[Rd] getting environment from "top" promise

[luke-tierney at uiowa.edu]

On Tue, 11 Feb 2014, Romain Francois wrote:

> Hello,
>
> We have something very similar to your while loop in dplyr.
> https://github.com/hadley/dplyr/blob/02a609310184d003c2ae9e0c013bfa69fa4d257a/inst/include/tools/DataDots.h#L15
>
> because we need to know exactly in which environment a promise is supposed to be evaluated, even though we might combine standard R evaluation with an alternative faster engine. this is the basis of what we called hybrid evaluation.
>
>
> For future work, I also have the while loop in the Promise class in Rcpp11, so that when you create a Promise in Rcpp11, its .environment() method gives you what you expect.
> https://github.com/romainfrancois/Rcpp11/blob/master/inst/include/Rcpp/Promise.h#L14
>
> So, this is something I find useful, although I’m not sure we are supposed to mess with promises.

No you are not :-)

Promises are an internal mechanism for implementing lazy
evaluation. They are convenient but also very inefficient, so they may
very well go away when a better approach becomes available.  What will
not go away is the functionality they provide -- bindings with
deferred evaluations, an expression/code for the evaluation, and an
environment (until the evaluation happens). If you build on those
concepts you will be more future proof than if you assume there will
be an internal promise object.

Best,

luke

>
> Romain
>
> Le 11 févr. 2014 à 19:02, Michael Lawrence <lawrence.michael at gene.com> a écrit :
>
>> Hi all,
>>
>> It seems that there is a use case for obtaining the environment for the
>> "top" promise. By "top", I mean following the promise chain up the call
>> stack until hitting a non-promise.
>>
>> S4 data containers often mimic the API of base R data structures. This
>> means writing S4 methods for functions that quote their arguments, like
>> with() and subset(). The methods package directly forwards any arguments
>> not used for dispatch, so substitute(subset) is able to resolve the
>> original quoted argument (this is not the case for naively written
>> wrappers).  The problem then becomes figuring out the environment in which
>> to evaluate the expression.
>>
>> Consider:
>>
>> setClass("A", representation(df = "data.frame"))
>>
>> setMethod("subset", "A", function(x, subset) {
>>  env <- parent.frame(2)
>>  x at df <- x at df[eval(substitute(subset), x at df, env),,drop=FALSE]
>>  x
>> })
>>
>> dropLowMpg <- function(x, cutoff=20) {
>>  invisible(subset(x, mpg > cutoff))
>> }
>>
>> a <- new("A", df=mtcars)
>> dropLowMpg(a)
>>
>> The above works just fine, because we figured out that we need to evaluate
>> in the grand-parent frame to avoid the frame of the generic call. But now
>> let's assume A has a subclass B, and subset,B delegates to subset,A via
>> callNextMethod(). The call stack is different, and our assumption is
>> invalid.
>>
>> setClass("B", representation(nrow="integer"), contains="A")
>> setMethod("subset", "B", function(x, ...) {
>>  ans <- callNextMethod()
>>  ans at nrow <- nrow(ans at df)
>>  ans
>> })
>> b <- new("B", df=mtcars)
>> dropLowMpg(b)
>> Error in eval(expr, envir, enclos) (from #3) : object 'cutoff' not found
>>
>> We can fix this with a simple C function:
>> SEXP top_prenv(SEXP nm, SEXP env)
>> {
>>  SEXP promise = findVar(nm, env);
>>  while(TYPEOF(promise) == PROMSXP) {
>>    env = PRENV(promise);
>>    promise = PREXPR(promise);
>>  }
>>  return env;
>> }
>>
>> With R wrapper:
>> top_prenv <- function(x) {
>>  .Call2("top_prenv", substitute(x), parent.frame())
>> }
>>
>> Then this works (need to set subset,B again to reset cache):
>>
>> setMethod("subset", "A", function(x, subset) {
>>  env <- top_prenv(subset)
>>  x at df <- x at df[eval(substitute(subset), x at df, env),,drop=FALSE]
>>  x
>> })
>> setMethod("subset", "B", function(x, ...) {
>>  ans <- callNextMethod()
>>  ans at nrow <- nrow(ans at df)
>>  ans
>> })
>>
>> b <- new("B", df=mtcars)
>> dropLowMpg(b)
>>
>> Would this be a useful addition to R? Is there a better way to solve this
>> issue? We're using this successfully in the IRanges package now, but we'd
>> like to avoid dealing with the internal details of R, and this is something
>> that could be of general benefit.
>>
>> Thanks,
>> Michael
>>
>> 	[[alternative HTML version deleted]]
>>
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>
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-- 
Luke Tierney
Chair, Statistics and Actuarial Science
Ralph E. Wareham Professor of Mathematical Sciences
University of Iowa                  Phone:             319-335-3386
Department of Statistics and        Fax:               319-335-3017
    Actuarial Science
241 Schaeffer Hall                  email:   luke-tierney at uiowa.edu
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