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.
as_data_mask(data, parent = NULL) as_data_pronoun(data) new_data_mask(bottom, top = bottom, parent = NULL)
A data frame or named vector of masking data.
Soft-deprecated. This argument no longer has any effect. The parent of the data mask is determined from either:
The environment containing masking objects if the data mask is one environment deep. The bottom environment if the data mask comprises multiple environment.
The last environment of the data mask. If the data mask
is only one environment deep,
A data mask that you can supply to
Most of the time you can just call
eval_tidy() with a list or a
data frame and the data mask will be constructed automatically.
There are three main use cases for manual creation of data masks:
eval_tidy() is called with the same data in a tight loop.
Because there is some overhead to creating tidy eval data masks,
constructing the mask once and reusing it for subsequent
evaluations may improve performance.
When several expressions should be evaluated in the exact same
environment because a quoted expression might create new objects
that can be referred in other quoted expressions evaluated at a
later time. One example of this is
tibble::lst() where new
columns can refer to previous ones.
When your data mask requires special features. For instance the data frame columns in dplyr data masks are implemented with active bindings.
There are two ways of constructing an rlang data mask manually:
as_data_mask() transforms a list or data frame to a data mask.
It automatically installs the 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 (see section
as_data_mask() it does not install the
so you need to provide one yourself. You can provide a pronoun
as_data_pronoun() or your own pronoun class.
as_data_pronoun() will create a pronoun from a list, an
environment, or an rlang data mask. In the latter case, the whole
ancestry is looked up from the bottom to the top of the mask.
Functions stored in the mask are bypassed by the pronoun.
Once you have built a data mask, simply pass it to
data argument. You can repeat this as many times as
needed. Note that any objects created there (perhaps because of a
<-) will persist in subsequent evaluations.
In some cases you'll need several levels in your data mask. One good reason is when you include functions in the mask. It's a good idea to keep data objects one level lower than function objects, so that the former cannot override the definitions of the latter (see examples).
In that case, set up all your environments and keep track of the
bottom child and the top parent. You'll need to pass both to
Note that the parent of the top environment is completely
undetermined, you shouldn't expect it to remain the same at all
times. This parent is replaced during evaluation by
to one of the following environments:
The default environment passed as the
env argument of
The environment of the current quosure being evaluated, if applicable.
Consequently, all masking data should be contained between the bottom and top environment of the data mask.
Passing environments to
as_data_mask() is soft-deprecated. Please
build a data mask with
parent argument no longer has any effect. The parent of the
data mask is determined from either:
env argument of
Quosure environments when applicable
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 objects from user data mask objects in the current environment.
# Evaluating in a tidy evaluation environment enables all tidy # features: mask <- as_data_mask(mtcars) eval_tidy(quo(letters), mask)#>  "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" #>  "t" "u" "v" "w" "x" "y" "z"# You can install new pronouns in the mask: mask$.pronoun <- as_data_pronoun(list(foo = "bar", baz = "bam")) eval_tidy(quo(.pronoun$foo), mask)#>  "bar"# 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()#>  6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4# If new objects are created in the mask, they persist in the # subsequent calls: eval_tidy(quote(new <- cyl + am), mask)#>  7 7 5 6 8 6 8 4 4 6 6 8 8 8 8 8 8 5 5 5 4 8 8 8 8 5 5 5 9 7 9 5#>  14 14 10 12 16 12 16 8 8 12 12 16 16 16 16 16 16 10 10 10 8 16 16 16 16 #>  10 10 10 18 14 18 10# In some cases your data mask is a whole chain of environments # rather than a single environment. You'll have to use # `new_data_mask()` and let it know about the bottom of the mask # (the last child of the environment chain) and the topmost parent. # A common situation where you'll want a multiple-environment mask # is when you include functions in your mask. In that case you'll # put functions in the top environment and data in the bottom. This # will prevent the data from overwriting the functions. top <- new_environment(list(`+` = base::paste, c = base::paste)) # Let's add a middle environment just for sport: middle <- env(top) # And finally the bottom environment containing data: bottom <- env(middle, a = "a", b = "b", c = "c") # We can now create a mask by supplying the top and bottom # environments: mask <- new_data_mask(bottom, top = top) # This data mask can be passed to eval_tidy() instead of a list or # data frame: eval_tidy(quote(a + b + c), data = mask)#>  "a b c"# Note how the function `c()` and the object `c` are looked up # properly because of the multi-level structure: eval_tidy(quote(c(a, b, c)), data = mask)#>  "a b c"# new_data_mask() does not create data pronouns, but # data pronouns can be added manually: mask$.fns <- as_data_pronoun(top) # The `.data` pronoun should generally be created from the # mask. This will ensure data is looked up throughout the whole # ancestry. Only non-function objects are looked up from this # pronoun: mask$.data <- as_data_pronoun(mask) mask$.data$c#>  "c"# Now we can reference the values with the pronouns: eval_tidy(quote(c(.data$a, .data$b, .data$c)), data = mask)#>  "a b c"