 2)
B <- c(3, 6)

# Calculate Minkowski distance
minkowski_distance <- proxy::dist(A, B, method = "Minkowski", p = 3)
print(minkowski_distance)

In this example, the dist function from the proxy package calculates the Minkowski distance between vectors A and B.

Conclusion

The Minkowski distance is a fundamental distance metric in data science, machine learning, and statistical analysis, providing a general measurement that can morph into other forms of distance measures depending on the context. Its ability to become either Euclidean or Manhattan distance makes it a highly adaptable tool for various tasks.