The “parRapply” Function in R

• Package: parallel

• Purpose: Applies a function to each row of a matrix using a cluster for parallel processing.

• General class: Parallel computing

• Required argument(s):

• cl: A cluster object specifying the cluster to use for parallel processing.

• X: A matrix to be processed.

• fun: The function to apply to each element of X.

• Notable optional arguments:

• ...: Additional arguments passed to the function fun.

• Example:

• # Load the parallel library
  library(parallel)
  
  # Create a cluster object with 2 cores
  cl <- makeCluster(2)
  
  # Create a matrix to operate on
  Example_matrix <- matrix(1:25, ncol = 5, byrow = TRUE)
  
  # Apply the mean function to each row
  result <- parRapply(cl, Example_matrix, mean)
  
  # Close the cluster
  stopCluster(cl)
  
  # Print the result
  print(result)

• This example demonstrates how to use the parRapply function from the parallel package to apply a function (mean) to each row of a matrix (Example_matrix) in parallel using a cluster object (cl). The ... argument allows passing additional arguments to the function “mean”. The output is a vector containing the mean of elements in each row of the matrix.