Difference Between <- and <<-

What is the difference between assign() and - in R?

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.

Difference between - and -

The operator <<- is the parent scope assignment operator. It is used to make assignments to variables in the nearest parent scope to the scope in which it is evaluated. These assignments therefore "stick" in the scope outside of function calls. Consider the following code:

fun1 <- function() {
    x <- 10
    print(x)
}

> x <- 5    # x is defined in the outer (global) scope
> fun1()
[1] 10      # x was assigned to 10 in fun1()
> x
[1] 5       # but the global value of x is unchanged

In the function fun1(), a local variable x is assigned to the value 10, but in the global scope the value of x is not changed. Now consider rewriting the function to use the parent scope assignment operator:

fun2 <- function() {
    x <<- 10
    print(x)
}

> x <- 5
> fun2()
[1] 10      # x was assigned to 10 in fun2()
> x
[1] 10      # the global value of x changed to 10

Because the function fun2() uses the <<- operator, the assignment of x "sticks" after the function has finished evaluating. What R actually does is to go through all scopes outside fun2() and look for the first scope containing a variable called x. In this case, the only scope outside of fun2() is the global scope, so it makes the assignment there.

As a few have already commented, the <<- operator is frowned upon by many because it can break the encapsulation of your R scripts. If we view an R function as an isolated piece of functionality, then it should not be allowed to interfere with the state of the code which calls it. Abusing the <<- assignment operator runs the risk of doing just this.

Is there a technical difference between = and -

Yes there is. This is what the help page of '=' says:

The operators <- and = assign into the
environment in which they are
evaluated. The operator <- can be used
anywhere, whereas the operator = is
only allowed at the top level (e.g.,
in the complete expression typed at
the command prompt) or as one of the
subexpressions in a braced list of
expressions.

With "can be used" the help file means assigning an object here. In a function call you can't assign an object with = because = means assigning arguments there.

Basically, if you use <- then you assign a variable that you will be able to use in your current environment. For example, consider:

matrix(1,nrow=2)

This just makes a 2 row matrix. Now consider:

matrix(1,nrow<-2)

This also gives you a two row matrix, but now we also have an object called nrow which evaluates to 2! What happened is that in the second use we didn't assign the argument nrow 2, we assigned an object nrow 2 and send that to the second argument of matrix, which happens to be nrow.

Edit:

As for the edited questions. Both are the same. The use of = or <- can cause a lot of discussion as to which one is best. Many style guides advocate <- and I agree with that, but do keep spaces around <- assignments or they can become quite hard to interpret. If you don't use spaces (you should, except on twitter), I prefer =, and never use ->!

But really it doesn't matter what you use as long as you are consistent in your choice. Using = on one line and <- on the next results in very ugly code.

What's the difference between `=` and `-` in R?

From here:

The operators <- and = assign into the environment in which they are evaluated. The operator <- can be used anywhere, whereas the operator = is only allowed at the top level (e.g., in the complete expression typed at the command prompt) or as one of the subexpressions in a braced list of expressions.

What are the differences between = and - assignment operators?

What are the differences between the assignment operators = and <- in R?

As your example shows, = and <- have slightly different operator precedence (which determines the order of evaluation when they are mixed in the same expression). In fact, ?Syntax in R gives the following operator precedence table, from highest to lowest:

…
‘-> ->>’           rightwards assignment
‘<- <<-’           assignment (right to left)
‘=’                assignment (right to left)
…

But is this the only difference?

Since you were asking about the assignment operators: yes, that is the only difference. However, you would be forgiven for believing otherwise. Even the R documentation of ?assignOps claims that there are more differences:

The operator <- can be used anywhere,
whereas the operator = is only allowed at the top level (e.g.,
in the complete expression typed at the command prompt) or as one
of the subexpressions in a braced list of expressions.

Let’s not put too fine a point on it: the R documentation is wrong. This is easy to show: we just need to find a counter-example of the = operator that isn’t (a) at the top level, nor (b) a subexpression in a braced list of expressions (i.e. {…; …}). — Without further ado:

x
# Error: object 'x' not found
sum((x = 1), 2)
# [1] 3
x
# [1] 1

Clearly we’ve performed an assignment, using =, outside of contexts (a) and (b). So, why has the documentation of a core R language feature been wrong for decades?

It’s because in R’s syntax the symbol = has two distinct meanings that get routinely conflated (even by experts, including in the documentation cited above):

The first meaning is as an assignment operator. This is all we’ve talked about so far.
The second meaning isn’t an operator but rather a syntax token that signals named argument passing in a function call. Unlike the = operator it performs no action at runtime, it merely changes the way an expression is parsed.

So how does R decide whether a given usage of = refers to the operator or to named argument passing? Let’s see.

In any piece of code of the general form …

‹function_name›(‹argname› = ‹value›, …)
‹function_name›(‹args›, ‹argname› = ‹value›, …)

… the = is the token that defines named argument passing: it is not the assignment operator. Furthermore, = is entirely forbidden in some syntactic contexts:

if (‹var› = ‹value›) …
while (‹var› = ‹value›) …
for (‹var› = ‹value› in ‹value2›) …
for (‹var1› in ‹var2› = ‹value›) …

Any of these will raise an error “unexpected '=' in ‹bla›”.

In any other context, = refers to the assignment operator call. In particular, merely putting parentheses around the subexpression makes any of the above (a) valid, and (b) an assignment. For instance, the following performs assignment:

median((x = 1 : 10))

But also:

if (! (nf = length(from))) return()

_{Now you might object that such code is atrocious (and you may be right). But I took this code from the base::file.copy function (replacing <- with =) — it’s a pervasive pattern in much of the core R codebase.}

The original explanation by John Chambers, which the the R documentation is probably based on, actually explains this correctly:

[= assignment is] allowed in only two places in the grammar: at the top level (as a complete program or user-typed expression); and when isolated from surrounding logical structure, by braces or an extra pair of parentheses.

In sum, by default the operators <- and = do the same thing. But either of them can be overridden separately to change its behaviour. By contrast, <- and -> (left-to-right assignment), though syntactically distinct, always call the same function. Overriding one also overrides the other. Knowing this is rarely practical but it can be used for some fun shenanigans.

How do you use - (scoping assignment) in R?

<<- 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

Shiny: what is the difference between observeEvent and eventReactive?

As @daatali is saying the two functions are used for different purposes.

ui <- shinyUI(pageWithSidebar(
  headerPanel("eventReactive and observeEvent"),
  sidebarPanel(
    actionButton("evReactiveButton", "eventReactive"),
    br(),
    actionButton("obsEventButton", "observeEvent"),
    br(),
    actionButton("evReactiveButton2", "eventReactive2")
  ),
  mainPanel(
    verbatimTextOutput("eText"),
    verbatimTextOutput("oText")
  )
))

server <- shinyServer(function(input, output) {
  etext <- eventReactive(input$evReactiveButton, {
    runif(1)
  })
  observeEvent(input$obsEventButton,{
    output$oText <- renderText({ runif(1) })
  })
  eventReactive(input$evReactiveButton2,{
    print("Will not print")
    output$oText <- renderText({ runif(1) })
  })
  output$eText <- renderText({
    etext()
  })
})

shinyApp(ui=ui,server=server)

eventReactive creates a reactive value that changes based on the eventExpr while observeEvent simply is triggered based on eventExpr

The difference between bracket [ ] and double bracket [[ ]] for accessing the elements of a list or dataframe

The R Language Definition is handy for answering these types of questions:

http://cran.r-project.org/doc/manuals/R-lang.html#Indexing

R has three basic indexing operators, with syntax displayed by the following examples
    x[i]
    x[i, j]
    x[[i]]
    x[[i, j]]
    x$a
    x$"a"
For vectors and matrices the [[ forms are rarely used, although they have some slight semantic differences from the [ form (e.g. it drops any names or dimnames attribute, and that partial matching is used for character indices). When indexing multi-dimensional structures with a single index, x[[i]] or x[i] will return the ith sequential element of x.

For lists, one generally uses [[ to select any single element, whereas [ returns a list of the selected elements.

The [[ form allows only a single element to be selected using integer or character indices, whereas [ allows indexing by vectors. Note though that for a list, the index can be a vector and each element of the vector is applied in turn to the list, the selected component, the selected component of that component, and so on. The result is still a single element.