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Some Neat New R Notations

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The R package wrapr supplies a few neat new coding notations.

abacus

An Abacus, which gives us the term “calculus.”

The first notation is an operator called the “named map builder”. This is a cute notation that essentially does the job of stats::setNames(). It allows for code such as the following:

library("wrapr")

names <- c('a', 'b')

names := c('x', 'y')
#>   a   b 
#> "x" "y"

This can be very useful when programming in R, as it allows indirection or abstraction on the left-hand side of inline name assignments (unlike c(a = 'x', b = 'y'), where all left-hand-sides are concrete values even if not quoted).

A nifty property of the named map builder is it commutes (in the sense of algebra or category theory) with R‘s “c()” combine/concatenate function. That is: c('a' := 'x', 'b' := 'y') is the same as c('a', 'b') := c('x', 'y'). Roughly this means the two operations play well with each other.

The second notation is an operator called “anonymous function builder“. For technical reasons we use the same “:=” notation for this (and, as is common in R, pick the correct behavior based on runtime types).

The function construction is written as: “variables := { code }” (the braces are required) and the semantics are roughly the same as “function(variables) { code }“. This is derived from some of the work of Konrad Rudolph who noted that most functional languages have a more concise “lambda syntax” than “function(){}” (please see here and here for some details, and be aware the wrapr notation is not as concise as is possible).

This notation allows us to write the squares of 1 through 4 as:

sapply(1:4, x:={x^2})

instead of writing:

sapply(1:4, function(x) x^2)

It is only a few characters of savings, but being able to choose notation can be a big deal. A real victory would be able to directly use lambda-calculus notation such as “(λx.x^2)“. We are also experimenting with the following additional notation:

sapply(1:4, λ(x, x^2))

Edit 2017-08-24: the above functions (including λ), have all been moved from seplyr to wrapr and released on CRAN!

Categories: Tutorials

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jmount

Data Scientist and trainer at Win Vector LLC. One of the authors of Practical Data Science with R.

6 replies ›

  1. Don’t know what that is, but it is not a usable abacus. Seems to be missing some beads. Ought to have 4 on bottom and one on top in each column.

  2. I have improved the λ documentation a bit: link.

    And, the lambda(x)(x^2) form is pretty useless (same form as function(x) x^2, and without the R-language hooks). Mostly I put it in as a place holder so the λ(x, x^2) form has something to cross-link its help to (to prevent generating a help file with complicated character encoding in the help file name). I’ve also changed its syntax to lambda(x, x^2) to make it closer the λ(x, x^2) form.

  3. Are you familiar with the magrittr syntactic sugar . %>% … and . %>% { … }?

    They supply the same functionality as your lamba example above. The only downside being you cannot pass more than one argument. If you expanded := to support multiple arguments on the LHS, similar to the fat-arrow operator (“=>”) in EMACSscript6, that would be a significant improvement.

    1. I have used the magrittr “. %>% f” notation from time to time.

      The wrapr function builders can conveniently take multiple arguments:

      library("wrapr")
f <- c(x,y) := { x + 3 * y }
f(1,2)
#> [1] 7

      library("wrapr")
defineLambda()
f <-  λ(x, y, x + 2*y)
f(2,4)
#> [1] 10

      Or even the original formula interface version of arguments:

      library("wrapr")
f <- x~y := x + 3 * y
f(5, 47)
#> [1] 146

  4. Nice to learn about another approach to anonymous functions. You might be interesting in the [nofrills](https://cran.r-project.org/package=nofrills) package. Compared to wrapr, it is far more specialized—it only concerns anonymous functions—but has some noteworthy differences: uses essentially the same syntax as the normal function declaration (but shorter), supports quasiquotation (the README explains why), and includes an operator that enables higher-order functions to interpret an even shorter function syntax (in the GitHub version).

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