Applying a Function to a Data Frame for Multiple Inputs and Creating Columns with Outputs Using dplyr

Introduction

The dplyr package in R is a powerful tool for data manipulation and analysis. One of its key features is the ability to apply functions to data frames, which can be useful for a variety of tasks such as data cleaning, filtering, and grouping. In this article, we will explore how to apply a function to a data frame for multiple inputs and create columns with the outputs using dplyr.

Background

The provided example demonstrates a simple function called custom_function() that takes a data frame, a distance value, and a column name as input. The function uses the pipe operator (%>%) to apply the rowSums function to each row of the data frame, where the rows are compared to the given distance value.

custom_function <- function(some_data_frame, distance, name) {
  some_data_frame %>% 
    mutate(!!name := rowSums(. &lt;= distance, na.rm = TRUE)) %>% 
  return()
}

The function creates a new column with the same name as specified by the name argument, containing the number of values in the data frame that are less than or equal to the given distance value.

Problem Statement

Given the following data:

data_in <- data.frame(X1 = c(1, 3, 5, 2, 6), 
                       X2 = c(2, 4, 5, 1, 8),
                       X3 = c(3, 2, 4, 1, 4))

We want to apply the custom_function() to the data frame for multiple inputs and create columns with the outputs.

Solution

There is an easy way to do this using the mapply function (using the same distances as in @Sotos’ answer):

dst <- c(5, 3, 1, 6, 7, 8)

(cnm <- paste('some_name', dst, sep = '_'))

data_in[, cnm] <- mapply(function(d) rowSums(data_in &lt;= d, na.rm = T), d = dst)

Alternatively, we can use the purrr::map2 function:

cbind(
  data_in,
  purrr::map2(dst, cnm, ~custom_function(data_in, .x, .y))
)

Custom Function

The custom_function() is defined as follows:

custom_function <- function(some_data_frame, distance, name) {
  some_data_frame %>% 
    transmute(!!name := rowSums(. &lt;= distance, na.rm = TRUE))
}

This function uses the pipe operator (%>%) to apply the transmute function to each row of the data frame, where the rows are compared to the given distance value.

Code Explanation

Let’s break down the code:

• We define a data frame data_in with three columns: X1, X2, and X3.
• We create an anonymous vector dst containing the distances for which we want to apply the function.
• We use the paste function to create column names by concatenating “some_name” with each distance value in dst.
• We use the mapply function to apply the custom_function() to each element of dst, and assign the results to a new vector cnm. This creates a new column for each distance value.
• We use the cbind function to combine the original data frame with the new columns created in the previous step.

Conclusion

Applying a function to a data frame for multiple inputs and creating columns with outputs using dplyr is a useful technique that can be applied to various tasks. In this article, we demonstrated how to use the mapply and purrr::map2 functions to achieve this, as well as how to define a custom function using the pipe operator (%>%). By following these techniques, you can efficiently manipulate and analyze your data using dplyr.

Last modified on 2023-08-21