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"
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
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$b
But ours is not to reason why...
I have a function that needs to access a variable in its parent environment (scope from which the function is called). The variable is large in terms of memory so I would prefer not to pass it to by value to the function being called. Is there a standard way of doing this other than declaring the variable in the global scope? For example:
g <- function (a, b) { #do stuff}
f <- function(x) {
y <- 3 #but in my program y is very large
g(x, y)
}
I would like to access y in g(). So something like this:
g <- function (a) { a+y }
f <- function(x) {
y <- 3 #but in my program y is very large
g(x)
}
Is this possible?
Thanks
There is no advantage to "declaring the variable in the global scope" and it may not even be possible in R depending on what you mean by that. You certainly could use the second form. The action that causes duplicate or even triplicate copies of an object is assignment. You will need to describe in more detail what you are trying to illustrate by the code: y <- 3. That would not normally be needed inside a function that merely accessed an object named "y" that was located in an enclosing frame.
Storing variables in a declared environment will sometimes improve efficiency of access, but my understanding is that the efficiency is in terms of improved speed because a hash table is used. One accesses items in an environment in the same manner as one accesses list elements:
> evn <- new.env()
> evn$a <- rnorm(100000)
> ls(evn)
[1] "a"
> length(evn$a)
[1] 100000
The BigMemory project may offer facilities for this:
http://www.bigmemory.org/ .
It and Lumley's biglm may help with the large dataset mentioned in the comments.
The normal approach to writing functions in R (as I understand) is to avoid side-effects and return a value from a function.
contained <- function(x) {
x_squared <- x^2
return(x_squared)
}
In this case, the value computed from the input into the function is returned. But the variable x_squared is not available.
But if you need to violate this basic functional programming tenet (and I'm not sure how serious R is about this issue) and return an object from a function, you have two choices.
escape <- function(x){
x_squared <<- x^2
assign("x_times_x", x*x, envir = .GlobalEnv)
}
Both objects x_squared and x_times_x are returned. Is one method preferable to the other and why so?
Thomas Lumley answers this in a superb post on r-help the other day. <<- is about the enclosing environment so you can do thing like this (and again, I quote his post from April 22 in this thread):
make.accumulator<-function(){
a <- 0
function(x) {
a <<- a + x
a
}
}
> f<-make.accumulator()
> f(1)
[1] 1
> f(1)
[1] 2
> f(11)
[1] 13
> f(11)
[1] 24
This is a legitimate use of <<- as "super-assignment" with lexical scope. And not simply to assign in the global environment. For that, Thomas has these choice words:
The Evil and Wrong use is to modify
variables in the global environment.
Very good advice.
According to the manual page here,
The operators <<- and ->> cause a search to made through the environment for an existing definition of the variable being assigned.
I've never had to do this in practice, but to my mind, assign wins a lot of points for specifying the environment exactly, without even having to think about R's scoping rules. The <<- performs a search through environments and is therefore a little bit harder to interpret.
EDIT: In deference to #Dirk and #Hadley, it sounds like assign is the appropriate way to actually assign to the global environment (when that's what you know you want), while <<- is the appropriate way to "bump up" to a broader scope.
As pointed out by #John in his answer, assign lets you specify the environment specifically. A specific application would be in the following:
testfn <- function(x){
x_squared <- NULL
escape <- function(x){
x_squared <<- x^2
assign("x_times_x", x*x, envir = parent.frame(n = 1))
}
escape(x)
print(x_squared)
print(x_times_x)
}
where we use both <<- and assign. Notice that if you want to use <<- to assign to the environment of the top level function, you need to declare/initialise the variable. However, with assign you can use parent.frame(1) to specify the encapsulating environment.