I am a newbie for R, and I am quite confused with the usage of local and global variables in R.
I read some posts on the internet that say if I use =
or <-
I will assign the variable in the current environment, and with <<-
I can access a global variable when inside a function.
However, as I remember in C++ local variables arise whenever you declare a variable inside brackets {}
, so I'm wondering if this is the same for R? Or is it just for functions in R that we have the concept of local variables.
I did a little experiment, which seems to suggest that only brackets are not enough, am I getting anything wrong?
{
x=matrix(1:10,2,5)
}
print(x[2,2])
[1] 4
Variables that are created outside of a function are known as global variables. Global variables can be used by everyone, both inside of functions and outside.
The main difference between Global and local variables is that global variables can be accessed globally in the entire program, whereas local variables can be accessed only within the function or block in which they are defined.
Overview. Global variables in R are variables created outside a given function. A global variable can also be used both inside and outside a function.
So, by using a local variable you decrease the dependencies between your components, i.e. you decrease the complexity of your code. You should only use global variable when you really need to share data, but each variable should always be visible in the smallest scope possible.
Variables declared inside a function are local to that function. For instance:
foo <- function() {
bar <- 1
}
foo()
bar
gives the following error: Error: object 'bar' not found
.
If you want to make bar
a global variable, you should do:
foo <- function() {
bar <<- 1
}
foo()
bar
In this case bar
is accessible from outside the function.
However, unlike C, C++ or many other languages, brackets do not determine the scope of variables. For instance, in the following code snippet:
if (x > 10) {
y <- 0
}
else {
y <- 1
}
y
remains accessible after the if-else
statement.
As you well say, you can also create nested environments. You can have a look at these two links for understanding how to use them:
Here you have a small example:
test.env <- new.env()
assign('var', 100, envir=test.env)
# or simply
test.env$var <- 100
get('var') # var cannot be found since it is not defined in this environment
get('var', envir=test.env) # now it can be found
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