How to Delete Data Frame Variables by Column Name in R (2 Examples)
On this page you’ll learn how to delete data frame variables by their name in the R programming language.
Creation of Example Data
data(iris) # Load iris head(iris) # Head of iris # Sepal.Length Sepal.Width Petal.Length Petal.Width Species # 1 5.1 3.5 1.4 0.2 setosa # 2 4.9 3.0 1.4 0.2 setosa # 3 4.7 3.2 1.3 0.2 setosa # 4 4.6 3.1 1.5 0.2 setosa # 5 5.0 3.6 1.4 0.2 setosa # 6 5.4 3.9 1.7 0.4 setosa |
data(iris) # Load iris head(iris) # Head of iris # Sepal.Length Sepal.Width Petal.Length Petal.Width Species # 1 5.1 3.5 1.4 0.2 setosa # 2 4.9 3.0 1.4 0.2 setosa # 3 4.7 3.2 1.3 0.2 setosa # 4 4.6 3.1 1.5 0.2 setosa # 5 5.0 3.6 1.4 0.2 setosa # 6 5.4 3.9 1.7 0.4 setosa
Example 1: Delete Columns with %in%-operator
iris_new1 <- iris[ , ! names(iris) %in% # Using %in%-operator c("Sepal.Length", "Petal.Length")] head(iris_new1) # Return head of new data # Sepal.Width Petal.Width Species # 1 3.5 0.2 setosa # 2 3.0 0.2 setosa # 3 3.2 0.2 setosa # 4 3.1 0.2 setosa # 5 3.6 0.2 setosa # 6 3.9 0.4 setosa |
iris_new1 <- iris[ , ! names(iris) %in% # Using %in%-operator c("Sepal.Length", "Petal.Length")] head(iris_new1) # Return head of new data # Sepal.Width Petal.Width Species # 1 3.5 0.2 setosa # 2 3.0 0.2 setosa # 3 3.2 0.2 setosa # 4 3.1 0.2 setosa # 5 3.6 0.2 setosa # 6 3.9 0.4 setosa
Example 2: Delete Columns with subset() Function
iris_new2 <- subset(iris, # Using subset function select = - c(Sepal.Length, Petal.Length)) head(iris_new2) # Return head of new data # Sepal.Width Petal.Width Species # 1 3.5 0.2 setosa # 2 3.0 0.2 setosa # 3 3.2 0.2 setosa # 4 3.1 0.2 setosa # 5 3.6 0.2 setosa # 6 3.9 0.4 setosa |
iris_new2 <- subset(iris, # Using subset function select = - c(Sepal.Length, Petal.Length)) head(iris_new2) # Return head of new data # Sepal.Width Petal.Width Species # 1 3.5 0.2 setosa # 2 3.0 0.2 setosa # 3 3.2 0.2 setosa # 4 3.1 0.2 setosa # 5 3.6 0.2 setosa # 6 3.9 0.4 setosa
Related Articles & Further Resources
Here, you may find some additional resources on topics such as variables, dplyr, coding errors, and naming data.