eval_tidy() is a variant of base::eval() that powers the tidy evaluation framework. Like eval() it accepts user data as argument. Whereas eval() simply transforms the data to an environment, eval_tidy() transforms it to a data mask with as_data_mask(). Evaluating in a data mask enables the following features:

• Quosures. Quosures are expressions bundled with an environment. If data is supplied, objects in the data mask always have precedence over the quosure environment, i.e. the data masks the environment.

• Pronouns. If data is supplied, the .env and .data pronouns are installed in the data mask. .env is a reference to the calling environment and .data refers to the data argument. These pronouns are an escape hatch for the data mask ambiguity problem.

## Usage

eval_tidy(expr, data = NULL, env = caller_env())

## Arguments

expr

An expression or quosure to evaluate.

data

A data frame, or named list or vector. Alternatively, a data mask created with as_data_mask() or new_data_mask(). Objects in data have priority over those in env. See the section about data masking.

env

The environment in which to evaluate expr. This environment is not applicable for quosures because they have their own environments.

## When should eval_tidy() be used instead of eval()?

base::eval() is sufficient for simple evaluation. Use eval_tidy() when you'd like to support expressions referring to the .data pronoun, or when you need to support quosures.

If you're evaluating an expression captured with injection support, it is recommended to use eval_tidy() because users may inject quosures.

Note that unwrapping a quosure with quo_get_expr() does not guarantee that there is no quosures inside the expression. Quosures might be unquoted anywhere in the expression tree. For instance, the following does not work reliably in the presence of nested quosures:

my_quoting_fn <- function(x) {
x <- enquo(x)
expr <- quo_get_expr(x)
env <- quo_get_env(x)
eval(expr, env)
}

# Works:
my_quoting_fn(toupper(letters))

# Fails because of a nested quosure:
my_quoting_fn(toupper(!!quo(letters)))

## Stack semantics of eval_tidy()

eval_tidy() always evaluates in a data mask, even when data is NULL. Because of this, it has different stack semantics than base::eval():

• Lexical side effects, such as assignment with <-, occur in the mask rather than env.

• Functions that require the evaluation environment to correspond to a frame on the call stack do not work. This is why return() called from a quosure does not work.

• The mask environment creates a new branch in the tree representation of backtraces (which you can visualise in a browser() session with lobstr::cst()).

See also eval_bare() for more information about these differences.

• new_data_mask() and as_data_mask() for manually creating data masks.

## Examples


# With simple defused expressions eval_tidy() works the same way as
# eval():
fruit <- "apple"
vegetable <- "potato"
expr <- quote(paste(fruit, vegetable, sep = " or "))
expr
#> paste(fruit, vegetable, sep = " or ")

eval(expr)
#> [1] "apple or potato"
eval_tidy(expr)
#> [1] "apple or potato"

# Both accept a data mask as argument:
data <- list(fruit = "banana", vegetable = "carrot")
eval(expr, data)
#> [1] "banana or carrot"
eval_tidy(expr, data)
#> [1] "banana or carrot"

# The main difference is that eval_tidy() supports quosures:
with_data <- function(data, expr) {
quo <- enquo(expr)
eval_tidy(quo, data)
}
with_data(NULL, fruit)
#> [1] "apple"
with_data(data, fruit)
#> [1] "banana"

# eval_tidy() installs the .data and .env pronouns to allow
# users to be explicit about variable references:
with_data(data, .data$fruit) #> [1] "banana" with_data(data, .env$fruit)
#> [1] "apple"