Scope of variables

Remarks

The most common pitfall with scope arises in parallelization. All variables and functions must be passed into a new environment that is run on each thread.

Environments and Functions

Variables declared inside a function only exist (unless passed) inside that function.

x <- 1

foo <- function(x) {
    y <- 3
    z <- x + y
    return(z)
}

y

Error: object 'y' not found

Variables passed into a function and then reassigned are overwritten, but only inside the function.

foo <- function(x) {
    x <- 2
    y <- 3
    z <- x + y
    return(z)
}

foo(1)
x

5

1

Variables assigned in a higher environment than a function exist within that function, without being passed.

foo <- function() {
    y <- 3
    z <- x + y
    return(z)
}
    
foo()

4

Explicit Assignment of Environments and Variables

Environments in R can be explicitly call and named. Variables can be explicitly assigned and call to or from those environments.

A commonly created environment is one which encloses package:base or a subenvironment within package:base.

e1 <- new.env(parent = baseenv())
e2 <- new.env(parent = e1)

Variables can be explicitly assigned and call to or from those environments.

assign("a", 3, envir = e1)
   get("a", envir = e1)
   get("a", envir = e2)

3

3

Since e2 inherits from e1, a is 3 in both e1 and e2. However, assigning a within e2 does not change the value of a in e1.

assign("a", 2, envir = e2)
   get("a", envir = e2)
   get("a", envir = e1)

3

2

Function Exit

The on.exit() function is handy for variable clean up if global variables must be assigned.

Some parameters, especially those for graphics, can only be set globally. This small function is common when creating more specialized plots.

new_plot <- function(...) {
     
     old_pars <- par(mar = c(5,4,4,2) + .1, mfrow = c(1,1))
     on.exit(par(old_pars))
     plot(...)
 }

Global Assignment

Variables can be assigned globally from any environment using <<-. bar() can now access y.

bar <- function() {
    z <- x + y
    return(z)
}
    
foo <- function() {
    y <<- 3
    z <- bar()
    return(z)
}

foo()

4

Global assignment is highly discouraged. Use of a wrapper function or explicitly calling variables from another local environment is greatly preferred.

Packages and Masking

Functions and objects in different packages may have the same name. The package loaded later will 'mask' the earlier package and a warning message will be printed. When calling the function by name, the function from the most recently loaded package will be run. The earlier function can be accessed explicitly.

library(plyr)
library(dplyr)

Attaching package: ‘dplyr’

The following objects are masked from ‘package:plyr’:

arrange, count, desc, failwith, id, mutate, rename, summarise, summarize

The following objects are masked from ‘package:stats’:

filter, lag

The following objects are masked from ‘package:base’:

intersect, setdiff, setequal, union

When writing code, it is always best practice to call functions explicitly using package::function() specifically to avoid this issue.

Sub functions

Functions called within a function (ie subfunctions) must be defined within that function to access any variables defined in the local environment without being passed.

This fails:

bar <- function() {
    z <- x + y
    return(z)
}
    
foo <- function() {
    y <- 3
    z <- bar()
    return(z)
}

foo()

Error in bar() : object 'y' not found

This works:

foo <- function() {
    
    bar <- function() {
        z <- x + y
        return(z)
    }
    
    y <- 3
    z <- bar()
    return(z)
}
    
foo()

4



2016-07-23
2016-09-19
R Language Pedia
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