# vetR - Trust, but Verify

knitr::opts_chunk\$set(error=TRUE, comment=NA) library(vetr)

## Vetting Expressions

### Non Standard Evaluation

#### Vetting Expressions are Language Objects

vet captures the first argument unevaluated. For example in:

vet(. > 0, 1:3)

. > 0 is captured, processed, and evaluated in a special manner. This is a common pattern in R (e.g. as in with, subset, etc.) called Non Standard Evaluation (NSE). One additional wrinkle with vet is that symbols in the captured expression are recursively substituted:

a <- quote(integer() && . > 0) b <- quote(logical(1L) && !is.na(.)) c <- quote(a || b) vet(c, 1:3)

The above is thus equivalent to:

vet((integer() && . > 0) || (logical(1L) && !is.na(.)), 1:3)

The recursive substitution removes the typical limitation on "programming" with NSE, although there are a few things to know:

• Symbols in vetting expressions that evaluate to language objects (calls or symbols) in the parent frame are substituted with the corresponding language object.
• The result of this substitution is implicitly wrapped in parentheses to avoid operator precedence problems.
• The function part of a call is never substituted (e.g. the fun in fun(a, b)); this extends to operators.
• . is never substituted, though you can work around that by escaping it with an additional . (i.e. ..).
• You must take particular care when constructing vetting expressions for language objects.

To illustrate the last point, suppose we want to check that an object is a call in the form x + y, then we could use:

vet(quote(x + y), my.call) # notice quote

Or:

tpl.call <- quote(quote(x + y)) # notice quote(quote(...)) vet(tpl.call, my.call)

Additionally, you will need to ensure that x and y themselves do not evaluate to language objects in the parent frame.

#### Parsing and Evaluation Rules

Once a vetting expression has been recursively substituted, it is parsed into tokens. Tokens are the parts of the vetting expression bounded by the && and || operators and optionally enclosed in parentheses. For example, there are three tokens in the following vetting expression:

logical(1) || (numeric(1) && (. > 0 & . < 1))

They are logical(1), numeric(1), and . > 0 & . < 1. The last token is just one token not because of the parentheses around it but because it is a call to & as opposed to &&. Here we use the parentheses to remove parsing ambiguity caused by & and && having the same operator precedence.

After the tokens have been identified they are classified as standard tokens or template tokens. Standard tokens are those that contain the . symbol. Every other token is considered a template token.

Standard tokens are further processed by substituting any . with the value of the object being vetted. These tokens are then evaluated and if all(<result-of-evaluation>) is TRUE then the tokens pass, otherwise they fail. Note all(logical(0L)) is TRUE. With:

vet(. > 0, 1:3)

. > 0 becomes 1:3 > 0, which evaluates to c(TRUE, TRUE, TRUE) and the token passes.

Template tokens, i.e. tokens without a . symbol, are evaluated and the resulting R object is sent along with the object to vet to alike for structural comparison. If alike returns TRUE then the token passes, otherwise it fails.

Finally, the result of evaluating each token is plugged back into the original expression. So1:

vet(logical(1) || (numeric(1) && (. > 0 & . < 1)), 42) # becomes: alike(logical(1L), 42) || (alike(numeric(1L), 42) && all(42 > 0 & 42 < 1)) # becomes: FALSE || (TRUE && FALSE) # becomes: FALSE

And the vetting fails:

vet(logical(1) || (numeric(1) && (. > 0 & . < 1)), 42)

### Special Cases

If you need to reference a literal dot (.) in a token, you can escape it by adding another dot so that . becomes ... If you want to reference ... you'll need to use ..... If you have a standard token that does not reference the vetting object (i.e. does not use .) you can mark it as a standard token by wrapping it in .() (if you want to use a literal .() you can use ..()).

If you need && or || to be interpreted literally you can wrap the call in I to tell vet to treat the entire call as a single token:

I(length(a) == length(b) && . %in% 0:1)

vet will stop searching for tokens at the first call to a function other than (, &&, and ||. The use of I here is just an example of this behavior and convenient since I does not change the meaning of the vetting token. An implication of this is you should not nest template tokens inside functions as vet will not identify them as template tokens and you may get unexpected results. For example:

I(logical(1L) && my_special_fun(.))

will always fail because logical(1L) is part of a standard token and is evaluated as FALSE rather than used a template token for a scalar logical.

## In Functions

The vetr function streamlines parameter checks in functions. It behaves just like vet, except that you need only specify the vetting expressions. The objects to vet are captured from the function environment:

fun <- function(x, y, z) { vetr( matrix(numeric(), ncol=3), logical(1L), character(1L) && . %in% c("foo", "bar") ) TRUE # do work... } fun(matrix(1:12, 3), TRUE, "baz") fun(matrix(1:12, 4), TRUE, "baz") fun(matrix(1:12, 4), TRUE, "foo")

The arguments to vetr are matched to the arguments of the enclosing function in the same way as with match.call. For example, if we wished to vet just the third argument:

fun <- function(x, y, z) { vetr(z=character(1L) && . %in% c("foo", "bar")) TRUE # do work... } fun(matrix(1:12, 3), TRUE, "baz") fun(matrix(1:12, 4), TRUE, "bar")

Vetting expressions work the same way with vetr as they do with vet.

## Performance Considerations

### Benchmarks

vetr is written primarily in C to minimize the performance impact of adding validation checks to your functions. Performance should be faster than using stopifnot except for the most trivial of checks. The vetr function itself carries some additional overhead from matching arguments, but it should still be faster than stopifnot except in the simplest of cases. Here we run our checks on valid iris objects we used to illustrate declarative checks:

vetr_iris <- function(x) vetr(tpl.iris) bench_mark(times=1e4, vet(tpl.iris, iris), vetr_iris(iris), stopifnot_iris(iris) # defined in "Templates" section )

Performance is optimized for the success case. Failure cases should still perform reasonably well, but will be slower than most success cases.

### Templates and Performance

Complex templates will be slower to evaluate than simple ones, particularly for lists with lots of nested elements. Note however that the cost of the vetting expression is a function of the complexity of the template, not that of the value being vetted.

We recommend that you predefine templates in your package and not in the validation expression since some seemingly innocuous template creation expressions carry substantial overhead:

bench_mark(data.frame(a=numeric()))

In this case the data.frame call alone take over 100us. In your package code you could use:

df.tpl <- data.frame(a=numeric()) my_fun <- function(x) { vetr(x=df.tpl) TRUE # do work }

This way the template is created once on package load and re-used each time your function is called.

## Alternatives

There are many alternatives available to vetr. We do a survey of the following in our parameter validation functions review:

1We take some liberties in this example for clarity. For instance, alike returns a character vector on failure, not FALSE, so really what vet is doing is isTRUE(alike(...)).