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The Jaccard similarity index measures the similarity between two sets of data. It can range from 0 to 1. The higher the number, the more similar the two sets of data.

The Jaccard similarity index is calculated as:

**Jaccard Similarity** = (number of observations in both sets) / (number in either set)

Or, written in notation form:

**J(A, B) =Â **|Aâˆ©B| / |AâˆªB|

This tutorial explains how to calculate Jaccard Similarity for two sets of data in R.

**Example: Jaccard Similarity in R**

Suppose we have the following two sets of data:

ab

We can define the following function to calculate the Jaccard Similarity between the two sets:

#define Jaccard Similarity function jaccard function(a, b) { intersection = length(intersect(a, b)) union = length(a) + length(b) - intersection return (intersection/union) } #find Jaccard Similarity between the two sets jaccard(a, b) 0.4

The Jaccard Similarity between the two lists isÂ **0.4**.

Note that the function will returnÂ **0Â **if the two sets donâ€™t share any values:

c

And the function will returnÂ **1Â **if the two sets are identical:

e

The function also works for sets that contain strings:

g cat', 'dog', 'hippo', 'monkey') h monkey', 'rhino', 'ostrich', 'salmon') jaccard(g, h) 0.142857

You can also use this function to find the **Jaccard distanceÂ **between two sets, which is theÂ *dissimilarity* between two sets and is calculated as 1 â€“ Jaccard Similarity.

a #find Jaccard distance between setsaandb1 - jaccard(a, b) [1] 0.6

*Refer to this Wikipedia page to learn more details about the Jaccard Similarity Index.*