R cut() function allows you to cut data into bins and specify ‘**cut labels’**, so it is beneficial to create a factor from a continuous variable.

**cut in R**

The cut() is a built-in R function that divides the range of x into intervals and codes the values in x according to which interval they fall. To convert Numeric to Factor in R, use the cut() function.

**Syntax**

```
cut(nv, breaks, labels = NULL,
include.lowest = FALSE, right = TRUE,
dig.lab = 3, ordered_result = FALSE, …)
```

**Arguments**

**nv: **It is a numeric input vector.

**breaks: **It is a Number or vector of breaks.

**labels = NULL**: They are Labels for each group.

**include.lowest = FALSE: **Whether to include the lowest ‘break’ or not.

**right = TRUE: **The right interval is closed (and the left open) or vice versa.

**dig.lab = 3: **Number of digits of the groups if labels = NULL.

**ordered_result = FALSE: **Whether to order the factor result or not.

**Example**

To generate a random distribution number in R, use the **rnorm() **function**. **The normal distribution is the collection of random data from independent sources is distributed normally.

```
data <- stats::rnorm(20)
c <- cut(data, breaks = -3:3)
c
```

**Output**

```
[1] (0,1] (-1,0] (-2,-1] (0,1] (0,1] (1,2] (-1,0] (2,3] (-1,0]
[10] (0,1] (-1,0] (0,1] (0,1] (-2,-1] (-1,0] (0,1] (-1,0] (1,2]
[19] (-1,0] (-1,0]
Levels: (-3,-2] (-2,-1] (-1,0] (0,1] (1,2] (2,3]
```

The **breaks** argument allows you to cut the data in bins and hence categorize it.

To check the data distribution in different ranges, use the summary() function.

```
data <- stats::rnorm(20)
c <- cut(data, breaks = -3:3)
summary(c)
```

**Output**

```
(-3,-2] (-2,-1] (-1,0] (0,1] (1,2] (2,3]
0 1 9 9 1 0
```

The numbers are divided into 6 levels. Some levels are empty.

You can set the “**breaks”** argument to any integer, creating as many intervals (levels) as the defined number. These intervals will be all of the same lengths.

`c <- cut(data, breaks = 2)`

**Output**

```
[1] (-1.39,0.534] (-1.39,0.534] (-1.39,0.534] (0.534,2.46] (-1.39,0.534]
[6] (-1.39,0.534] (0.534,2.46] (-1.39,0.534] (-1.39,0.534] (-1.39,0.534]
[11] (-1.39,0.534] (0.534,2.46] (-1.39,0.534] (0.534,2.46] (-1.39,0.534]
[16] (-1.39,0.534] (0.534,2.46] (-1.39,0.534] (0.534,2.46] (-1.39,0.534]
Levels: (-1.39,0.534] (0.534,2.46]
```

You can see that the number has been divided into two intervals. You can also specify the intervals you prefer.

```
data <- stats::rnorm(20)
c <- cut(data, breaks = c(-2, 2, 1))
c
```

**Output**

```
[1] (1,2] (-2,1] (-2,1] (-2,1] (-2,1] (1,2] (1,2] (-2,1] (-2,1] (1,2]
[11] (-2,1] (-2,1] <NA> (-2,1] (1,2] (-2,1] (-2,1] (-2,1] (-2,1] (-2,1]
Levels: (-2,1] (1,2]
```

It is worth mentioning that if the intervals have decimals, you can modify the number of decimals with the **dig.lab**.

```
data <- stats::rnorm(30)
c <- cut(data, breaks = 6, dig.lab=2)
c
```

**Output**

```
[1] (1,1.8] (-1.4,-0.59] (-0.59,0.22] (-0.59,0.22] (0.22,1]
[6] (-0.59,0.22] (1,1.8] (0.22,1] (-1.4,-0.59] (0.22,1]
[11] (-0.59,0.22] (0.22,1] (1,1.8] (-0.59,0.22] (-2.2,-1.4]
[16] (1,1.8] (1,1.8] (1.8,2.7] (-2.2,-1.4] (0.22,1]
[21] (-2.2,-1.4] (0.22,1] (-1.4,-0.59] (-0.59,0.22] (0.22,1]
[26] (-0.59,0.22] (-2.2,-1.4] (0.22,1] (1.8,2.7] (-0.59,0.22]
Levels: (-2.2,-1.4] (-1.4,-0.59] (-0.59,0.22] (0.22,1] (1,1.8] (1.8,2.7]
```

**Passing labels argument to the cut() function in R**

To change the levels of the output factor in the cut() method, use the labels argument.

```
info <- c(11, 21, 18, 19, 23, 46, 29, 37)
cut(info, breaks = c(0, 2, 10, 60, 40, 50),
labels = c("First", "Second", "Third", "Fourth", "Fifth"))
```

**Output**

```
[1] Third Third Third Third Third Fourth Third Third
Levels: First Second Third Fourth Fifth
```

That is it for this tutorial.

Krunal Lathiya is an Information Technology Engineer by education and web developer by profession. He has worked with many back-end platforms, including Node.js, PHP, and Python. In addition, Krunal has excellent knowledge of Data Science and Machine Learning, and he is an expert in R Language. Krunal has written many programming blogs, which showcases his vast expertise in this field.