def-magical.Rmd

# Avoid magical defaults {#def-magical}

```{r, include = FALSE, cache = FALSE}
source("common.R")
source("fun_def.R")
```

```{r, eval = FALSE, include = FALSE}
funs <- c(pkg_funs("base"), pkg_funs("stats"), pkg_funs("utils"))
funs %>% funs_formals_keep(~ is_symbol(.x) && !is_missing(.x))
pkg_funs("base") %>% funs_body_keep(has_call, "missing")
```

## What's the problem?

If a function behaves differently when the default value is supplied explicitly, we say it has a __magical default__. Magical defaults are best avoided because they make it harder to interpret the function specification.

## What are some examples?

*   In `data.frame()`, the default argument for `row.names` is `NULL`, but
    if you supply it directly you get a different result:

    ```{r}
    fun_call(data.frame)
    
    x <- setNames(nm = letters[1:3])
    data.frame(x)
    data.frame(x, row.names = NULL)
    ```
    
*   In `hist()`, the default value of `xlim` is `range(breaks)`, and the
    default value for `breaks` is `"Sturges"`. `range("Sturges")` returns 
    `c("Sturges", "Sturges")` which doesn't work when supplied explicitly:
    
    ```{r, error = TRUE, fig.show = "hide"}
    fun_call(hist.default)
    
    hist(1:10, xlim = c("Sturges", "Sturges"))
    ```

*   In `Vectorize()`, the default argument for `vectorize.args` is `arg.names`, 
    but this variable is defined inside of `Vectorize()`, so if you supply it
    explicitly you get an error.

    ```{r, error = TRUE}
    fun_call(Vectorize)
    
    Vectorize(rep.int, vectorize.args = arg.names)
    ```

*   In `rbeta()`, the default value of `ncp` is 0, but if you explicitly supply
    it the function uses a different algorithm:
    
    ```{r}
    rbeta
    ```
    
*   In `table()`, the default value of `dnn` is `list.names(...)`; but 
    `list.names()` is only defined inside of `table()`. 
    
*   `readr::read_csv()` has `progress = show_progress()`, but until version
    1.3.1, `show_progress()` was not exported from the package. That means if you 
    attempted to run it yourself, you'd see an error message:
   
    ```{r, error = TRUE}
    show_progress()
    ```

*   In `usethis::use_rmarkdown_template()`, `template_dir` has the default value
    of `tolower(asciify(template_name))`, but `asciify` is not exported. That
    means there's no way to interactively explore this default value.

## What are the exceptions?

It's ok to use this behaviour when you want the default value of one argument to be the same as another. For example, take `rlang::set_names()`, which allows you to create a named vector from two inputs:

```{r}
fun_call(set_names)

set_names(1:3, letters[1:3])
```

The default value for the names is the vector itself. This provides a convenient shortcut for naming a vector with itself:

```{r}
set_names(letters[1:3])
```

You can see this same technique in `merge()`, where `all.x` and `all.y` default to the same value as `all`, and in `factor()` where `labels` defaults to the same value as `levels`.

If you use this technique, make sure that you never use the value of an argument that comes later in the argument list. For example, in `file.copy()` `overwrite` defaults to the same value as `recursive`, but the `recursive` argument is defined after `overwrite`:

```{r}
fun_call(file.copy)
```

This makes the defaults arguments harder to understand because you can't just read from left-to-right.

## What causes the problem?

There are three primary causes:

*   Overuse of lazy evaluation of default values, which are evaluated in the
    environment of the function, as described in 
    [Advanced R](https://adv-r.hadley.nz/functions.html#default-arguments). 
    Here's a simple example:
    
    ```{r}
    f1 <- function(x = y) {
      y <- trunc(Sys.time(), units = "months")
      x
    }
    
    y <- 1
    f1()
    f1(y)
    ```
    
    When `x` takes the value `y` from its default, it's evaluated inside the 
    function, yielding `1`. When `y` is supplied explicitly, it is evaluated 
    in the caller environment, yielding `2`.

*   Use of `missing()` so that the default value is never consulted:

    ```{r}
    f2 <- function(x = 1) {
      if (missing(x)) {
        2
      } else {
        x
      }
    }
    
    f2()
    f2(1)
    ```

*   In packages, it's easy to use a non-exported function without thinking 
    about it. This function is available to you, the package author, but not 
    the user of the package, which makes it harder for them to understand
    how a package works.

## How do I remediate the problem?

This problem is generally easy to avoid for new functions:

* Don't use default values that depend on variables defined inside the function.

* Don't use `missing()`[^missing-exceptions].
  
* Don't use unexported functions.

[^missing-exceptions]: The only exceptions are described in Sections \@ref(args-mutually-exclusive) and \@ref(args-compound).

If you have a made a mistake in an older function you can remediate it by using a `NULL` default, as described in Chapter \@ref(def-short). If the problem is caused by an unexported function, you can also choose to document and export it.

```{r}
`%||%` <- function(x, y) if (is.null(x)) y else x

f1_better <- function(x = NULL) {
  y <- trunc(Sys.time(), units = "weeks")
  x <- x %||% y
  
  x
}

f2_better <- function(x = NULL) {
  x <- x %||% 2
  
  x
}
```

This modification should not break existing code, because expands the function interface: all previous code will continue to work, and the function will also work if the argument is passed `NULL` input (which probably didn't previously).

For functions like `data.frame()` where `NULL` is already a permissible value, you'll need to use a sentinel object, as described in Section \@ref(args-default-sentinel).

```{r}
sentinel <- function() structure(list(), class = "sentinel")
is_sentinel <- function(x) inherits(x, "sentinel")

data.frame_better <- function(..., row.names = sentinel()) {
  if (is_sentinel(row.names)) {
    # old default behaviour
  }
}
```