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.mich...@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@df <- x@df[eval(substitute(subset), x@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@nrow <- nrow(ans@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@df <- x@df[eval(substitute(subset), x@df, env),,drop=FALSE] x }) setMethod("subset", "B", function(x, ...) { ans <- callNextMethod() ans@nrow <- nrow(ans@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]] ______________________________________________ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel______________________________________________ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
-- 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-tier...@uiowa.edu Iowa City, IA 52242 WWW: http://www.stat.uiowa.edu
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