# Using the Cut2 Function in R Programming

## Another function, cut2(), in the Hmsic library will help group the observations of your dataset. Here’s how.

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I explained in an earlier post how the *cut()* function in R programming worked. Yet more than one version of that function is available. That second one, *cut2()*, is included in the *Hmisc* library. It provides more nuanced features in identifying bins among a vector of data. This post will explain some of the details so you can appreciate the options and be imaginative in the

The *cut2()* function has vectors, cut points, the minimum number of observations in a group, a number of quartile groups, and some additional parameters.

One difference is the cutpoints for each bin. The “cutpoints” describe the start and end points of the bin groups.

In *cut()* you have a starting point, lower, and end point, upper. They appear as (lower, upper] which indicates the cutpoints for a bin range that excludes the lower number.

In *cut2(),* the bins have inclusive lower endpoints and excludes the upper endpoint. The bins in the data range appears as [lower, upper), with the exception that the last interval is completely inclusive — [lower, upper]. This means *cut2()* will by default ensure that given cutpoints consider the entire data range.

If cutpoints are not given, the *cut2()* function will cut the data into quantile groups (g) or groups with a given minimum number of observations (m).

For example, I am recreating the cuts example I made in the post **How to Use The Cut Function In R Programming****. **In that example, I made city gas mileage bins of the vehicles in the dataset.

Below is the same example, but created with the *cut2()* function.

The *cut2()* function yields a set of bins similar to the cut example, with the *cuts* parameter set to a vector. Yet, the [lower, upper)…