There are three common ways to drop columns from a data frame in R by name:

Method 1: Use Base R

#drop col2 and col4 from data frame
df_new <- subset(df, select = -c(col2, col4))

Method 2: Use dplyr

library(dplyr)

#drop col2 and col4 from data frame
df_new <- df %>% select(-c(col2, col4))

Method 3: Use data.table

library(data.table)

#convert data frame to data table
dt <- setDT(df)

#drop col2 and col4 from data frame
dt[, c('col2', 'col4'):=NULL]

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

#create data frame
df <- data.frame(team=c('A', 'A', 'B', 'B', 'C', 'C', 'C', 'D'),
                 points=c(12, 15, 22, 29, 35, 24, 11, 24),
                 rebounds=c(10, 4, 4, 15, 14, 9, 12, 8),
                 assists=c(7, 7, 5, 8, 19, 14, 11, 10))

#view data frame
df

  team points rebounds assists
1    A     12       10       7
2    A     15        4       7
3    B     22        4       5
4    B     29       15       8
5    C     35       14      19
6    C     24        9      14
7    C     11       12      11
8    D     24        8      10

Example 1: Drop Columns by Name Using Base R

The following code shows how to drop the points and assists columns from the data frame by using the subset() function in base R:

#create new data frame by dropping points and assists columns
df_new <- subset(df, select = -c(points, assists))

#view new data frame
df_new

  team rebounds
1    A       10
2    A        4
3    B        4
4    B       15
5    C       14
6    C        9
7    C       12
8    D        8

Notice that the points and assists columns have both been dropped from the new data frame.

Example 2: Drop Columns by Name Using dplyr

The following code shows how to drop the points and assists columns from the data frame by using the select() function in the dplyr package:

library(dplyr)

#create new data frame by dropping points and assists columns
df_new <- df %>% select(-c(points, assists))

#view new data frame
df_new

  team rebounds
1    A       10
2    A        4
3    B        4
4    B       15
5    C       14
6    C        9
7    C       12
8    D        8

Notice that the points and assists columns have both been dropped from the new data frame.

Example 3: Drop Columns by Name Using data.table

The following code shows how to drop the points and assists columns from the data frame by setting both columns equal to NULL using the data.table package:

library(data.table)

#convert data frame to data table
dt <- setDT(df)

#drop points and assists columns
dt[, c('points', 'assists'):=NULL]

#view updated data table
dt

   team rebounds
1:    A       10
2:    A        4
3:    B        4
4:    B       15
5:    C       14
6:    C        9
7:    C       12
8:    D        8

Notice that the points and assists columns have both been dropped from the new data table.

Note: All three methods produce the same result, but the dplyr and data.table methods will tend to be faster when working with extremely large datasets.