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Data binning is a method of transforming continuous data into discrete bins, or categories. This can be useful for visualizing the data or creating a model. In R, this can be done using the cut or cut2 functions. These functions allow you to specify the number of bins, the bin width, and the bin labels. Examples of data binning in R are provided to help illustrate how to use these functions.
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))
#view head of data frame
head(df)
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.
The following tutorials explain how to perform other common tasks in R: