as_data_mask {rlang} | R Documentation |
A data mask is an environment (or possibly multiple environments forming an ancestry) containing user-supplied objects. Objects in the mask have precedence over objects in the environment (i.e. they mask those objects). Many R functions evaluate quoted expressions in a data mask so these expressions can refer to objects within the user data.
These functions let you construct a tidy eval data mask manually.
They are meant for developers of tidy eval interfaces rather than
for end users. Most of the time you can just call eval_tidy()
with user data and the data mask will be constructed automatically.
There are three main use cases for manual creation of data masks:
When eval_tidy()
is called with the same data in a tight loop.
Tidy eval data masks are a bit expensive to build so it is best
to construct it once and reuse it the other times for optimal
performance.
When several expressions should be evaluated in the same environment because a quoted expression might create new objects that can be referred in other quoted expressions evaluated at a later time.
When your data mask requires special features. For instance the data frame columns in dplyr data masks are implemented with active bindings.
as_data_mask(data, parent = base_env()) as_data_pronoun(data) new_data_mask(bottom, top = bottom, parent = base_env())
data |
A data frame or named vector of masking data. |
parent |
The parent environment of the data mask. |
bottom |
The environment containing masking objects if the data mask is one environment deep. The bottom environment if the data mask comprises multiple environment. |
top |
The last environment of the data mask. If the data mask
is only one environment deep, |
A data mask that you can supply to eval_tidy()
.
Creating a data mask for base::eval()
is a simple matter of
creating an environment containing masking objects that has the
user context as parent. eval()
automates this task when you
supply data as second argument. However a tidy eval data mask also
needs to enable support of quosures and data
pronouns. These functions allow manual construction
of tidy eval data masks:
as_data_mask()
transforms a data frame, named vector or
environment to a data mask. If an environment, its ancestry is
ignored. It automatically installs a data pronoun.
new_data_mask()
is a bare bones data mask constructor for
environments. You can supply a bottom and a top environment in
case your data mask comprises multiple environments.
Unlike as_data_mask()
it does not install the .data
pronoun
so you need to provide one yourself. You can provide a pronoun
constructed with as_data_pronoun()
or your own pronoun class.
as_data_pronoun()
constructs a tidy eval data pronoun that
gives more useful error messages than regular data frames or
lists, i.e. when an object does not exist or if an user tries to
overwrite an object.
To use a a data mask, just supply it to eval_tidy()
as data
argument. You can repeat this as many times as needed. Note that
any objects created there (perhaps because of a call to <-
) will
persist in subsequent evaluations:
All these functions are now stable.
In early versions of rlang data masks were called overscopes. We think data mask is a more natural name in R. It makes reference to masking in the search path which occurs through the same mechanism (in technical terms, lexical scoping with hierarchically nested environments). We say that that objects from user data mask objects in the current environment.
Following this change in terminology, as_data_mask()
and
new_overscope()
were soft-deprecated in rlang 0.2.0 in favour of
as_data_mask()
and new_data_mask()
.
# Evaluating in a tidy evaluation environment enables all tidy # features: mask <- as_data_mask(mtcars) eval_tidy(quo(letters), mask) # You can install new pronouns in the mask: mask$.pronoun <- as_data_pronoun(list(foo = "bar", baz = "bam")) eval_tidy(quo(.pronoun$foo), mask) # In some cases the data mask can leak to the user, for example if # a function or formula is created in the data mask environment: cyl <- "user variable from the context" fn <- eval_tidy(quote(function() cyl), mask) fn() # If new objects are created in the mask, they persist in the # subsequent calls: eval_tidy(quote(new <- cyl + am), mask) eval_tidy(quote(new * 2), mask)