I am pleased to announce the release of curry – a small package I’ve developed as part of improving my meta-programming skills. curry is yet another attempt at providing a native currying/partial application mechanism in R. Other examples of implementations of this can be found in purrr and functional (and probably others). curry sets itself apart in the manner it is used and in the functions it creates. curry is operator based and a partially applied function retains named arguments for easier autocomplete etc. curry provides three mechanisms for partial application: %<% (curry()), %-<% (tail_curry()), and %><% (partial()), as well as a true currying operator (%<!%) and a “weak partial application” (%<?%) - read on to see the differences.

Usage

Below I’ll show the different usages of the curry operators

Currying

Currying is the reduction of the arity of a function by fixing the first argument, returning a new function lacking this (no it’s not - read on or click here).

# Equivalent to curry(`+`, 5)
add_5 <- `+` %<% 5
add_5(10)

#> [1] 15

# ellipsis are retained when currying
bind_5 <- cbind %<% 5
bind_5(1:10)

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

Tail currying

Tail currying is just like currying except it reduces the arity of the function from the other end by fixing the last argument.

# Equivalent to tail_curry(`/`, 5)
divide_by_5 <- `/` %-<% 5
divide_by_5(10)

#> [1] 2

no_factors <- data.frame %-<% FALSE
df <- no_factors(x = letters[1:5])
class(df$x)

#> [1] "character"

Partial function application

When the argument you wish to fix is not in either end of the argument list it is necessary to use a more generalised approach. Using %><% (or partial()) it is possible to fix any (and multiple) arguments in a function using a list of values to fix.

dummy_lengths <- vapply %><% list(FUN = length, FUN.VALUE = integer(1))
test_list <- list(a = 1:5, b = 1:10)
dummy_lengths(test_list)

#>  a  b 
#>  5 10

Other efforts in this has the drawback of returning a new function with just an ellipsis, making argument checks and autocomplete impossible. With curry the returned functions retains named arguments (minus the fixed ones).

args(no_factors)

#> function (..., row.names = NULL, check.rows = FALSE, check.names = TRUE, 
#>     fix.empty.names = TRUE) 
#> NULL

args(dummy_lengths)

#> function (X, ..., USE.NAMES = TRUE) 
#> NULL

Real currying

The above uses a very loose (incorrect) definition of currying. The correct definition is that currying a function returns a function taking a single argument (the first from the original function). Calling a curried function will return a new function accepting the second argument of the original function and so on. Once all arguments of the original function has been consumed it evaluates the call and returns the result. Thus:

# Correct currying
foo(arg1)(arg2)(arg3)

# Incorrect currying
foo(arg1)(arg2, arg3)

True currying is less useful in R as it does not play nice with function containing ... as the argument list will never be consumed. Still, it is available in curry using the Curry() function or the %<!% operator:

testfun <- function(x = 10, y, z) {
  x + y + z
}
curriedfun <- Curry(testfun)
curriedfun(1)(2)(3)

#> [1] 6

# Using the operator
testfun %<!% 1 %<!% 2 %<!% 3

#> [1] 6

# The strict operator is only required for the first call
testfun %<!% 1 %<% 2 %<% 3

#> [1] 6

As with the partial application functionality the strict currying retains argument names and defaults at each step of the currying sequence:

args(curriedfun)

#> function (x = 10) 
#> NULL

Weak partial function application

The last functionality provided by curry is a “weak” partial function application in the sense that it sets (or changes) argument defaults. Thus, compared to partial application it returns a function with the same arguments, but if the defaulted arguments are ignored it will be equivalent to a partial application. Defaults can be set or changed using the set_defaults() function or the %<?% operator:

testfun <- function(x = 1, y = 2, z = 3) {
  x + y + z
}
testfun()

#> [1] 6

testfun2 <- testfun %<?% list(y = 10)
testfun3 <- testfun %><% list(y = 10)
testfun2()

#> [1] 14

testfun3()

#> [1] 14

testfun2(y = 20)

#> [1] 24

testfun3(y = 20)

#> Error in testfun3(y = 20): unused argument (y = 20)

Installation

curry can be installed from CRAN:

install.packages('curry')

but for the latest and greatest use GitHub:

if (!require(devtools)) {
    install.packages(devtools)
}
devtools::install_github('thomasp85/curry')

Performance

curry adds a layer around the manipulated functions adding some overhead, but the overhead is not accumulative (currying or partially applying multiple times does not add additional overhead). depending on the runtime of the function the overhead can seem large but as the complexity of the call increases, the effect of the overhead will decrease:

library(microbenchmark)
meanP <- mean %><% list(na.rm = TRUE)

data <-  sample(1000)
microbenchmark(mean(data, na.rm = TRUE), meanP(data))

#> Unit: microseconds
#>                      expr    min      lq     mean  median     uq      max
#>  mean(data, na.rm = TRUE) 16.039 25.6585 40.60812 26.2140 26.606 1461.897
#>               meanP(data) 40.969 54.5685 56.56638 55.6465 57.477  104.947
#>  neval
#>    100
#>    100

data <- sample(1e6)
microbenchmark(mean(data, na.rm = TRUE), meanP(data))

#> Unit: milliseconds
#>                      expr      min       lq     mean   median       uq
#>  mean(data, na.rm = TRUE) 10.45805 11.77362 19.93268 12.20861 14.10476
#>               meanP(data) 12.27619 12.87454 23.51132 13.96323 32.44742
#>      max neval
#>  53.6542   100
#>  98.3925   100

Happy coding!