recursive_iteration.R

library(tidyverse)

set.seed(23131)
original_matrix <- replicate(10, sample(1:10, 10, replace = TRUE))
my_params <- tibble(x = sample(1:10, 5, replace = TRUE),
                    y = sample(2:3, 5, replace = TRUE))

                                        # Define the matrix that will be updated in each iteration
my_matrix <- original_matrix
my_function2 <- function(param1, param2) {
                                        # In each iteration, multiply by your params
                                        # and update `my_matrix` in the global environment

                                        # This ensures that in every interation you're getting
                                        # the up to date `my_matrix`.

  my_matrix <<- (my_matrix + param1) * param2

  my_matrix
}

final_res <- map2(my_params$x,
                  my_params$y,
                  my_function2)

final_res[[nrow(my_params)]]

iter_fun <- function(param1, param2, matrix) {
  i <- 1
  max <- length(param1)
  orig_m <- (matrix + param1[i]) * param2[i]
  test_fun <- function() {

    while (i < max) {
      orig_m <<- (orig_m + param1[i + 1]) * param2[i + 1]
      i <<- i + 1

    }
    
    orig_m
  }
  
  test_fun()
}

res <- microbenchmark::microbenchmark(
                         purrr = map2(my_params$x,
                                      my_params$y,
                                      my_function2),
                         base = iter_fun(my_params$x,
                                         my_params$y,
                                         original_matrix)
                       )