What is the difference between mutate and mutate_at?

What is the difference between mutate and mutate_at?

In the world of data manipulation in R, two commonly used functions are mutate and mutate_at. While both functions are used to create new variables in a dataset, they have distinct differences in their application and functionality.

What is mutate?

mutate is a function in the dplyr package that allows you to create new variables by applying a transformation to existing variables. This function is particularly useful when you want to add new variables to a dataset while retaining the original variables. The basic syntax of mutate is as follows:

df %>% mutate(new_variable = existing_variable * 2)

This will create a new variable new_variable that is twice the value of existing_variable in the dataset df.

What is mutate_at?

mutate_at is another function in the dplyr package that allows you to create new variables by applying a transformation to a subset of variables in a dataset. Unlike mutate, which applies the transformation to a specific variable, mutate_at applies the transformation to a group of variables that match certain criteria. The basic syntax of mutate_at is as follows:

df %>% mutate_at(vars(starts_with("x")), funs(sqrt(.)))

This will create new variables by applying the sqrt function to all variables in the dataset df that start with the prefix "x".

Key differences between mutate and mutate_at

Here are the key differences between mutate and mutate_at:

In summary, mutate is used to create new variables by applying a transformation to existing variables, while mutate_at is used to create new variables by applying a transformation to a subset of variables that match certain criteria.

When to use each function

Here are some scenarios where you might use each function:

Use mutate when:

• You want to create a new variable by applying a transformation to a specific existing variable.
• You want to retain the original variables in the dataset.
• You have a small number of variables to transform.

Use mutate_at when:

• You want to create new variables by applying a transformation to a group of variables that match certain criteria.
• You have a large number of variables to transform and want to use a more efficient approach.
• You want to apply the same transformation to multiple variables in a dataset.

In conclusion, mutate and mutate_at are both useful functions in the dplyr package, but they have distinct differences in their application and functionality. By understanding the key differences between these functions, you can use them effectively to create new variables in your dataset.