# How to Perform Data Binning in R (With Examples)

You can use one of the following two methods to perform data binning in R:

Method 1: Use cut() Function

```library(dplyr)

#perform binning with custom breaks
df %>% mutate(new_bin = cut(variable_name, breaks=c(0, 10, 20, 30)))

#perform binning with specific number of bins
df %>% mutate(new_bin = cut(variable_name, breaks=3))
```

Method 2: Use ntile() Function

```library(dplyr)

#perform binning with specific number of bins
df %>% mutate(new_bin = ntile(variable_name, n=3))
```

The following examples show how to use each method in practice with the following data frame:

```#create data frame
df <- data.frame(points=c(4, 4, 7, 8, 12, 13, 15, 18, 22, 23, 23, 25),
assists=c(2, 5, 4, 7, 7, 8, 5, 4, 5, 11, 13, 8),
rebounds=c(7, 7, 4, 6, 3, 8, 9, 9, 12, 11, 8, 9))

points assists rebounds
1      4       2        7
2      4       5        7
3      7       4        4
4      8       7        6
5     12       7        3
6     13       8        8```

### Example 1: Perform Data Binning with cut() Function

The following code shows how to perform data binning on the points variable using the cut() function with specific break marks:

```library(dplyr)

#perform data binning on points variable
df %>% mutate(points_bin = cut(points, breaks=c(0, 10, 20, 30)))

points assists rebounds points_bin
1       4       2        7     (0,10]
2       4       5        7     (0,10]
3       7       4        4     (0,10]
4       8       7        6     (0,10]
5      12       7        3    (10,20]
6      13       8        8    (10,20]
7      15       5        9    (10,20]
8      18       4        9    (10,20]
9      22       5       12    (20,30]
10     23      11       11    (20,30]
11     23      13        8    (20,30]
12     25       8        9    (20,30]
```

Notice that each row of the data frame has been placed in one of three bins based on the value in the points column.

We could also specify the number of breaks to use to create bins of equal width that range from the minimum value to the maximum value of the points column:

```library(dplyr)

#perform data binning on points variable
df %>% mutate(points_bin = cut(points, breaks=3))

points assists rebounds points_bin
1       4       2        7  (3.98,11]
2       4       5        7  (3.98,11]
3       7       4        4  (3.98,11]
4       8       7        6  (3.98,11]
5      12       7        3    (11,18]
6      13       8        8    (11,18]
7      15       5        9    (11,18]
8      18       4        9    (11,18]
9      22       5       12    (18,25]
10     23      11       11    (18,25]
11     23      13        8    (18,25]
12     25       8        9    (18,25]
```

### Example 2: Perform Data Binning with ntile() Function

The following code shows how to perform data binning on the points variable using the ntile() function with a specific number of resulting bins:

```library(dplyr)

#perform data binning on points variable
df %>% mutate(points_bin = ntile(points, n=3))

points assists rebounds points_bin
1       4       2        7          1
2       4       5        7          1
3       7       4        4          1
4       8       7        6          1
5      12       7        3          2
6      13       8        8          2
7      15       5        9          2
8      18       4        9          2
9      22       5       12          3
10     23      11       11          3
11     23      13        8          3
12     25       8        9          3
```

Notice that each row has been assigned a bin from 1 to 3 based on the value of the points column.

It’s best to use the ntile() function when you’d like an integer value to be displayed in each row as opposed to an interval showing the range of the bin.