[R] Extracting specific arguments from "..."

[@vi@e@gross m@iii@g oii gm@ii@com]

Like many things in R, the original way things were done may have ossified
in place and even if largely unknown packages came along, may not be known
by many.

The topic John is talking about is NOT in my mind about systems programming
at all. It is about writing any function where you want control over
evaluating arguments. There may be a better way from a programmers
perspective.

I can imagine a set of functions in a package that are well designed and
hide all the details so they can be easily used. I suspect aspects of what I
am talking about have been done. They could include some "logical" functions
that test if an option has been specified, or even if it is just the
default, without evaluating anything. Other functions would return a
specified argument. Yet others would remove a specified argument so further
evaluation does not see it, including removing it from ... so that in the
end, you can pass along a reduced ... to other functions you call.

I understand some R evaluations can be tricky or even have side effects. But
something better than what I have seen seems quite possible.

Other languages have variants such as getopt() that are a tad different but
quite useful.

-----Original Message-----
From: R-help <r-help-bounces using r-project.org> On Behalf Of Sorkin, John
Sent: Tuesday, January 7, 2025 6:54 PM
To: Ben Bolker <bbolker using gmail.com>; r-help using r-project.org
Subject: Re: [R] Extracting specific arguments from "..."

Ben,
As always, thank you.
You are correct, it is something like what I want, but not exactly. Perhaps
someday someone will write a more complete guide.
Thank you,
John


John David Sorkin M.D., Ph.D.
Professor of Medicine, University of Maryland School of Medicine;
Associate Director for Biostatistics and Informatics, Baltimore VA Medical
Center Geriatrics Research, Education, and Clinical Center;
PI Biostatistics and Informatics Core, University of Maryland School of
Medicine Claude D. Pepper Older Americans Independence Center;
Senior Statistician University of Maryland Center for Vascular Research;

Division of Gerontology and Paliative Care,
10 North Greene Street
GRECC (BT/18/GR)
Baltimore, MD 21201-1524
Cell phone 443-418-5382




________________________________________
From: R-help <r-help-bounces using r-project.org> on behalf of Ben Bolker
<bbolker using gmail.com>
Sent: Tuesday, January 7, 2025 5:06 PM
To: r-help using r-project.org
Subject: Re: [R] Extracting specific arguments from "..."


   There's an ancient (2003) document on the CRAN "developers' page"
https://developer.r-project.org/model-fitting-functions.html that is
sort of (but not exactly) what you're looking for ...


On 2025-01-07 5:03 p.m., Sorkin, John wrote:
> Colleagues,
>
> My interest is not in writing ad hoc functions (which I might use once to
analyze my data), but rather what I will call a system function that might
be part of a package. The lm function is a paradigm of what I call a system
function.
>
> The lm function begins by processing the arguments passed to the function
(represented in the function as parameters, see code below.) Much of this
processing is only peripherally related to running a regression, but the
code is necessary to determine exactly what the user of the system function
wants the function to do. It would be helpful if there was a document that
would describe best practices when writing system functions, with clear
explanations of what each step in system function is designed to do and how
the line accomplishes its task. It would also be nice if the system function
had documentation. I have pushed my way through the lm function, and with
the help of R help files, I have come to understand how the function works,
but this is not an efficient way to learn best practices that should be used
when writing a system function.
>
> Perhaps there is a document that does what I would like to see done, but I
do not know of one.
>
> John
>
> lmlm
> function (formula, data, subset, weights, na.action, method = "qr",
>      model = TRUE, x = FALSE, y = FALSE, qr = TRUE, singular.ok = TRUE,
>      contrasts = NULL, offset, ...)
> {
>      ret.x <- x
>      ret.y <- y
>      cl <- match.call()
>      mf <- match.call(expand.dots = FALSE)
>      m <- match(c("formula", "data", "subset", "weights", "na.action",
>          "offset"), names(mf), 0L)
>      mf <- mf[c(1L, m)]
>      mf$drop.unused.levels <- TRUE
>      mf[[1L]] <- quote(stats::model.frame)
>      mf <- eval(mf, parent.frame())
>      if (method == "model.frame")
>          return(mf)
>      else if (method != "qr")
>          warning(gettextf("method = '%s' is not supported. Using 'qr'",
>              method), domain = NA)
>      mt <- attr(mf, "terms")
>      y <- model.response(mf, "numeric")
>      w <- as.vector(model.weights(mf))
>      if (!is.null(w) && !is.numeric(w))
>          stop("'weights' must be a numeric vector")
>      offset <- model.offset(mf)
>      mlm <- is.matrix(y)
>      ny <- if (mlm)
>          nrow(y)
>      else length(y)
>      if (!is.null(offset)) {
>          if (!mlm)
>              offset <- as.vector(offset)
>          if (NROW(offset) != ny)
>              stop(gettextf("number of offsets is %d, should equal %d
(number of observations)",
>                  NROW(offset), ny), domain = NA)
>      }
>      if (is.empty.model(mt)) {
>          x <- NULL
>          z <- list(coefficients = if (mlm) matrix(NA_real_, 0,
>              ncol(y)) else numeric(), residuals = y, fitted.values = 0 *
>              y, weights = w, rank = 0L, df.residual = if (!is.null(w))
sum(w !=
>              0) else ny)
>          if (!is.null(offset)) {
>              z$fitted.values <- offset
>              z$residuals <- y - offset
>          }
>      }
>      else {
>          x <- model.matrix(mt, mf, contrasts)
>          z <- if (is.null(w))
>              lm.fit(x, y, offset = offset, singular.ok = singular.ok,
>                  ...)
>          else lm.wfit(x, y, w, offset = offset, singular.ok = singular.ok,
>              ...)
>      }
>      class(z) <- c(if (mlm) "mlm", "lm")
>      z$na.action <- attr(mf, "na.action")
>      z$offset <- offset
>      z$contrasts <- attr(x, "contrasts")
>      z$xlevels <- .getXlevels(mt, mf)
>      z$call <- cl
>      z$terms <- mt
>      if (model)
>          z$model <- mf
>      if (ret.x)
>          z$x <- x
>      if (ret.y)
>          z$y <- y
>      if (!qr)
>          z$qr <- NULL
>      z
> }
>
>
>
> John David Sorkin M.D., Ph.D.
> Professor of Medicine, University of Maryland School of Medicine;
> Associate Director for Biostatistics and Informatics, Baltimore VA Medical
Center Geriatrics Research, Education, and Clinical Center;
> PI Biostatistics and Informatics Core, University of Maryland School of
Medicine Claude D. Pepper Older Americans Independence Center;
> Senior Statistician University of Maryland Center for Vascular Research;
>
> Division of Gerontology and Paliative Care,
> 10 North Greene Street
> GRECC (BT/18/GR)
> Baltimore, MD 21201-1524
> Cell phone 443-418-5382
>
>
>
>
> ________________________________________
> From: Jorgen Harmse <JHarmse using roku.com>
> Sent: Tuesday, January 7, 2025 1:47 PM
> To: r-help using r-project.org; ikwsimmo using gmail.com; Bert Gunter; Sorkin, John;
jdnewmil using dcn.davis.ca.us
> Subject: Re: Extracting specific arguments from "..."
>
> Interesting discussion. A few things occurred to me.
>
> Apologies to Iris Simmons: I mixed up his answer with Bert's question.
>
> Bert raises questions about promises, and I think they are related to John
Sorkin's question. A big difference between R and most other languages is
that function arguments are computed lazily. match.call & substitute tell us
what expressions will be evaluated if function arguments are needed but not
the environments in which that will happen. The usual suspects are
environment() and parent.frame(), but parent.frame(k) & maybe even other
environments are possible. If you are really determined then I guess you can
keep evaluating match.call() in parent frames until you have accounted for
all the inputs.
>
> It's not clear to what extent John Sorkin is concerned about writing
functions as opposed to using functions. Lazy computation has advantages but
leads to some issues.
> Exactly matching the function's default expression for an input is not
necessarily the same as omitting the input. The evaluation environment is
different.
> If the caller uses an expression with side effects then there is no
guarantee that the side effects will happen. If there are side effects from
two or more inputs then the order is uncertain. (If an argument is not
supplied and the default has side effects then they might not happen either.
However, I don't know why the function writer would specify any side effect
except stop(), and then he or she has probably arranged for it to happen
exactly when it should.)
> If a default value depends on another input and that input is modified
inside the function then order of evaluation of inputs becomes important.
Even if you know exactly what you're doing when you write the function, you
should make it clear to future maintainers. An explicit call to force
clarifies that the input needs to be computed with the existing values of
anything that is used in the default, even if the code is refactored so that
the value is not used immediately. If you really want to modify another
input before evaluating the default then specify that in a comment.
>
> Jeff Newmiller makes a good point. You can still change your mind about
inspecting a particular input without breaking old code that uses your
function, and you don't necessarily need default values.
>
> Old definition: f <- function(.) {<code that passes . to other functions
and does some other things>}
>
> New definition:
> f <- function(., a = <default expression, possibly stop()>)
> { <pass ., a=a to another function>
>    <do something with the output>
> }
>
> OR
>
> f <- function(., a)
> { if (missing(a)) # OK, this becomes clunky if there are several such
inputs
>    { < pass . to another function >}
>    else
>   { <inspect or modify a> # Pitfall: Changing the order of evaluation may
break old code, but then the design was probably too devious in the first
place.
>      <pass ., a=a to another function>
>    }
>    <do something with the output>
> }
>
> Regards,
> Jorgen Harmse.
>
>
>
>
> ______________________________________________
> R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
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--
Dr. Benjamin Bolker
Professor, Mathematics & Statistics and Biology, McMaster University
Director, School of Computational Science and Engineering
 > E-mail is sent at my convenience; I don't expect replies outside of
working hours.

______________________________________________
R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
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PLEASE do read the posting guide
https://www.r-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

______________________________________________
R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide
https://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.