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# How to Calculate Jaccard Similarity in R

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:

```a
b ```

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 sets a and b
1 - jaccard(a, b)

[1] 0.6```