README.md

In vfunc: Manipulate Virtual Functions

The vfunc package: adding two functions in R

Overview

In mathematics, given two functions , it is natural to define as the function that maps to . However, in base R, objects of class function do not have arithmetic methods defined, so idiom such as f + g returns an error, even though it has a perfectly reasonable expectation. The vfunc package offers this functionality. Other similar features are provided, which lead to compact and readable idiom. A wide class of coding bugs is eliminated.

Consider the following R session:

f <- function(x){x^2}
g <- function(x){1/(1-x)}
f + g
#> Error in f + g: non-numeric argument to binary operator

Above, there is a reasonably clear expectation for f + g: it should give a function that returns the sum of f() and g(); something like function(x){f(x) + g(x)}. However, it returns an error because f and g are objects of S4 class function, which do not have an addition method. The vfunc package allows us to do this.

The package in use

The package is designed so that objects of class vf operate as functions but are subject to arithmetic operations, which are executed transparently. For example:

library("vfunc")
#> 
#> Attaching package: 'vfunc'
#> The following object is masked from 'package:stats':
#> 
#>     Gamma
f <- as.vf(f)
g <- as.vf(g)
(f + g)(1:10)
#>  [1]      Inf  3.00000  8.50000 15.66667 24.75000 35.80000 48.83333 63.85714
#>  [9] 80.87500 99.88889

Further, functions may be combined arithmetically:

(f + 4*g - f*g)(1:10)
#>  [1]       NaN   4.00000  11.50000  20.00000  30.25000  42.40000  56.50000
#>  [8]  72.57143  90.62500 110.66667

or compositionally:

(f(g) + g(f))(1:10)
#>  [1]         Inf 0.666666667 0.125000000 0.044444444 0.020833333 0.011428571
#>  [7] 0.006944444 0.004535147 0.003125000 0.002244669

The advantages of such idiom fall in to two main categories. Firstly, code can become considerably more compact; and secondly one can guard against a wide class of hard-to-find bugs. Now consider f() and g() to be trivariate functions, each taking three arguments, say,

f <- function(x,y,z){x + x*y - x/z}
g <- function(x,y,z){x^2 - z}

and , , . Given this, we wish to calculate

How would one code up such an expression in R? The standard way would be

 x <- 1.2
 y <- 1.7
 z <- 4.3       
(f(x,y,z) + g(x,y,z))*(f(x,y,z) + 4 - 2*f(x,y,z)*g(x,y,z))
#> [1] 2.411975

Note the repeated specification of argument list (x,y,z), repeated here five times. Now use the vfunc package:

f <- as.vf(f)
g <- as.vf(g)
((f + g)*(f + 4 - 2*f*g))(x,y,z)
#> [1] 2.411975

See how the package allows one to ‘’factorize’’ the argument list so it appears once, leading to more compact code. It is also arguably less error-prone, as the following example illustrates. Consider

(such expressions arise in the study of dynamical systems). Note that functions and are to be evaluated with two distinct sets of arguments at different levels of nesting, namely at the inner level and at the outer. Standard R idiom would be

f(x + z, y + z, f(x, x, y) - g(x, x, y)) + g(x + z, y + z, f(x, x, y) - g(x, x, y))
#> [1] 64.04918

The author can attest that finding bugs in such expressions can be difficult [it is easy to mistype (x,x,y) in one of its occurrences, yet difficult to detect the error]. However, vfunc idiom would be

(f + g)(x + z, y + z, (f - g)(x, x, y))
#> [1] 64.04918

which is certainly shorter, arguably neater and at least the author finds such constructions considerably less error-prone. In this form, one can be sure that both f() and g() are called with identical arguments at each of the two levels in the expression, as the arguments appear only once.

The package includes functions such as Sin() which is a vf equivalent to base::sin(). This allows one to define composite functions such as

j <- as.vf(function(x,y){Cos(x) + Sin(x-y)})
k <- as.vf(function(x,y){Tan(x) + Log(x+y)})
l <- as.vf(function(x,y){Sin(x/2) + x^2   })

(note that functions j(), k() and l() are bivariate). Then compare

(j + k + l)(Sin + Log, Cos + Exp)(Sin + Tan)(0.4)
#> [1] 2.545235

with the one-stage idiom which reads:

j(sin(sin(0.4) + tan(0.4)) + log(sin(0.4) + tan(0.4)), cos(sin(0.4) + tan(0.4)) +
exp(sin(0.4) + tan(0.4))) + k(sin(sin(0.4) + tan(0.4)) + log(sin(0.4) + tan(0.4)),
cos(sin(0.4) + tan(0.4)) + exp(sin(0.4) + tan(0.4)))+ l(sin(sin(0.4) + tan(0.4)) +
log(sin(0.4) + tan(0.4)), cos(sin(0.4) + tan(0.4)) + exp(sin(0.4) + tan(0.4)))
#> [1] 2.545235

and the multi-stage idiom:

A <- function(x,y){j(x,y) + k(x,y) + l(x,y)}
B <- function(x){sin(x) + log(x)}
C <- function(x){cos(x) + exp(x)}
D <- function(x){sin(x) + tan(x)}
x <- 0.4
A(B(D(x)), C(D(x)))
#> [1] 2.545235

See how the one-stage idiom is very long, and the multi-stage idiom is opaque [and nevertheless has repeated instances of (x,y) and x].

Conclusions

The vfunc package allows functions to be ‘’factorized’’, that is, f(x) + g(x) to be re-written (f + g)(x). This allows for concise idiom and eliminates a certain class of coding errors. The package also allows for recursive application of such ideas.

Further information

For more detail, see the package vignette

vignette("vfunc")

vfunc documentation built on Aug. 8, 2025, 6:20 p.m.