---
title: "Getting started with stbl"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Getting started with stbl}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup}
library(stbl)
```

The goal of {stbl} is to help you stabilize function arguments *before* you use them.
This is especially important when a function performs a slow or expensive operation, like writing to a database or calling a web API.
You want to fail fast and with a clear error message if the inputs aren't right.

This vignette demonstrates this principle by incrementally building a single function, `register_user()`, which validates several arguments before hypothetically sending them to an external service.

## The `register_user()` Function

Here is the base function we'll be improving.
Without any checks, it's vulnerable to bad inputs that could cause cryptic errors later on or send corrupt data to our external service.

```{r}
register_user <- function(username,
                          email_address,
                          age,
                          is_premium_member,
                          interests) {
  # Imagine this is a slow API call, rather than simply returning the list()
  list(
    username = username,
    email_address = email_address,
    age = age,
    is_premium_member = is_premium_member,
    interests = interests
  )
}
```

## Step 1: Handling a Vector with `to_*()`

Let's start adding checks.
The first check will be for the `interests` argument.
We expect this to be a character vector, but we're not picky about the content.
`to_chr()` will convert inputs that are character-like (like factors or a simple list of strings) into a proper character vector.

```{r}
register_user <- function(username,
                          email_address,
                          age,
                          is_premium_member,
                          interests) {
  interests <- to_chr(interests)

  list(
    username = username,
    email_address = email_address,
    age = age,
    is_premium_member = is_premium_member,
    interests = interests
  )
}
```

The function still returns the list when `interests` is a character vector:

```{r}
register_user(
  username = "test_user", 
  email_address = "test@example.com", 
  age = 42, 
  is_premium_member = TRUE, 
  interests = c("R", "hiking")    # Note that this is already a character vector
) |> 
  # Note: Throughout this article, we pipe the result into `str()` to make it
  # easier to see the differences.
  str()
```

It also works when `interests` is a list of strings:

```{r}
register_user(
  username = "test_user", 
  email_address = "test@example.com", 
  age = 42, 
  is_premium_member = TRUE, 
  # This is a list, but becomes a character vector
  interests = list("R", "hiking") 
) |> str()
```

If the input is something that cannot be reasonably flattened to a character vector, it fails with a helpful error message:

```{r, error = TRUE}
# Fails because the list contains a function, which is not character-like.
register_user(
  username = "test_user", 
  email_address = "test@example.com", 
  age = 42, 
  is_premium_member = TRUE, 
  interests = list("R", mean)
)
```

## Step 2: Simple Scalar Coercion with `to_*_scalar()`

Next, we'll add checks for `age` and `is_premium_member`.
These arguments must each contain a single value.
We'll use the `_scalar` variants: `to_int_scalar()` and `to_lgl_scalar()`.
These functions are liberal in what they accept.
For example, `to_lgl_scalar()` understands that `1`, `"T"`, and `"True"` all mean `TRUE`.

```{r}
register_user <- function(username,
                          email_address,
                          age,
                          is_premium_member,
                          interests) {
  interests <- to_chr(interests)
  age <- to_int_scalar(age)
  is_premium_member <- to_lgl_scalar(is_premium_member)

  list(
    username = username,
    email_address = email_address,
    age = age,
    is_premium_member = is_premium_member,
    interests = interests
  )
}
```

When everything follows the rules, `register_user()` works:

```{r}
register_user(
  username = "test_user", 
  email_address = "test@example.com", 
  age = "42",                 # Coercible to integer
  is_premium_member = "True", # Coercible to logical
  interests = c("R", "hiking")
) |> str()
```

The `register_user()` function will fail with clear error messages if the input isn't a single value or can't be coerced.

For example, when age is not a single value, `register_user()` throws an error:

```{r, error = TRUE}
register_user(
  username = "test_user", 
  email_address = "test@example.com", 
  age = c(30, 31),            # Not a single value
  is_premium_member = TRUE, 
  interests = c("R", "hiking")
)
```

Likewise, this next example fails because "forty-two" cannot be converted to an integer:

```{r, error = TRUE}
register_user(
  username = "test_user", 
  email_address = "test@example.com", 
  age = "forty-two",          # Not coercible to integer
  is_premium_member = TRUE, 
  interests = c("R", "hiking")
)
```

## Step 3: Complex Validation with `stabilize_*()`

Finally, let's add more complex validation for `username` and `email_address`.
For these, simple type coercion isn't enough; we need to check their content and structure using `stabilize_chr_scalar()`.
This function first coerces the input to character, then applies a list of validation rules supplied via the `regex` argument (see the `stabilize_chr()` documentation for details).
If speed matters, you should prefer the faster `to_*()` functions and only "upgrade" to `stabilize_*()` when you need these additional checks.

This is our final, fully stabilized function:

```{r}
register_user <- function(username,
                          email_address,
                          age,
                          is_premium_member,
                          interests) {
  # Simple checks.
  interests <- to_chr(interests)
  age <- to_int_scalar(age)
  is_premium_member <- to_lgl_scalar(is_premium_member)

  # Make sure the username is a length-1 character vector without any spaces,
  # tabs, or newlines. "\\s" means "any space character".
  space_regex <- c("must not contain spaces" = "\\s")
  attr(space_regex, "negate") <- TRUE
  username <- stabilize_chr_scalar(
    username,
    regex = space_regex
  )

  # The email address has to have the pattern "*@*.*". Or, in regex, "^" (start
  # of string), "[...]+" (one or more of any character in the brackets), "@"
  # (the at sign), "[...]+" (one or more of any character in the brackets),
  # "\\." (a literal period), "[...]{2,}" (two or more of any character in the
  # brackets), "$" (end of string).
  email_regex <- "^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$"
  email_address <- stabilize_chr_scalar(
    email_address,
    regex = c("must be a valid email address" = email_regex)
  )

  list(
    username = username,
    email_address = email_address,
    age = age,
    is_premium_member = is_premium_member,
    interests = interests
  )
}
```

When all of the inputs follow the expectations, the call succeeds:

```{r}
register_user(
  username = "test_user", 
  email_address = "test@example.com", 
  age = 42, 
  is_premium_member = TRUE, 
  interests = c("R", "hiking")
) |> str()
```

If anything is wrong, it fails with informative error messages.

When the username has a space, `register_user()` fails with an informative error message:

```{r, error = TRUE}
register_user(
  username = "test user", 
  email_address = "test@example.com", 
  age = 42, 
  is_premium_member = TRUE, 
  interests = c("R", "hiking")
)
```

This example fails because the email address is invalid:

```{r, error = TRUE}
register_user(
  username = "test_user", 
  email_address = "not-a-valid-email", 
  age = 42, 
  is_premium_member = TRUE, 
  interests = c("R", "hiking")
)
```

## Conclusion

Our `register_user()` function is now robust against a variety of bad inputs.
We've built up layers of protection:

- `to_chr()` ensures `interests` is a character vector.
- `to_int_scalar()` and `to_lgl_scalar()` ensure `age` and `is_premium_member` are single values of the correct type.
- `stabilize_chr_scalar()` with regex rules ensures `username` and `email_address` meet specific content requirements.

If the user enters something strange, they'll get a clear error message *before* we do anything expensive, like calling an external API.
