Functional Programming in R

If a task can be completed faster using parallel processing, why not do it?
Modern laptops and PCs come with multi-core processors and ample memory. Instead of waiting minutes or hours, you can split workloads across multiple cores and finish in seconds.
Learning parallel programming is a serious career advantage. Once you start using it, you’ll only wonder why you didn’t learn it earlier.

Functional Programming in R (Before We Go Parallel)
Before true parallelism, R provides fast vectorized and functional approaches that already outperform traditional loops.
lapply() – Always Returns a List
lapply(1:5, function(x) x^2)

Output:
[[1]] 1
[[2]] 4
[[3]] 9
[[4]] 16
[[5]] 25

You can return multiple values:
lapply(1:5, function(x) c(x^2, x^3))

sapply() – Tries to Simplify Output
sapply(1:5, function(x) x^2)

Vector output:
[1] 1 4 9 16 25

Matrix output:
sapply(1:5, function(x) c(x^2,x^3))

 [,1] [,2] [,3] [,4] [,5]

[1,] 1 4 9 16 25
[2,] 1 8 27 64 125

If you disable simplification:
sapply(1:5, function(x) x^2, simplify = FALSE, USE.NAMES = FALSE)

…it behaves like lapply().
These are fast, but they are not truly parallel.

True Parallelism in R Using the parallel Package
Step 1: Detect Cores
library(parallel)
no_cores <- detectCores()

Step 2: Create a Cluster and Run Code
clust <- makeCluster(no_cores)

parLapply(clust, 1:5, function(x) c(x^2, x^3))

stopCluster(clust)

Using parSapply() With Exported Variables
base <- 4
clusterExport(clust, "base")

parSapply(clust, 1:5, function(exponent) base^exponent)

⚠ Important:
Variables must be exported to worker nodes using:
clusterExport(clust, "variable_name")

Libraries must also be loaded on all workers:
clusterEvalQ(clust, library(randomForest))

Parallel Loops Using foreach and doParallel
Setup
library(foreach)
library(doParallel)

registerDoParallel(makeCluster(no_cores))

Vector Output
foreach(exponent = 1:5, .combine = c) %dopar% base^exponent

Matrix Output
foreach(exponent = 1:5, .combine = rbind) %dopar% base^exponent

List Output
foreach(exponent = 1:5, .combine = list, .multicombine = TRUE) %dopar% base^exponent

Data Frame Output
foreach(exponent = 1:5, .combine = data.frame) %dopar% base^exponent

Stop Cluster
stopImplicitCluster()

Exporting Variables and Packages in foreach
If variables are outside the loop:
foreach(exponent = 1:5, .combine = c, .export = "base") %dopar% base^exponent

Using packages inside workers:
foreach(i = 1:5, .combine = c, .packages = "dplyr") %dopar% {
iris[i, ] %>% select(-Species) %>% sum
}

Memory Optimization: FORK vs PSOCK
Default cluster type is PSOCK.
To save memory:
clust <- makeCluster(no_cores, type = "FORK")

FORK shares memory across processes, reducing overhead.

Debugging Parallel Code
Debug File Output
registerDoParallel(makeCluster(no_cores, outfile = "debug_file.txt"))

foreach(x = list(1,2,3,4,5,"a")) %dopar%
cat(dput(x), file = paste0("debug_file_", x, ".txt"))

Error Handling With tryCatch()
foreach(x = list(1,2,"a")) %dopar% {
tryCatch({
1/x
}, error = function(e) {
paste0("Error for '", x, "': ", e)
})
}

Memory Cleanup
Remove objects:
rm(base)
rm(list = ls())

Force garbage collection:
gc()

Summary
Parallel programming in R allows you to:
✅ Fully use multi-core CPUs
✅ Reduce execution time drastically
✅ Scale ML and ETL workflows
✅ Improve production-grade performance
Key tools:
PurposeTool
Functional programming
lapply(), sapply()
Core-level parallelism
parallel
Loop-based parallelism
foreach + doParallel
Not all code needs to be parallel. Focus on bottlenecks first, then parallelize wisely.

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