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How to Remove a Column using Dplyr package in R

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In this article, we are going to remove a column(s) in the R programming language using dplyr library.

Dataset in use:

Remove column using column name

Here we will use select() method to select and remove column by its name.

Syntax:

select(dataframe,-column_name)

Here, dataframe is the input dataframe and column_name is the column in the dataframe to be removed

To remove multiple columns:

Syntax:

select(dataframe,-c(column1,column2,.,column n))

Here, dataframe is the input dataframe and columns are the columns in the dataframe to be removed.

Example: R program to remove column by its name

R




# load the library
library(dplyr)
 
# create dataframe with 3 columns
# id,name and address
data1=data.frame(id=c(1,2,3,4,5,6,7,1,4,2),
                  
                 name=c('sravan','ojaswi','bobby',
                        'gnanesh','rohith','pinkey',
                        'dhanush','sravan','gnanesh',
                        'ojaswi'),
                  
                 address=c('hyd','hyd','ponnur','tenali',
                           'vijayawada','vijayawada','guntur',
                           'hyd','tenali','hyd'))
 
# remove id and name
print(select(data1,-c(name,id)))


Output:

Remove columns by position

Here we will remove column(s) using column index or position. We will use the select() method to select a column by removing its position

Note: Index starts with 1

Syntax:

select(dataframe,-index)

Here, dataframe is the input dataframe and index is the column position in the dataframe to be removed.

To remove multiple columns:

Syntax:

select(dataframe,-c(index1,index2,.,index n))

Here, dataframe is the input dataframe and indexes are the column positions in the dataframe to be removed.

Example: R program to remove column by index

R




# load the library
library(dplyr)
 
# create dataframe with 3 columns
# id,name and address
data1=data.frame(id=c(1,2,3,4,5,6,7,1,4,2),
                  
                 name=c('sravan','ojaswi','bobby',
                        'gnanesh','rohith','pinkey',
                        'dhanush','sravan','gnanesh',
                        'ojaswi'),
                  
                 address=c('hyd','hyd','ponnur','tenali',
                           'vijayawada','vijayawada','guntur',
                           'hyd','tenali','hyd'))
 
# remove id by index
print(select(data1,-1))
 
# remove multiple columns (name,id) by index
print(select(data1,-c(1,2)))


Output:

Remove column which contains a value or matches a pattern

Let’s discuss how to remove the column that contains the character or string.

Method 1: Using contains()

contains() removes the column that contains the given substring.

Syntax:

select(dataframe,-contains(‘sub_string’))

Here, dataframe is the input dataframe and sub_string is the string present in the column name will be removed.

Method 2: Using matches()

matches() removes the column that contains the given substring.

Syntax:

select(dataframe,-matches(‘sub_string’))

Here, dataframe is the input dataframe and sub_string is the string present in the column name will be removed.

Example: R program that removes column using contains() method

R




# load the library
library(dplyr)
 
# create dataframe with 3 columns
# id,name and address
data1=data.frame(id=c(1,2,3,4,5,6,7,1,4,2),
                  
                 name=c('sravan','ojaswi','bobby',
                        'gnanesh','rohith','pinkey',
                        'dhanush','sravan','gnanesh',
                        'ojaswi'),
                  
                 address=c('hyd','hyd','ponnur','tenali',
                           'vijayawada','vijayawada','guntur',
                           'hyd','tenali','hyd'))
 
# remove column that contains na
print(select(data1,-contains('na')))
     
# remove column that contains re
print(select(data1,-contains('re')))


Output:

Remove column which starts with or ends with certain character

Here we can also select columns based on starting and ending characters.

  • starts_with() is used to return the column that starts with the given character.

Syntax:

select(dataframe,-starts_with(‘substring’))

Where, dataframe is the input dataframe and substring is the character/string that starts with it.

  •  ends_with() is used to return the column that ends with the given character.

Syntax:

select(dataframe,-ends_with(‘substring’))

Where, dataframe is the input dataframe and substring is the character/string that ends with it.

Example: R program to remove a column that starts with a character/substring

R




# load the library
library(dplyr)
 
# create dataframe with 3 columns
# id,name and address
data1=data.frame(id=c(1,2,3,4,5,6,7,1,4,2),
                  
                 name=c('sravan','ojaswi','bobby',
                        'gnanesh','rohith','pinkey',
                        'dhanush','sravan','gnanesh',
                        'ojaswi'),
                  
                 address=c('hyd','hyd','ponnur','tenali',
                           'vijayawada','vijayawada','guntur',
                           'hyd','tenali','hyd'))
 
# remove column that starts with na
print(select(data1,-starts_with('na')))
     
# remove column that starts with ad
print(select(data1,-starts_with('ad')))


Output:

Example: R program to remove a column that ends with character/substring

R




# load the library
library(dplyr)
 
# create dataframe with 3 columns
# id,name and address
data1=data.frame(id=c(1,2,3,4,5,6,7,1,4,2),
                  
                 name=c('sravan','ojaswi','bobby',
                        'gnanesh','rohith','pinkey',
                        'dhanush','sravan','gnanesh',
                        'ojaswi'),
                  
                 address=c('hyd','hyd','ponnur','tenali',
                           'vijayawada','vijayawada','guntur',
                           'hyd','tenali','hyd'))
 
# remove column that ends with d
print(select(data1,-ends_with('d')))
     
# remove column that starts with ss
print(select(data1,-ends_with('ss')))


Output:



Last Updated : 07 Aug, 2023
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