You can use one of the following three methods to convert a data frame column to a vector in R:

#use $ operator
new_vector <- df$column_name

#use indexing
new_vector <- df[['column_name']]

#use 'pull' from dplyr package
new_vector <- dplyr::pull(df, column_name)

Each of these methods returns identical results.

The following examples show how to use each of these methods in practice with the following data frame:

#create data frame
df <- data.frame(a=c(1, 2, 5, 6, 12, 14),
                 b=c(8, 8, 9, 14, 22, 19),
                 c=c(3, 3, 2, 1, 2, 10))

#display data frame
df

   a  b  c
1  1  8  3
2  2  8  3
3  5  9  2
4  6 14  1
5 12 22  2
6 14 19 10

Example 1: Use $ Operator

The following code shows how to use the $ operator to convert a data frame column to a vector:

#convert column 'a' to vector
new_vector <- df$a

#view vector
new_vector

[1]  1  2  5  6 12 14

#view class of vector
class(new_vector)[1] "numeric"

Example 2: Use Indexing

The following code shows how to use indexing to convert a data frame column to a vector:

#convert column 'a' to vector
new_vector <- df[['a']]

#view vector
new_vector

[1]  1  2  5  6 12 14

#view class of vector
class(new_vector)[1] "numeric"

Example 3: Use ‘pull’ from dplyr

The following code shows how to use the ‘pull’ function from the package to convert a data frame column to a vector:

library(dplyr)

#convert column 'a' to vector
new_vector <- pull(df, a)

#view vector
new_vector

[1]  1  2  5  6 12 14

#view class of vector
class(new_vector)[1] "numeric"

Notice that all three methods return identical results.

Note: If you happen to be working with an extremely large dataset, the ‘pull’ function from the dplyr package will perform the fastest out of the three functions shared in this tutorial.