It's the 'superassignment' operator. It does the assignment in the enclosing environment. That is, starting with the enclosing frame, it works its way up towards the global environment until it finds a variable called ecov_xy, and then assigns to it.
The scoping rules of the language define how value is assigned to free variables. R uses lexical scoping, which says the value for z is searched for in the environment where the function was defined. Note: Lexical scoping is also referred to as statical scoping.
Compare <- and = in the global environment in R The inbuilt function environment() returns the name of the current environment. We can see below that there is no difference between using <- or =.
The assignment operators in R allows you to assign data to a named object in order to store the data. Note that in almost scripting programming languages you can just use the equal (=) operator. However, in R it is recommended to use the arrow assignment (<-) and use the equal sign only to set arguments.
<<-
is most useful in conjunction with closures to maintain state. Here's a section from a recent paper of mine:
A closure is a function written by another function. Closures are so-called because they enclose the environment of the parent function, and can access all variables and parameters in that function. This is useful because it allows us to have two levels of parameters. One level of parameters (the parent) controls how the function works. The other level (the child) does the work. The following example shows how can use this idea to generate a family of power functions. The parent function (
power
) creates child functions (square
andcube
) that actually do the hard work.
power <- function(exponent) {
function(x) x ^ exponent
}
square <- power(2)
square(2) # -> [1] 4
square(4) # -> [1] 16
cube <- power(3)
cube(2) # -> [1] 8
cube(4) # -> [1] 64
The ability to manage variables at two levels also makes it possible to maintain the state across function invocations by allowing a function to modify variables in the environment of its parent. The key to managing variables at different levels is the double arrow assignment operator <<-
. Unlike the usual single arrow assignment (<-
) that always works on the current level, the double arrow operator can modify variables in parent levels.
This makes it possible to maintain a counter that records how many times a function has been called, as the following example shows. Each time new_counter
is run, it creates an environment, initialises the counter i
in this environment, and then creates a new function.
new_counter <- function() {
i <- 0
function() {
# do something useful, then ...
i <<- i + 1
i
}
}
The new function is a closure, and its environment is the enclosing environment. When the closures counter_one
and counter_two
are run, each one modifies the counter in its enclosing environment and then returns the current count.
counter_one <- new_counter()
counter_two <- new_counter()
counter_one() # -> [1] 1
counter_one() # -> [1] 2
counter_two() # -> [1] 1
It helps to think of <<-
as equivalent to assign
(if you set the inherits
parameter in that function to TRUE
). The benefit of assign
is that it allows you to specify more parameters (e.g. the environment), so I prefer to use assign
over <<-
in most cases.
Using <<-
and assign(x, value, inherits=TRUE)
means that "enclosing environments of the supplied environment are searched until the variable 'x' is encountered." In other words, it will keep going through the environments in order until it finds a variable with that name, and it will assign it to that. This can be within the scope of a function, or in the global environment.
In order to understand what these functions do, you need to also understand R environments (e.g. using search
).
I regularly use these functions when I'm running a large simulation and I want to save intermediate results. This allows you to create the object outside the scope of the given function or apply
loop. That's very helpful, especially if you have any concern about a large loop ending unexpectedly (e.g. a database disconnection), in which case you could lose everything in the process. This would be equivalent to writing your results out to a database or file during a long running process, except that it's storing the results within the R environment instead.
My primary warning with this: be careful because you're now working with global variables, especially when using <<-
. That means that you can end up with situations where a function is using an object value from the environment, when you expected it to be using one that was supplied as a parameter. This is one of the main things that functional programming tries to avoid (see side effects). I avoid this problem by assigning my values to a unique variable names (using paste with a set or unique parameters) that are never used within the function, but just used for caching and in case I need to recover later on (or do some meta-analysis on the intermediate results).
One place where I used <<-
was in simple GUIs using tcl/tk. Some of the initial examples have it -- as you need to make a distinction between local and global variables for statefullness. See for example
library(tcltk)
demo(tkdensity)
which uses <<-
. Otherwise I concur with Marek :) -- a Google search can help.
On this subject I'd like to point out that the <<-
operator will behave strangely when applied (incorrectly) within a for loop (there may be other cases too). Given the following code:
fortest <- function() {
mySum <- 0
for (i in c(1, 2, 3)) {
mySum <<- mySum + i
}
mySum
}
you might expect that the function would return the expected sum, 6, but instead it returns 0, with a global variable mySum
being created and assigned the value 3. I can't fully explain what is going on here but certainly the body of a for loop is not a new scope 'level'. Instead, it seems that R looks outside of the fortest
function, can't find a mySum
variable to assign to, so creates one and assigns the value 1, the first time through the loop. On subsequent iterations, the RHS in the assignment must be referring to the (unchanged) inner mySum
variable whereas the LHS refers to the global variable. Therefore each iteration overwrites the value of the global variable to that iteration's value of i
, hence it has the value 3 on exit from the function.
Hope this helps someone - this stumped me for a couple of hours today! (BTW, just replace <<-
with <-
and the function works as expected).
f <- function(n, x0) {x <- x0; replicate(n, (function(){x <<- x+rnorm(1)})())}
plot(f(1000,0),typ="l")
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