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R Programming : Environment and Scope

Tutorial by:Maria Ghoste      Date: 2016-06-10 00:35:07

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In order to write functions in a proper way and avoid unusual errors, we need to know the concept of environment and scope in R.

R Programming Environment

Environment can be thought of as a collection of objects (functions, variables etc.). An environment is created when we first fire up the R interpreter. Any variable we define, is now in this environment. The top level environment available to us at the R command prompt is the global environment called R_GlobalEnv. Global environment can be referred to as .GlobalEnv in R codes as well. We can use the ls() function to show what variables and functions are defined in the current environment. Moreover, we can use the environment() function to get the current environment.

> a <- 2
> b <- 5
> f <- function(x) x<-0

> ls()
[1] "a" "b" "f"

> environment()
<environment: R_GlobalEnv>

> .GlobalEnv
<environment: R_GlobalEnv>

In the above example, we can see that a, b and f are in the R_GlobalEnv environment. Notice that x (in the argument of the function) is not in this global environment. When we define a function, a new environment is created. In the above example, the function f creates a new environment inside the global environment. Actually an environment has a frame, which has all the objects defined, and a pointer to the enclosing (parent) environment. Hence, x is in the frame of the new environment created by the function f. This environment will also have a pointer to R_GlobalEnv. Consider the following example for more clarification of the cascading of environments.

f <- function(f_x){
   g <- function(g_x){
       print("Inside g")
       print(environment())
       print(ls())
   }
   g(5)
   print("Inside f")
   print(environment())
   print(ls())
}

Now when we run it from the command prompt, we get.

> f(6)
[1] "Inside g"
<environment: 0x0000000010c2bdc8>
[1] "g_x"
[1] "Inside f"
<environment: 0x0000000010c2a870>
[1] "f_x" "g"  

> environment()
<environment: R_GlobalEnv>

> ls()
[1] "f"

Here, we defined function g inside f and it is clear that they both have different environments with different objects within their respective frames.

R Programming Scope

Let us consider the following example.

outer_func <- function(){
   b <- 20
   inner_func <- function(){
       c <- 30
   }
}
a <- 10

 

 
 

Here we have a function inner_func() nested within outer_func(). The variable c is local to it while, a and b are global. Now from the perspective of outer_func(), b is local to it and a is global. The variable c is completely invisible to outer_func(). If we assign a value to a variable within a function, it will be local and will not effect to any global variable even if the name matches. For example, if we have a function as below.

outer_func <- function(){
   a <- 20
   inner_func <- function(){
       a <- 30
       print(a)
   }
   inner_func()
   print(a)
}

When we call it,

> a <- 10

> outer_func()
[1] 30
[1] 20

> print(a)
[1] 10

We see that the variable a is created locally within the environment frame of both the functions and is different to that of the global environment frame.

Global variables can be read but when we try to assign to it, a new local variable is created instead. To make assignments to global variables, superassignment operator, <<-, is used. When using this operator within a function, it searches for the variable in the parent environment frame, if not found it keeps on searching the next level until it reaches the global environment. If the variable is still not found, it is created and assigned at the global level.

outer_func <- function(){
   inner_func <- function(){
       a <<- 30
       print(a)
   }
   inner_func()
   print(a)
}

On running this function,

> outer_func()
[1] 30
[1] 30
> print(a)
[1] 30

When the statement a <<- 30 is encountered within inner_func(), it looks for the variable a in outer_func() environment. When the search fails, it searches in R_GlobalEnv. Since, a is not defined in this global environment as well, it is created and assigned there which is now referenced and printed from within inner_func() as well as outer_func().

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R Programming

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