Convenient way to delegate function calls - r

Question

I have a series of similar functions that all need to extract some values from a data frame. Something like this:
foo_1 <- function(data, ...) {
x <- data$x
y <- data$y
# some preparatory code common to all foo_X functions
# .. do some boring stuff with x and y
# pack and process the result into 'ret'
return(ret)
}
These functions are then provided as arguments to some other function (let us call it "the master function". I cannot modify the master function).
However, I wish I could avoid re-writing the same preparatory code in each of these functions. For example, I don't want to use data$x instead of assigning it to x and using x because it makes the boring stuff hard to read. Presently, I need to write x <- data$x (etc.) in all of the foo_1, foo_2... functions. Which is annoying and clutters the code. Also, packing and processing is common for all the foo_N functions. Other preparatory code includes scaling of variables or regularization of IDs.
What would be an elegant and terse way of doing this?
One possibility is to attach() the data frame (or use with(), as Hong suggested in the answer below), but I don't know what other variables would then be in my name space: attaching data can mask other variables I use in fun_1. Also, preferably the foo_N functions should be called with explicit parameters, so it is easier to see what they need and what they are doing.
Next possibility I was thinking of was a construction like this:
foo_generator <- function(number) {
tocall <- switch(1=foo_1, 2=foo_2, 3=foo_3) # etc.
function(data, ...) {
x <- data$x
y <- data$y
tocall(x, y, ...)
# process and pack into ret
return(ret)
}
foo_1 <- function(x, y, ...) {
# do some boring stuff
}
Then I can use foo_generator(1) instead of foo_1 as the argument for the master function.
Is there a better or more elegant way? I feel like I am overlooking something obvious here.

Answer 1

You might be overthinking it. You say that the code dealing with preparation and packing are common to all foo_n functions. I assume, then, that # .. do some boring stuff with x and y is where each function differs. If that's the case then just create a single prep_and_pack function which takes a function name as a parameter, and then pass in foo_1, foo_2, etc. For example:
prep_and_pack <- function(data, func){
x <- data$x
y <- data$y
# preparatory code here
xy_output <- func(x, y) # do stuff with x and y
# code to pack and process into "ret"
return(ret)
}
Now you can create your foo_n functions that do different things with x and y:
foo_1 <- function(x, y) {
# .. do some boring stuff with x and y
}
foo_2 <- function(x, y) {
# .. do some boring stuff with x and y
}
foo_3 <- function(x, y) {
# .. do some boring stuff with x and y
}
Finally, you can pass multiple calls to prep_and_pack into your master function, where foo_1 etc. are passed in via the func argument:
master_func(prep_and_pack(data = df, func = foo_1),
prep_and_pack(data = df, func = foo_2),
prep_and_pack(data = df, func = foo_3)
)
You could also use switch in prep_and_pack and/or forgo the foo_n functions completely in favor of if-else conditionals to deal with the various cases, but I think the above keeps things nice a clean.

Answer 2

The requirements still seem a bit vague to me,
but if your code is so similar that you can simply wrap it around a helper function like tocall in your example,
and your input is in a list-like structure
(like a data frame which is just a list of columns),
then just write all your foo_* functions to take the "spliced" parameters like in your proposed solution,
and then use do.call:
foo_1 <- function(x, y) {
x + y
}
foo_2 <- function(x, y) {
x - y
}
data <- list(x = 1:2, y = 3:4)
do.call(foo_1, data)
# [1] 4 6
do.call(foo_2, data)
# [1] -2 -2

Answer 3

I'm not sure the following is a good idea. It reminds me a bit of programming with macros. I don't think I would do this. You'd need to carefully document because it is unexpected, confusing and not self-explanatory.
If you want to reuse the same code in different functions, it might be an option to create it as an unevaluated call and evaluate that call in the different functions:
prepcode <- quote({
x <- data$x
y <- data$y
}
)
foo_1 <- function(data, ...) {
eval(prepcode)
# some preparatory code common to all foo_X functions
# .. do some boring stuff with x and y
# pack and process the result into 'ret'
return(list(x, y))
}
L <- list(x = 1, y = "a")
foo_1(L)
#[[1]]
#[1] 1
#
#[[2]]
#[1] "a"
It might be better, to then have prepcode as an argument to foo_1 to make sure there won't be any scoping issues.

Answer 4

Use with inside the function:
foo_1 <- function(data, ...) {
with(data, {
# .. in here, x and y refer to data$x and data$y
}
}

Answer 5

I'm not sure I understand fully, but can't you simply use a function for all common stuff, and then unpack that into the foo_N functions using list2env? For example:
prepFun <- function(data, ...) {
x <- data$x
y <- data$y
tocall(x, y, ...)
# process and pack into a named list called ret
# so you know specifically what the elements of ret are called
return(ret)
}
foo_1 <- function(data, ...) {
# Get prepFun to do the prepping, then use list2env to get the result in foo_1
# You know which are the named elements, so there should be no confusion about
# what does or does not get masked.
prepResult <- prepFun(data, ...)
list2env(prepResult)
# .. do some boring stuff with x and y
# pack and process the result into 'ret'
return(ret)
}
Hope this is what you're looking for!

Answer 6

I think defining a function factory for this task is a bit overkill and confusing. You can define a general function and use purrr::partial() on it when passing it to your master function.
Something like :
foo <- function(data, ..., number, foo_funs) {
tocall <- foo_funs[[number]])
with(data[c("x", "y")], tocall(x, y, ...))
# process and pack into ret
return(ret)
}
foo_1 <- function(x, y, ...) {
# do some boring stuff
}
foo_funs <- list(foo_1, foo_2, ...)
Then call master_fun(fun = purrr::partial(foo, number =1) , ...)

Answer 7

another possibility is to use list2env which saves the components of a list in to a specified environment:
foo_1 <- function(data){
list2env(data, envir = environment())
x + y
}
foo_1(data.frame(x = 1:2, y = 3:4))
See also this question.