To remove column from dataframe in R, you can use four different ways:
The following methods show how you can do it with syntax.
Method 1: Use subset() with Negative Index
subset(df, select = -n)
Method 2: Use subset() with Column Name
subset(df, select = -column_name)
Method 3: Use Indexing Operator
df[, !names(df) %in% c('column_name')]
Method 4: Use select() Function
library(dplyr)
df %>% select(-column_name)
The following examples show how to remove column from dataframe in R.
Using subset() with Negative Index
Let’s see how we can use subset() function in R:
# Create dataframe
df <- data.frame(Start_date=as.Date(c("2000-05-21","1998-03-28","2001-01-19","2003-04-27","2004-11-26","2008-11-25")),
Machine_name = c("Machine1","Machine2","Machine3","Machine4","Machine5","Machine6"),
Value = c(108,120,135,95,98,105),Reading= c(110,97,91,89,80,85))
# Remove column from dataframe
df <- subset(df,select = -3)
# Print dataframe
print(df)
Output:
Start_date Machine_name Reading
1 2000-05-21 Machine1 110
2 1998-03-28 Machine2 97
3 2001-01-19 Machine3 91
4 2003-04-27 Machine4 89
5 2004-11-26 Machine5 80
6 2008-11-25 Machine6 85
Here the output shows dataframe from which Value column is removed using negative index.
Using subset() with Column Name
Let’s see how we can use subset() function to remove column:
# Create dataframe
df <- data.frame(Start_date=as.Date(c("2000-05-21","1998-03-28","2001-01-19","2003-04-27","2004-11-26","2008-11-25")),
Machine_name = c("Machine1","Machine2","Machine3","Machine4","Machine5","Machine6"),
Value = c(108,120,135,95,98,105),Reading= c(110,97,91,89,80,85))
# Remove column from dataframe
df <- subset(df,select = -Reading)
# Print dataframe
print(df)
Output:
Start_date Machine_name Value
1 2000-05-21 Machine1 108
2 1998-03-28 Machine2 120
3 2001-01-19 Machine3 135
4 2003-04-27 Machine4 95
5 2004-11-26 Machine5 98
6 2008-11-25 Machine6 105
Here the output shows dataframe from which Reading column is removed.
Using Indexing Operator
Let’s see how we can use indexing operator to remove column:
# Create dataframe
df <- data.frame(Start_date=as.Date(c("2000-05-21","1998-03-28","2001-01-19","2003-04-27","2004-11-26","2008-11-25")),
Machine_name = c("Machine1","Machine2","Machine3","Machine4","Machine5","Machine6"),
Value = c(108,120,135,95,98,105),Reading= c(110,97,91,89,80,85))
# Remove column from dataframe
new_df <- df[, !names(df) %in% c('Value')]
# Show new dataframe
print(new_df)
Output:
Start_date Machine_name Reading
1 2000-05-21 Machine1 110
2 1998-03-28 Machine2 97
3 2001-01-19 Machine3 91
4 2003-04-27 Machine4 89
5 2004-11-26 Machine5 80
6 2008-11-25 Machine6 85
Here in the output it shows new dataframe which created from dataframe df but we removed Value column.
Using select() Function
Let’s see how to use select() function in R:
library(dplyr)
# Create dataframe
df <- data.frame(Start_date=as.Date(c("2000-05-21","1998-03-28","2001-01-19","2003-04-27","2004-11-26","2008-11-25")),
Machine_name = c("Machine1","Machine2","Machine3","Machine4","Machine5","Machine6"),
Value = c(108,120,135,95,98,105),Reading= c(110,97,91,89,80,85))
# Remove column from dataframe
new_df <- df %>% select(-Reading)
# Print dataframe
print(df)
Output:
Start_date Machine_name Value
1 2000-05-21 Machine1 108
2 1998-03-28 Machine2 120
3 2001-01-19 Machine3 135
4 2003-04-27 Machine4 95
5 2004-11-26 Machine5 98
6 2008-11-25 Machine6 105
Here in the output it shows new dataframe which created from dataframe df but we removed Reading column.
