Understanding the mutate(across...) Function in R
The Problem at Hand
Within R, when using the mutate(across...) function from the dplyr package, we often need to perform various transformations on existing columns in a data frame. One common requirement is to modify column names after applying these transformations. In this blog post, we’ll explore how to specify new column names that reflect changes made by mutate(across...).
The Example Scenario
Consider a scenario where we have a data frame d with three columns: alpha_rate, beta_rate, and gamma_rate. We want to apply the function my_function(x) = x * 8 across all these columns using mutate(across...). However, instead of retaining the original column names (alpha_rate_new, beta_rate_new), we’d like to have a new naming convention where the suffix _new is added to each transformed value.
The Original Approach
Before exploring alternative approaches, let’s examine how we might achieve this in the original way:
d %>% mutate(across(all_of(columns_i_want), my_function, .names = "{col}_new"))
This code transforms the values using my_function and assigns new column names with _new appended to each original name. However, we’re interested in modifying the column names themselves, not just appending a suffix.
A New Approach: Using .names Function
To modify the column names dynamically within the .names function, we can utilize R’s sub() function from string manipulation packages like stringr. Specifically, we’ll use it to replace any occurrences of ‘rate’ with ‘_new’.
Here’s how we might approach this:
library(dplyr)
library(stringr)
columns_i_want <- c("alpha_rate", "beta_rate")
d %>% mutate(across(all_of(columns_i_want), my_function, .names = "{sub('rate', 'new', col)}"))
In the code above, .names is used to specify a function that generates new column names. This function sub('rate', 'new', col) performs string replacement: it takes each original column name and replaces any occurrences of ‘rate’ with ‘_new’. This approach allows us to dynamically modify column names as we see fit.
Understanding .names Function
The .names function plays a crucial role in defining how new column names are generated. Here’s a breakdown:
.names: This is the syntax for specifying a function that generates new column names.all_of(columns_i_want): This expression specifies which columns to apply the transformation across. In our case, we’re working with two columns:alpha_rateandbeta_rate.my_function(x = ...): This is the actual R code applied to each value within the specified column(s)..names: Within this function, we can specify any additional logic for generating new column names.
Best Practices for Using .names
When working with .names, keep in mind these best practices:
- Be mindful of data types: If your original columns contain mixed data types (e.g., numeric and character), ensure that the resulting column names reflect this mix.
- Use consistent naming conventions: Maintain consistency across all transformed column names to maintain readability and avoid confusion.
Additional Considerations
While we’ve covered how to modify column names using .names, let’s explore a few additional considerations:
- Handling multiple transformations: When working with multiple columns or applying more complex transformations, it might be useful to group similar operations within the
.namesfunction. For instance, you could create a new name pattern for each transformation and apply these patterns individually. - Data consistency: Ensure that your transformed column names align with other parts of your data frame, such as labels or titles.
Conclusion
In this blog post, we delved into how to specify newly mutated names on mutate(across...) in R. By using the .names function and understanding string manipulation functions like sub(), we can dynamically modify column names to better reflect our data’s transformations. Remember to consider best practices for consistent naming conventions across your transformed columns.
References
Last modified on 2025-05-02