Why do we use arrow as an assignment operator?

A Twitter Thread turned into a blog post.

In June, I published a littlethread on Twitter about the history of the <- assignment operator in R. Here is a blog post version of this thread.

Historical reasons

As you all know, R comes from S. But you might not know a lot about S (I don’t). This language used <- as an assignment operator. It’s partly because it was inspired by a language called APL, which also had this sign for assignment.

But why again? APL was designed on a specific keyboard, which had a key for<-:

At that time, it was also chosen because there was no == for testing equality: equality was tested with =, so assigning a variable needed to be done with another symbol.

From APL Reference Manual

Until 2001, in R, =could only be used for assigning function arguments, like fun(foo = "bar") (remember that R was born in 1993). So before 2001, the <- was the standard (and only way) to assign value into a variable.

Before that, _ was also a valid assignment operator. It was removed inR 1.8:

(So no, at that time, no snake_case_naming_convention)

Colin Gillespie published some of his code from early 2000, where assignment was made like this :)

The main reason “equal assignment” was introduced is because other languages uses = as an assignment method, and because it increased compatibility with S-Plus.

And today?

Readability

Nowadays, there are seldom any cases when you can’t use one in place of the other. It’s safe to use = almost everywhere. Yet, <- is preferred and advised in R Coding style guides:

One reason, if not historical, to prefer the <- is that it clearly states in which side you are making the assignment (you can assign from left to right or from right to left in R):

a <- 12
13 -> b 
a

## [1] 12

b

## [1] 13

a -> b
a <- b

The RHS assignment can for example be used for assigning the result of a pipe

library(dplyr)
iris %>%
  filter(Species == "setosa") %>% 
  select(-Species) %>%
  summarise_all(mean) -> res
res

## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 5.006 3.428 1.462 0.246

Also, it’s easier to distinguish equality comparison and assignment in the last line of code here:

c <- 12
d <- 13
e = c == d
f <- c == d

Note that <<- and ->> also exist:

create_plop_pouet <- function(a, b){
  plop <<- a
  b ->> pouet
}
create_plop_pouet(4, 5)
plop

## [1] 4

pouet

## [1] 5

And that Ross Ihaka uses = :https://www.stat.auckland.ac.nz/~ihaka/downloads/JSM-2010.pdf

Environments

There are some environment and precedence differences. For example, assignment with = is only done on a functional level, whereas <-does it on the top level when called inside as a function argument.

median(x = 1:10)

## [1] 5.5

x

## Error in eval(expr, envir, enclos): object 'x' not found

median(x <- 1:10)

## [1] 5.5

x

## [1] 1 2 3 4 5 6 7 8 9 10

In the first code, you’re passing x as the parameter of the medianfunction, whereas the second one is creating a variable x in the environment, and uses it as the first argument of median. Note that it works because x is the name of the parameter of the function, and won’t work with y:

median(y = 12)

## Error in is.factor(x): argument "x" is missing, with no default

median(y <- 12)

## [1] 12

There is also a difference in parsing when it comes to both these operators (but I guess this never happens in the real world), one failing and not the other:

x <- y = 15

## Error in x <- y = 15: could not find function "<-<-"

x = y <- 15
c(x, y)

## [1] 15 15

It is also good practice because it clearly indicates the difference between function arguments and assignation:

x <- shapiro.test(x = iris$Sepal.Length)
x

## 
## Shapiro-Wilk normality test
## 
## data: iris$Sepal.Length
## W = 0.97609, p-value = 0.01018

And this weird behavior:

rm(list = ls())
data.frame(
  a = rnorm(10),
  b <- rnorm(10)
)

## a b....rnorm.10.
## 1 0.6457433 -0.5001296
## 2 0.2073077 -0.4575013
## 3 -0.4758076 -0.2820372
## 4 0.2568369 -0.4271579
## 5 0.4775034 -1.8024830
## 6 0.9281543 -0.2811589
## 7 0.3622706 -1.5172742
## 8 0.5093346 -1.9805609
## 9 -1.7333491 0.5559907
## 10 -2.0203632 1.9717890

a

## Error in eval(expr, envir, enclos): object 'a' not found

b

## [1] -0.5001296 -0.4575013 -0.2820372 -0.4271579 -1.8024830 -0.2811589
## [7] -1.5172742 -1.9805609 0.5559907 1.9717890

Little bit unrelated but

I love this one:

g <- 12 -> h
g

## [1] 12

h

## [1] 12

Which of course is not doable with =.

Other operators

Some users pointed out on Twitter that this could make the code a little bit harder to read if you come from another language. <- is use “only” use in F#, OCaml, R and S (as far as Wikipedia can tell). Even if <-is rare in programming, I guess its meaning is quite easy to grasp, though.

Note that the second most used assignment operator is := (= being the most common). It’s used in {data.table} and {rlang} notably. The:= operator is not defined in the current R language, but has not been removed, and is still understood by the R parser. You can’t use it on the top level:

a := 12

## Error in `:=`(a, 12): could not find function ":="

But as it is still understood by the parser, you can use := as an infix without any %%, for assignment, or for anything else:

`:=` <- function(x, y){
  x$y <- NULL
  x
}
head(iris := Sepal.Length)

## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa

You can see that := was used as an assignment operatorhttps://developer.r-project.org/equalAssign.html :