Use of the <<- operator in R [duplicate] - r

I just finished reading about scoping in the R intro, and am very curious about the <<- assignment.
The manual showed one (very interesting) example for <<-, which I feel I understood. What I am still missing is the context of when this can be useful.
So what I would love to read from you are examples (or links to examples) on when the use of <<- can be interesting/useful. What might be the dangers of using it (it looks easy to loose track of), and any tips you might feel like sharing.

<<- 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 and cube) 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")

The <<- operator can also be useful for Reference Classes when writing Reference Methods. For example:
myRFclass <- setRefClass(Class = "RF",
fields = list(A = "numeric",
B = "numeric",
C = function() A + B))
myRFclass$methods(show = function() cat("A =", A, "B =", B, "C =",C))
myRFclass$methods(changeA = function() A <<- A*B) # note the <<-
obj1 <- myRFclass(A = 2, B = 3)
obj1
# A = 2 B = 3 C = 5
obj1$changeA()
obj1
# A = 6 B = 3 C = 9

I use it in order to change inside map() an object in the global environment.
a = c(1,0,0,1,0,0,0,0)
Say I want to obtain a vector which is c(1,2,3,1,2,3,4,5), that is if there is a 1, let it 1, otherwise add 1 until the next 1.
map(
.x = seq(1,(length(a))),
.f = function(x) {
a[x] <<- ifelse(a[x]==1, a[x], a[x-1]+1)
})
a
[1] 1 2 3 1 2 3 4 5

Related

equivalent of within(), attach() etc. for working within an environment?

I want to add an environment to a search path and modify the values of variables within that environment, in a limited chunk of code, without having to specify the name of the environment every time I refer to a variable: for example, given the environment
ee <- list2env(list(x=1,y=2))
Now I would like to do stuff like
ee$x <- ee$x+1
ee$y <- ee$y*2
ee$z <- 6
but without appending ee$ to everything (or using assign("x", ee$x+1, ee) ... etc.): something like
in_environment(ee, {
x <- x+1
y <- y+2
z <- 6
})
Most of the solutions I can think of are explicitly designed not to modify the environment, e.g.
?attach: "The database is not actually attached. Rather, a new environment
is created on the search path ..."
within(): takes lists or data frames (not environments) "... and makes the corresponding modifications to a copy of ‘data’"
There are two problems with <<-: (1) using it will cause NOTEs in CRAN checks (I think? can't find direct evidence of this, but e.g. see here — maybe this only happens because of the appearance of assigning to a locally undefined symbol? I guess I could put this in a package and test it with --as-cran to confirm ...); (2) it will try to assign in the parent environment, which in a package context [which this is] will be locked ...
I suppose I could use a closure as described in section 10.7 of the Introduction to R by doing
clfun <- function() {
x <- 1
y <- 2
function(...) {
x <<- x + 1
y <<- y * 2
}
}
myfun <- clfun()
This seems convoluted (but I guess not too bad?) but:
will still incur problem #1 (CRAN check?).
I think (??) it won't work with variables that don't already exist in the environment (would need an explicit assign() for that ...)
doesn't allow a choice of which environment to operate in - it's necessarily going to work in the enclosing environment, not with arbitrary environment ee
Am I missing something obvious and idiomatic?
Thanks to #Nuclear03020704 ! I think with() was what I wanted all along; I was incorrectly assuming that it would also create a local copy of the environment, but it only does this if the data argument is not already an environment.
ee <- list2env(list(x=1,y=2))
with(ee, {
x <- x+1
y <- y+2
z <- 6
})
does exactly that I want.
Just had another idea, which also seems to have some drawbacks: using a big eval clause. Rather than make my question a long laundry list of unsatisfactory solutions, I'll add it here.
myfun <- function() {
eval(quote( {
x <- x+1
y <- y*2
z <- 3
}), envir=ee)
}
This does seem to work, but also seems very weird/mysterious! I hate to think about explaining it to someone who's being using R for less than 10 years ... I suppose I could write an in_environment() based on this, but I'd have to be very careful to capture the expression properly without evaluating it ...
What about with()? From here,
with(data, expr)
data is the data to use for constructing an environment. For the default with method this may be an environment, a list, a data frame, or an integer.
expr is the expression to evaluate.
with is a generic function that evaluates expr in a local environment constructed from data. The environment has the caller's environment as its parent. This is useful for simplifying calls to modeling functions. (Note: if data is already an environment then this is used with its existing parent.)
Note that assignments within expr take place in the constructed environment and not in the user's workspace.
with() returns value of the evaluated expr.
ee <- list2env(list(x=1,y=2))
with(ee, {
x <- x+1
y <- y+2
z <- 6
})

R functions counting elements

I would like to print the element number of the list that is going through a function. For example if there are 10 elements in the list, I would like a counter that will go from 0-10 as the function goes through each element.
a = length(url)
func0 = function(url){
a = a-1
print(a)
}
cc = lapply(url, func0)
However this does not work. Please let me know what I'm doing wrong.
Your function changes the internal copy of variable a, and prints number 9 10 times. To change this behaviour to desired you should change the assignment operator = (btw, why not <-? equal sign is usually used in definitions of functions' parameters) to the global assignment operator <<-.
func0 = function(url){
a <<- a-1
print(a)
}
It will work, but the common recommendation is to avoid the global assignment operator in your code.
As an alternative I can suggest to check the package pbapply, which adds progress bar to the *apply functions.
require(pbapply)
pblapply(url, func1)
Where func1 will stand for the the function you want to apply to each element of the list.

Assign to list index in specific environment using `get`

If I make an environment with a list in it, and want to assign values to that list, why does the following fail when using get and assign?
res <- new.env()
res$calls <- vector("list", 100)
res$counter <- 1
## works fine
res$calls[[1]] <- 1
## Fails, why?
get("calls", envir=res)[[get("counter", envir=res)]] <- 2
## doesnt make the assignment
val <- get("calls", envir=res)[[get("counter", envir=res)]]
assign("val", 2, envir=res)
I think the following will address your issue:
get("calls", envir=res)[[get("counter", envir=res)]] <- 2 fails because get is not a replacement function. On the other hand res$calls[[1]] <- 1 is actually a replacement function which you can see if you type help('[[<-'). This is the function used when you make an assignment. I think the reason why get has no replacement counterpart i.e. (get<-) is that there is a specific function to do this, which is called assign (as per #TheTime 's comment).
For the second case val <- get("calls", envir=res)[[get("counter", envir=res)]] is created in the global environment. When you use assign("val", 2, envir=res) a res$val variable is created inside the res environment which you can see below:
> res$val
[1] 2
However, val remains the same on the global environment as 1:
> val
[1] 1
So, You probably won't be able to do the assignment with either get or assign. get won't allow it because it is not a replacement function and ?assign mentions:
assign does not dispatch assignment methods, so it cannot be used to set elements of vectors, names, attributes, etc.
So, you can just use the normal [[<- assignment method. #Frank in the comments provides a nice way like:
res[[ "calls" ]][[ res[["counter"]] ]] <- 2

Changing the functional/global environment in R

Is there any way to change the set the working environment or workspace in R to a defined environment? I'd like to call the variables in an environment without constantly referring to the environment. I understand that the attach function can be used to access the variable in this manner, but any variables created don't get placed back in the attached environment. The goal is to have all the functions take place in the other environment.
For example:
original.env <- .GlobalEnv
other.env <- new.env()
other.env$A <- 12; other.env$B <- 1.5
set.env(other.env)
C <- A + B
set.env(original.env)
other.env$C
[1] 13.5
It's the step with set.env which I can't figure out if it exists, or there's some other trick to doing this.
The goal of this approach is to allow the same code to be used on different data sets with identical structures in several non-nested environments without constantly calling other environments with the Environment$ prefix, which gets really verbose in many cases.
If the results can also be assigned back to the set environment (as in the global environment, everything has an implicit .GlobalEnv$ in front of any variable) it would make it way easier to access and return multiple values from inside of a function.
Any help is appreciated. Thanks.
You are looking for eval / evalq
From help("evalq")
Evaluate an R expression in a specified environment.
Specifically, evalq noting
The evalq form is equivalent to eval(quote(expr), ...). eval evaluates its first argument in the current scope before passing it to the evaluator: evalq avoids this.
# Therefore you want something like this
evalq(C <- A + B, envir = other.env)
If you want to wrap more than one expression use {} eg
evalq({C <-A + B
d <- 5
}, envir = other.env)
How about using with?
original.env <- .GlobalEnv
other.env <- new.env()
other.env$A <- 12; other.env$B <- 1.5
with(other.env, { C <- A + B ; FOO <- 'bar' })
other.env$C
[1] 13.5
other.env$FOO
[1] "bar"

Retrieve/access dynamic variable from R function

I have a function in R that structures my raw data. I create a dataframe called output and then want to make a dynamic variable name depending on the function value block.
The output object does contain a dataframe as I want, and to rename it dynamically, at the end of the function I do this (within the function):
a = assign(paste("output", block, sep=""), output)
... but after running the function there is no object output1 (if block = 1). I simply cannot retrieve the output object, neither merely output nor the dynamic output1 version.
I tried this then:
a = assign(paste("output", block, sep=""), output)
return(a)
... but still - no success.
How can I retrieve the dynamic output variable? Where is my mistake?
Environments.
assign will by default create a variable in the environment in which it's called. Read about environments here: http://adv-r.had.co.nz/Environments.html
I assume you're doing something like:
foo <- function(x){ assign("b", x); b}
If you run foo(5), you'll see it returns 5 as expected (implying that b was created successfully somewhere), but b won't exist in your current environment.
If, however, you do something like this
foo <- function(x){ assign("b", x, envir=parent.frame()); b}
Here, you're assigning not to the current environment at the time assign is called (which happens to be foo's environment). Instead, you're assigning into the parent environment (which, since you're calling this function directly, will be your environment).
All this complexity should reveal to you that this will be fairly complex, a nightmare to maintain, and a really bad idea from a maintenance perspective. You'd surely be better off with something like:
foo <- function(x) { return(x) };
b <- foo(5)
Or if you need multiple items returned:
foo <- function(x) { return(list(df=data.frame(col1=x), b=x)) }
results <- foo(5)
df <- results$df
b <- results$b
But ours is not to reason why...

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