do.call to Build and Execute Data.table Commands

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In this article, we will explore how to use do.call to build and execute data.table commands in R. We’ll delve into the intricacies of data.table manipulation and provide a comprehensive guide on how to create complex commands using do.call.

Background: Data.table Manipulation

Data.tables are an extension to the base table data type in R, providing improved performance and functionality for large datasets. The set() function is used to add new columns or update existing ones by reference.

However, sometimes we need to perform more complex operations involving multiple steps, such as creating a script that builds and executes a data.table command. This is where do.call comes into play.

Introduction to do.call

The do.call function in R is used to call a function with a list of arguments passed to it. It’s a powerful tool for creating complex functions by embedding other functions within them.

In the context of data.table manipulation, we can use do.call to build and execute commands that involve multiple steps.

Building a Script with do.call

Let’s start by examining the example provided in the original question. The goal is to create a script that builds and executes a series of commands that add new columns to a data.table.

The original code uses the following structure:

a <- do.call("paste", c("test.1.results <- mutate(test.1.results, P.Better.", list(unlist(test.1.results[,Group]), " = pnorm(Delta, test.1.results['", unlist(test.1.results[,Group]), "]")[,Delta], SD.diff, lower.tail=TRUE)))) 

This code creates a new script that builds and executes the mutate() function with multiple arguments.

However, as we can see, this approach only compares one test cell results against itself. We need to extend this logic to compare each test cell against each other.

The Challenge: Comparing Each Test Cell Against Each Other

To achieve this, we’ll use a combination of do.call and data.table manipulation techniques.

Firstly, let’s define the input data:

# Define the input data
test.1.results <- data.table(Group = c("Control", "Cell1", "Cell2", "Cell3", "Cell4"),
                              Delta = c(0, 0.00200, 0.00196, 0.00210, 0.00160),
                              SD.diff = c(0, 0.001096139, 0.001095797, 0.001096992, 0.001092716))

Next, we’ll create a script that builds and executes the mutate() function with multiple arguments using do.call. We’ll iterate over each test cell and compare it against each other:

# Create a script that builds and executes the mutate() function
for (i in paste0("Cell", 1:4)) {
  # Define the column name or position
  j <- paste("P.Better.", i)
  
  # Build the argument list using do.call
  args <- list(
    test.1.results,          # data.table to update
    i == NULL,               # no row subset (NULL is default anyway)
    j,                       # column name or position
    value = pnorm(test.1.results$Delta, test.1.results[i][, Delta], test.1.results$SD.diff, lower.tail=TRUE)
  )
  
  # Execute the script using do.call
  paste0("test.1.results <- ", do.call("mutate", args))
}

This code creates a series of scripts that build and execute the mutate() function with multiple arguments. We’ll then use these scripts to update the input data.

Execution: Updating the Input Data

To execute the script, we can use the following command:

# Execute the script using dplyr
library(dplyr)
test.1.results <- dplyr::mutate(test.1.results, P.Better.Cell1 = pnorm(Delta, Delta, SD.diff, lower.tail=TRUE))

Note that we’re not directly executing the do.call command but rather using it as a starting point for building our script.

Conclusion: Using do.call to Build and Execute Data.table Commands

In this article, we explored how to use do.call to build and execute data.table commands in R. We discussed the importance of creating complex functions by embedding other functions within them.

By following these steps, you can create a custom script that builds and executes multiple data.table commands using do.call. This approach is particularly useful when working with large datasets or performing complex manipulations.

Remember to use this technique judiciously and in combination with other R programming techniques to achieve your desired results.

Last modified on 2023-06-17