Working with Multiple Columns and Functions in Dplyr’s Across

In this post, we’ll explore the across function from the dplyr package in R, which allows us to apply different functions to multiple columns within a dataset. We’ll delve into how to use across with multiple arguments, including grouping by species and applying different functions to different sets of columns.

Introduction to the across Function

The across function is part of the dplyr package in R and provides an efficient way to apply various functions to multiple columns within a dataset. This function takes several key components:

• The specification of columns or column names
• A function that will be applied to these columns
• Options for handling different types of data

By using across, you can simplify your code and make it more readable, especially when working with complex datasets.

Specifying Columns with Across

To start using across, you need to specify which columns in your dataset you want to apply the function to. There are several ways to do this:

• Using column names: You can pass a character vector of column names to across.
• Using column indices: If you have already selected or loaded your data, you can use column indices (1-based) for the columns you’re interested in.
• Using functions that return column indices: Some functions like starts_with or ends_with can be used to select columns based on prefixes or suffixes.

Example 1: Applying a Function to Multiple Columns with Across

Let’s start with a basic example where we want to calculate the mean of two columns, Sepal.Length and Petal.Length, across different species in our dataset:

library(dplyr)

# Sample iris dataset
iris %>% 
  group_by(Species) %>% 
  summarise(across(starts_with("Sepal"), mean), across(starts_with("Petal"), median))

In this code snippet, we use starts_with to specify that the function should be applied to columns whose names start with “Sepal” and “Petal”. The results are then grouped by species.

Example 2: Using Multiple Across Statements in Summarise

As demonstrated in the Stack Overflow question you provided, it’s possible to apply multiple across statements within a single summarise or mutate function:

# Sample iris dataset
iris %>% 
  group_by(Species) %>% 
  summarise(across(starts_with("Sepal"), mean), across(starts_with("Petal"), median))

This code applies the mean function to all columns whose names start with “Sepal” and the median function to all columns whose names start with “Petal”, both while grouping by species.

Handling Different Data Types

One of the most powerful features of the across function in dplyr is its ability to handle different data types:

• Numeric values: The default behavior for numeric values is to calculate their mean. If you want to use a different function, like median, you can specify it directly.
• Character values: For character values, the function returns the number of unique values in each column.

Handling Missing Values

When working with missing values (NA), dplyr’s across function provides some options:

• na.rm = TRUE: The default behavior is to ignore NA values when calculating means. However, for other functions like median or max/min, you might want to include NA values in the calculation.

Real-World Application of across

The versatility of across makes it a valuable tool in data analysis tasks beyond simple calculations:

• Feature Engineering: You can use across to extract new features from existing columns. For example, extracting the logarithm of the original values using log().
• Data Transformation: Applying multiple transformations across different columns can help clean or normalize your data.
• Data Analysis: When working with datasets where some variables are categorical and others are continuous, you might use across to apply functions differently based on these types.

Conclusion

In this article, we’ve explored the capabilities of R’s across function within the dplyr package. By understanding how to specify columns, apply functions, and handle different data types and missing values, you’ll be better equipped to tackle complex analysis tasks in your own work.

When faced with datasets that require multiple aggregations based on different conditions or variables, remember to use across in combination with other dplyr functions like group_by, summarise, or mutate.

Last modified on 2023-08-09