vetr

R build status Project Status: WIP - Initial development is in progress, but there has not yet been a stable, usable release suitable for the public. Dependencies direct/recursive

Trust, but Verify

Easily

When you write functions that operate on S3 or unclassed objects you can either trust that your inputs will be structured as expected, or tediously check that they are.

vetr takes the tedium out of structure verification so that you can trust, but verify. It lets you express structural requirements declaratively with templates, and it auto-generates human-friendly error messages as needed.

Quickly

vetr is written in C to minimize overhead from parameter checks in your functions. It has no dependencies.

Declarative Checks with Templates

Templates

Declare a template that an object should conform to, and let vetr take care of the rest:

library(vetr)
tpl <- numeric(1L)
vet(tpl, 1:3)
## [1] "`length(1:3)` should be 1 (is 3)"

vet(tpl, "hello")
## [1] "`\"hello\"` should be type \"numeric\" (is \"character\")"

vet(tpl, 42)
## [1] TRUE

The template concept is based on vapply, but generalizes to all S3 objects and adds some special features to facilitate comparison. For example, zero length templates match any length:

tpl <- integer()
vet(tpl, 1L:3L)
## [1] TRUE

vet(tpl, 1L)
## [1] TRUE

And for convenience short (<= 100 length) integer-like numerics are considered integer:

tpl <- integer(1L)
vet(tpl, 1)       # this is a numeric, not an integer
## [1] TRUE

vet(tpl, 1.0001)
## [1] "`1.0001` should be type \"integer-like\" (is \"double\")"

vetr can compare recursive objects such as lists, or data.frames:

tpl.iris <- iris[0, ]      # 0 row DF matches any number of rows in object
iris.fake <- iris
levels(iris.fake$Species)[3] <- "sibirica"   # tweak levels

vet(tpl.iris, iris)
## [1] TRUE

vet(tpl.iris, iris.fake)
## [1] "`levels(iris.fake$Species)[3]` should be \"virginica\" (is \"sibirica\")"

From our declared template iris[0, ], vetr infers all the required checks. In this case, vet(iris[0, ], iris.fake, stop=TRUE) is equivalent to:

stopifnot_iris <- function(x) {
  stopifnot(
    is.data.frame(x),
    is.list(x),
    length(x) == length(iris),
    identical(lapply(x, class), lapply(iris, class)),
    is.integer(attr(x, 'row.names')),
    identical(names(x), names(iris)),
    identical(typeof(x$Species), "integer"),
    identical(levels(x$Species), levels(iris$Species))
  )
}
stopifnot_iris(iris.fake)
## Error in stopifnot_iris(iris.fake): identical(levels(x$Species), levels(iris$Species)) is not TRUE

vetr saved us typing, and the time and thought needed to come up with what needs to be compared.

You could just as easily have created templates for nested lists, or data frames in lists. Templates are compared to objects with the alike function. For a thorough description of templates and how they work see the alike vignette. For template examples see example(alike).

Auto-Generated Error Messages

Let’s revisit the error message:

vet(tpl.iris, iris.fake)
## [1] "`levels(iris.fake$Species)[3]` should be \"virginica\" (is \"sibirica\")"

It tells us:

vetr does what it can to reduce the time from error to resolution. The location of failure is generated such that you can easily copy it in part or full to the R prompt for further examination.

Vetting Expressions

You can combine templates with && / ||:

vet(numeric(1L) || NULL, NULL)
## [1] TRUE

vet(numeric(1L) || NULL, 42)
## [1] TRUE

vet(numeric(1L) || NULL, "foo")
## [1] "`\"foo\"` should be `NULL`, or type \"numeric\" (is \"character\")"

Templates only check structure. When you need to check values use . to refer to the object:

vet(numeric(1L) && . > 0, -42)  # strictly positive scalar numeric
## [1] "`-42 > 0` is not TRUE (FALSE)"

vet(numeric(1L) && . > 0, 42)
## [1] TRUE

If you do use the . symbol in your vetting expressions in your packages, you will need to include utils::globalVariables(".") as a top-level call to avoid the “no visible binding for global variable ‘.’” R CMD check NOTE.

You can compose vetting expressions as language objects and combine them:

scalar.num.pos <- quote(numeric(1L) && . > 0)
foo.or.bar <- quote(character(1L) && . %in% c('foo', 'bar'))
vet.exp <- quote(scalar.num.pos || foo.or.bar)

vet(vet.exp, 42)
## [1] TRUE

vet(vet.exp, "foo")
## [1] TRUE

vet(vet.exp, "baz")
## [1] "At least one of these should pass:"                         
## [2] "  - `\"baz\" %in% c(\"foo\", \"bar\")` is not TRUE (FALSE)" 
## [3] "  - `\"baz\"` should be type \"numeric\" (is \"character\")"

all_bw is available for value range checks (~10x faster than isTRUE(all(. >= x & . <= y)) for large vectors):

vet(all_bw(., 0, 1), runif(5) + 1)
## [1] "`all_bw(runif(5) + 1, 0, 1)` is not TRUE (is chr: \"`1.614709` at index 1 not in `[0,1]`\")"

There are a number of predefined vetting tokens you can use in your vetting expressions such as:

vet(NUM.POS, -runif(5))    # positive numeric; see `?vet_token` for others
## [1] "`-runif(5)` should contain only positive values, but has negatives"

Vetting expressions are designed to be intuitive to use, but their implementation is complex. We recommend you look at example(vet) for usage ideas, or at the “Non Standard Evaluation” section of the vignette for the gory details.

vetr in Functions

If you are vetting function inputs, you can use the vetr function, which works just like vet except that it is streamlined for use within functions:

fun <- function(x, y) {
  vetr(numeric(1L), logical(1L))
  TRUE   # do work...
}
fun(1:2, "foo")
## Error in fun(x = 1:2, y = "foo"): For argument `x`, `length(1:2)` should be 1 (is 2)

fun(1, "foo")
## Error in fun(x = 1, y = "foo"): For argument `y`, `"foo"` should be type "logical" (is "character")

vetr automatically matches the vetting expressions to the corresponding arguments and fetches the argument values from the function environment.

See vignette for additional details on how the vetr function works.

Additional Documentation

Development Status

vetr is still in development, although most of the features are considered mature. The most likely area of change is the treatment of function and language templates (e.g. alike(sum, max)), and more flexible treatment of list templates (e.g. in future lists may be allowed to be different lengths so long as every named element in the template exists in the object).

Installation

This package is available on CRAN:

install.packages('vetr')

It has no runtime dependencies.

For the development version use remotes::install_github('brodieg/vetr@development') or:

f.dl <- tempfile()
f.uz <- tempfile()
github.url <- 'https://github.com/brodieG/vetr/archive/development.zip'
download.file(github.url, f.dl)
unzip(f.dl, exdir=f.uz)
install.packages(file.path(f.uz, 'vetr-development'), repos=NULL, type='source')
unlink(c(f.dl, f.uz))

The master branch typically mirrors CRAN and should be stable.

Alternatives

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

The following packages also perform related tasks, although we do not review them:

Acknowledgments

Thank you to:

About the Author

Brodie Gaslam is a hobbyist programmer based on the US East Coast.