Recursive functions are the natural way to express iterations in a functional programming langauge, but in R, they can be significantly slower than loop-versions and for moderately long sequences or moderately deep trees, recursive functions will reach a limit imposted on them by the stack limit.

There are known solutions to these problems, as long as functions are written to be tail-recursive, meaning that the return value of a function is either a base value or another recursive call, but where we do not call recursively to then do something with the result.

The goal of `tailr`

is to automatically transform tail-recursive functions into loops or trampolines.

You can install the released version of `tailr`

from CRAN using

You can install tailr from GitHub with:

```
# install.packages("devtools")
devtools::install_github("mailund/tailr")
```

We can take a classical recursive function and write it in a tail-recursive form using an accumulator:

We can then, automatically, translate that into a looping version:

```
tr_factorial <- tailr::loop_transform(factorial, byte_compile = FALSE)
tr_factorial
#> function(n, acc = 1) {
#> if (n <= 1) acc
#> else factorial(n - 1, acc * n)
#> }
tr_factorial(100)
#> [1] 9.332622e+157
```

I have disabled byte compilation to make running time comparisons fair; by default it is enabled. For a function as simple as `factorial`

, though, byte compiling will not affect the running time in any substantial amount.

We can compare the running time with the recursive function and a version that is written using a loop:

```
loop_factorial <- function(n) {
val <- 1
while (n > 1) {
val <- n * val
n <- n - 1
}
val
}
n <- 1000
bm <- microbenchmark::microbenchmark(factorial(n),
loop_factorial(n),
tr_factorial(n))
bm
#> Unit: microseconds
#> expr min lq mean median uq max
#> factorial(n) 729.025 847.2085 1117.21646 996.0535 1203.120 6873.311
#> loop_factorial(n) 56.282 58.6220 89.87922 60.2395 63.538 2662.805
#> tr_factorial(n) 722.090 771.2465 928.12864 827.4775 1021.528 3153.916
#> neval
#> 100
#> 100
#> 100
boxplot(bm)
```

There is *some* overhead in using the automatically translated version over the hand-written, naturally, and for a simple function such as `factorial`

, it is not hard to write the loop-variant instead of the recursive function.

However, consider a more complicated example. Using the `pmatch`

package, we can create a linked list data structure as this:

A natural way to process linked lists using pattern matching is to write recursive functions that matches different patterns of their input. A function for computing the length of a linked list can look like this:

```
llength <- function(llist, acc = 0) {
cases(llist,
NIL -> acc,
CONS(car, cdr) -> llength(cdr, acc + 1))
}
```

It is reasonably simple to understand this function, whereas a looping version is somewhat more complicated. An initial attempt could look like this:

```
loop_llength <- function(llist) {
acc <- 0
repeat {
cases(llist,
NIL -> return(acc),
CONS(car, cdr) -> {
acc <- acc + 1
llist <- cdr
})
}
}
```

This version will not function, however, since it tries to `return`

from inside a call to `cases`

, and `return`

only works inside the immediate scope.

Instead, we can use `callCC`

to implement a non-local return like this:

```
loop_llength <- function(llist) {
callCC(function(escape) {
acc <- 0
repeat {
cases(llist,
NIL -> escape(acc),
CONS(car, cdr) -> {
acc <<- acc + 1
llist <<- cdr
})
}
})
}
```

Notice that we have to use the `<<-`

assignment operator here. This is for the same reason that we need a non-local return. The expression inside the call to `cases`

is evaluated in a different environment than the local function environment, so to get to the actual variables we want to assign to, we need the non-local assignment operator.

It is possible to avoid `cases`

using other functions from the `pmatch`

package, but the result is vastly more compliated since pattern matching and expressions that should be evaluated per case needs to handle scoping. We can automatically make such a function using `tailr`

, however:

`tr_llength <- tailr::loop_transform(llength)`

The function we generate is rather complicated

```
tr_llength
#> function(llist, acc = 0) {
#> cases(llist,
#> NIL -> acc,
#> CONS(car, cdr) -> llength(cdr, acc + 1))
#> }
#> <bytecode: 0x7fa2f8028740>
```

but, then, it is not one we want to manually inspect in any case.

The automatically generated function is complicated, but it actually outcompetes the hand-written loop version.

```
make_llist <- function(n) {
l <- NIL
for (i in 1:n) {
l <- CONS(i, l)
}
l
}
test_llist <- make_llist(100)
bm <- microbenchmark::microbenchmark(llength(test_llist),
loop_llength(test_llist),
tr_llength(test_llist))
bm
#> Unit: milliseconds
#> expr min lq mean median uq
#> llength(test_llist) 56.07179 59.44156 64.32394 61.27681 64.38318
#> loop_llength(test_llist) 58.98844 62.91033 67.65086 64.55698 68.94217
#> tr_llength(test_llist) 39.97359 41.06288 44.73603 42.93393 45.02601
#> max neval
#> 108.95810 100
#> 113.32328 100
#> 88.02851 100
boxplot(bm)
```

It is, of course, possible to write a faster hand-written function to deal with this case, but it will be about as complicated as the automatically generated function, and you don’t really want to write that by hand.

As you have no doubt noticed about `llength`

, it is not in fact tail-recursive, from the look of it, since the final recursion is enclosed by a call to `cases`

. The function is only tail-recursive because it can be translated into one by rewriting the `cases`

function call to a sequence of `if`

-statements. The `tailr`

package doesn’t handle `cases`

from `pmatch`

by knowing about this package. Instead, it has a mechanism that lets you provide re-writing rules.

If you set the attribute “tailr_transform” on a function, and set this attribute to a function, then that function will be called when `tailr`

sees the function, before it attempts any other processing. The attribute must be a function that maps an expression to another, re-written, expression. The one for `cases`

looks like this:

```
tailr_transform_call <- function(expr) {
stopifnot(rlang::call_name(expr) == "cases")
args <- rlang::call_args(expr)
value <- args[[1]]
patterns <- args[-1]
eval(rlang::expr(cases_expr(!!value, !!!patterns)))
}
attr(cases, "tailr_transform") <- tailr_transform_call
```

You can use this mechanism to support tail-recursion for non-tail-recursive functions that can be rewritten to be tail-recursive.