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These functions create new environments.

  • env() creates a child of the current environment by default and takes a variable number of named objects to populate it.

  • new_environment() creates a child of the empty environment by default and takes a named list of objects to populate it.



new_environment(data = list(), parent = empty_env())


..., data

<dynamic> Named values. You can supply one unnamed to specify a custom parent, otherwise it defaults to the current environment.


A parent environment.

Environments as objects

Environments are containers of uniquely named objects. Their most common use is to provide a scope for the evaluation of R expressions. Not all languages have first class environments, i.e. can manipulate scope as regular objects. Reification of scope is one of the most powerful features of R as it allows you to change what objects a function or expression sees when it is evaluated.

Environments also constitute a data structure in their own right. They are a collection of uniquely named objects, subsettable by name and modifiable by reference. This latter property (see section on reference semantics) is especially useful for creating mutable OO systems (cf the R6 package and the ggproto system for extending ggplot2).


All R environments (except the empty environment) are defined with a parent environment. An environment and its grandparents thus form a linear hierarchy that is the basis for lexical scoping in R. When R evaluates an expression, it looks up symbols in a given environment. If it cannot find these symbols there, it keeps looking them up in parent environments. This way, objects defined in child environments have precedence over objects defined in parent environments.

The ability of overriding specific definitions is used in the tidyeval framework to create powerful domain-specific grammars. A common use of masking is to put data frame columns in scope. See for example as_data_mask().

Reference semantics

Unlike regular objects such as vectors, environments are an uncopyable object type. This means that if you have multiple references to a given environment (by assigning the environment to another symbol with <- or passing the environment as argument to a function), modifying the bindings of one of those references changes all other references as well.

See also


# env() creates a new environment that inherits from the current
# environment by default
env <- env(a = 1, b = "foo")
#> [1] "foo"
identical(env_parent(env), current_env())
#> [1] TRUE

# Supply one unnamed argument to inherit from another environment:
env <- env(base_env(), a = 1, b = "foo")
identical(env_parent(env), base_env())
#> [1] TRUE

# Both env() and child_env() support tidy dots features:
objs <- list(b = "foo", c = "bar")
env <- env(a = 1, !!! objs)
#> [1] "bar"

# You can also unquote names with the definition operator `:=`
var <- "a"
env <- env(!!var := "A")
#> [1] "A"

# Use new_environment() to create containers with the empty
# environment as parent:
env <- new_environment()
#> <environment: R_EmptyEnv>

# Like other new_ constructors, it takes an object rather than dots:
new_environment(list(a = "foo", b = "bar"))
#> <environment: 0x5576a3b920f8>