README.Rmd

---
output: github_document
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```



# functionals: Functional mapping with parallelism and progress bars

## Overview

`functionals` is a lightweight toolkit for functional programming in R with built-in support for parallelism and progress bars. It extends base R's functional tools with a consistent, minimal API for mapping, walking, reducing, cross-validating, and repeating computations across lists, data frames, and grouped data.

As of `0.5.1`, progress reporting is completion-driven across sequential,
multicore, and cluster-backed execution. When `pb = TRUE`, the bar advances as
individual tasks finish rather than at internal chunk boundaries. Rendering is
throttled for large workloads so the console keeps a single lightweight status
bar instead of redrawing on every task.


## Function Reference Table

| Function     | Main arguments                        | Output type | Description                                                     |
|--------------|----------------------------------------|-------------|-----------------------------------------------------------------|
| `fmap()`     | `.x`, `.f`, `ncores`, `pb`            | list        | Map `.f` over elements of `.x`                                  |
| `fmapn()`    | `.l`, `.f`, `ncores`, `pb`            | list        | Map `.f` over multiple aligned lists                            |
| `fmapr()`    | `.df`, `.f`, `ncores`, `pb`           | list        | Map `.f` over each row of a data frame (as named list)          |
| `fmapc()`    | `.df`, `.f`, `ncores`, `pb`           | list        | Map `.f(column, name)` over each column                         |
| `fmapg()`    | `.df`, `.f`, `by`, `ncores`, `pb`     | list        | Map `.f(group_df)` over groups defined by a column              |
| `floop()`    | `.x`, `.f`, `...`, `ncores`, `pb`     | list        | General-purpose functional loop with side-effects               |
| `fwalk()`    | `.x`, `.f`, `ncores`, `pb`            | NULL        | Map `.f` over `.x` for side-effects only (invisible return)     |
| `frepeat()`  | `times`, `expr`, `.x`, `ncores`, `pb` | list/vector | Repeat a call/expression multiple times                         |
| `fcv()`      | `.splits`, `.f`, `ncores`, `pb`       | list        | Map `.f` over resampling splits from `rsample::vfold_cv()`      |
| `freduce()`  | `.x`, `.f`, `...`                     | scalar/list | Reduce `.x` using a binary function `.f`                        |
| `fcompose()` | any number of functions `f1, f2, ...` | function    | Compose multiple functions: `f1(f2(...(x)))`                    |
| `fapply()`   | `.x`, `.f`, `ncores`, `pb`, `...`     | list        | Core internal utility for applying a function over `.x`         |



## Syntax Equivalence

| Task                     | `functionals` Example                                              | `purrr` Example                                    | Base R                                    |
|--------------------------|------------------------------------------------------------|----------------------------------------------------|--------------------------------------------|
| Map square               | `fmap(1:5, function(x) x^2)`                               | `map(1:5, function(x) x^2)`                        | `lapply(1:5, function(x) x^2)`             |
| Map over N arguments     | `fmapn(list(1:3, 4:6, 7:9), function(x, y, z) x + y + z)`  | `pmap(list(1:3, 4:6, 7:9), function(x, y, z) ...)` | `Map(function(x, y, z) ..., 1:3, 4:6, 7:9)` |
| Map over data frame rows | `fmapr(df, function(row) row$a + row$b)`                  | `pmap(df[c("a", "b")], function(x, y) x + y)`      | `apply(df, 1, function(row) ...)`         |
| Map over data frame cols | `fmapc(df, function(x, name) mean(x))`                    | `imap(df, function(x, name) mean(x))`              | `lapply(df, mean)`                        |
| Grouped map              | `fmapg(df, f, by = "group")`                              | `map(split(df, df$group), f)`                      | `lapply(split(df, df$group), f)`          |
| General-purpose loop     | `floop(1:3, function(x) cat(x))`                          | *(manual recursion)*                               | `for (x in 1:3) cat(x)`                   |
| Parallel + progress      | `fmap(x, f, ncores = 4, pb = TRUE)`                       | *(future_map(x, f))* with `progressr`              | `parLapply(cl, x, f)` or `mclapply()`     |
| Repeat simulation        | `frepeat(100, function() rnorm(1))`                       | *(manual loop)*                                    | `replicate(100, rnorm(1))`                |
| Walk with side effects   | `fwalk(letters, function(x) cat(x))`                      | `walk(letters, function(x) cat(x))`                | `lapply(letters, cat)`                    |
| Reduce                   | ``freduce(1:5, `+`)``                              | ``reduce(1:5, `+`)``                             | ``Reduce(`+`, 1:5)``                        |
| Compose functions        | `fcompose(sqrt, abs)(-4)`                                 | `compose(sqrt, abs)(-4)`                           | `(function(x) sqrt(abs(x)))(-4)`          |




## Why no formula interface like `~ .x + .y`?

While `functionals` draws inspiration from `purrr`, it intentionally avoids supporting the formula-based anonymous function syntax (e.g., `~ .x + 1`) for now.

This decision is based on:

- Keeping dependencies minimal (no reliance on `rlang`)
- Avoiding non-standard evaluation that can confuse new users
- Encouraging explicit, readable code using `function(x) { ... }` style

We may consider adding tidy evaluation support (e.g., with quosures or `rlang::as_function`) in a future release. However, the current philosophy favors clarity and simplicity.


## Installation

```{r, eval=FALSE}
# from CRAN
install.packages("functionals")

# from github
remotes::install_github("ielbadisy/functionals")
```


## Examples 

```{r}
library(functionals)
library(purrr)
library(furrr)
library(pbapply)
library(dplyr)
library(rsample)
library(bench)


plan(multisession)

# utility to compare results
compare_outputs <- function(label, x, y) {
  cat("\n", label, "->", if (identical(x, y)) "dentical\n" else if (isTRUE(all.equal(x, y))) "nearly equal\n" else "different\n")
}

# strip names and convert to plain numeric vector
as_vec <- function(x) as.numeric(unlist(x, use.names = FALSE))

```

### Element-wise map

```{r}
x1 <- fmap(1:5, function(x) x^2)
x2 <- lapply(1:5, function(x) x^2)
x3 <- map(1:5, ~ .x^2)
x4 <- future_map(1:5, ~ .x^2)
x5 <- pblapply(1:5, function(x) x^2)
compare_outputs("Element-wise: base", x1, x2)
compare_outputs("Element-wise: purrr", x1, x3)
compare_outputs("Element-wise: furrr", x1, x4)
compare_outputs("Element-wise: pbapply", x1, x5)
```


### Parallel progress semantics

```{r}
slow <- function(x) {
  Sys.sleep(c(0.12, 0.24, 0.36, 0.48)[[x]])
  x^2
}

fmap(1:4, slow, ncores = 2, pb = TRUE)
```

The progress bar advances on each completed task, shows elapsed time and ETA,
and throttles redraws for large workloads so intermediate counts stay readable
without repainting the console on every iteration.


## Multi-input map

```{r}
x1 <- fmapn(list(1:3, 4:6), function(x, y) x + y)
x2 <- Map(`+`, 1:3, 4:6)
x3 <- pmap(list(1:3, 4:6), ~ ..1 + ..2)
x4 <- future_pmap(list(1:3, 4:6), ~ ..1 + ..2)
compare_outputs("Multi-input: base", x1, x2)
compare_outputs("Multi-input: purrr", x1, x3)
compare_outputs("Multi-input: furrr", x1, x4)
```


### Row-wise map

```{r}
x1 <- fmapr(mtcars, function(row) row$mpg + row$cyl)
rowlist <- lapply(seq_len(nrow(mtcars)), function(i) as.list(mtcars[i, ]))
x2 <- lapply(rowlist, function(row) row$mpg + row$cyl)
x3 <- map(rowlist, function(row) row$mpg + row$cyl)
compare_outputs("Row-wise: base", as_vec(x1), as_vec(x2))
compare_outputs("Row-wise: purrr", as_vec(x1), as_vec(x3))
```


### Column-wise map

```{r}
x1 <- fmapc(mtcars, function(col, name) mean(col))
x2 <- sapply(mtcars, mean)
x3 <- imap(mtcars, ~ mean(.x))
x4 <- future_imap(mtcars, ~ mean(.x))
compare_outputs("Column-wise: base", x1, as.list(x2))
compare_outputs("Column-wise: purrr", x1, x3)
compare_outputs("Column-wise: furrr", x1, x4)
```


### Group-wise map

```{r}
x1 <- fmapg(iris, function(df) colMeans(df[1:4]), by = "Species")
x2 <- lapply(split(iris, iris$Species), function(df) colMeans(df[1:4]))
x3 <- map(split(iris, iris$Species), ~ colMeans(.x[1:4]))
x4 <- future_map(split(iris, iris$Species), ~ colMeans(.x[1:4]))
compare_outputs("Group-wise: base", x1, x2)
compare_outputs("Group-wise: purrr", x1, x3)
compare_outputs("Group-wise: furrr", x1, x4)
```


### Side-effect map

```{r}
cat("\nSide-effects:\n")
fwalk(1:3, print)
```


### General-purpose loop with return values

```{r}
x1 <- floop(1:5, function(x) x^2, .capture = TRUE)
x2 <- lapply(1:5, function(x) x^2)
x3 <- {
  out <- list()
  for (i in 1:5) out[[i]] <- i^2
  out
}
compare_outputs("floop() vs lapply()", x1, x2)
compare_outputs("floop() vs for()", x1, x3)
```


### General-purpose loop (side-effect only)

```{r}
cat("\nGeneral-purpose loop (side-effects):\n")
floop(1:3, function(x) cat("floop says:", x, "\n"), pb = TRUE, .capture = FALSE)
cat("for-loop equivalent:\n")
for (x in 1:3) cat("for says:", x, "\n")
```


### Cross-validation

```{r}
splits <- vfold_cv(iris, v = 3)$splits
fit_model <- function(split) mean(analysis(split)$Sepal.Length)
x1 <- fcv(splits, fit_model)
x2 <- lapply(splits, fit_model)
compare_outputs("CV map: base", x1, x2)
```


### Repeat simulation

```{r}
x1 <- frepeat(times = 10, expr = rnorm(1))
x2 <- as.list(replicate(10, rnorm(1)))
x3 <- as.list(pbreplicate(10, rnorm(1)))
cat("\nRepeat: Results not comparable (randomized output)\n")
```


### Reduce

```{r}
x1 <- freduce(1:5, `+`)
x2 <- Reduce(`+`, 1:5)
x3 <- reduce(1:5, `+`)
compare_outputs("Reduce: base", x1, x2)
compare_outputs("Reduce: purrr", x1, x3)
```


### Compose

```{r}
x1 <- fcompose(sqrt, abs)(-4)
x2 <- (function(x) sqrt(abs(x)))(-4)
x3 <- compose(sqrt, abs)(-4)
compare_outputs("Compose: base", x1, x2)
compare_outputs("Compose: purrr", x1, x3)
```