CRAN policies
It looks like you define a few functions that use substitute() or sys.call()
or similar functions to look at the unevaluated argument list. E.g.,
"cq" <-
function( ...) {
# Saves putting in quotes!
# E.G.: quoted( first, second, third) is the same as c( 'first', 'second', 'third')
# wrapping by as.character means cq() returns character(0) not list()
as.character( sapply( as.list( match.call( expand.dots=TRUE))[-1], as.character))
}
%such.that% and %SUCH.THAT% do similar things.
Almost all the complaints from check involve calls to a handful of such
functions. If you could tell codetools:::checkUsage that that these functions
did nonstandard evaluation on all or some of their arguments then the
complaints would go away and other checks for real errors like misspellings
would still be done.
Another possible part of the problem is that if checkUsage is checking a function like
f <- function(x) paste(x, cq(suffix), sep=".")
it attributes the out-of-scope suffix problem to 'f' and doesn't mention that the immediate
caller is 'cq', so you cannot easily filter output complaints about cq. (CRAN would
not do such filtering, but a developer might.)
Bill Dunlap
Spotfire, TIBCO Software
wdunlap tibco.com
-----Original Message----- From: r-devel-bounces at r-project.org [mailto:r-devel-bounces at r-project.org] On Behalf Of Mark.Bravington at csiro.au Sent: Thursday, March 29, 2012 6:30 PM Cc: r-devel at stat.math.ethz.ch Subject: Re: [Rd] CRAN policies I'm concerned this thread is heading the wrong way, towards techno-fixes for imaginary problems. R package-building is already encumbered with a huge set of complicated rules, and more instructions/rules eg for metadata would make things worse not better. RCMD CHECK on the 'mvbutils' package generates over 300 Notes about "no visible binding...", which inevitably I just ignore. They arise because RCMD CHECK is too "stupid" to understand one of my preferred coding idioms (I'm not going to explain what-- that's beside the point). And RCMD CHECK always will be too "stupid" to understand everything that a rich language like R might quite reasonably cause experienced coders to do. It should not be CRAN's business how I write my code, or even whether my code does what it is supposed to. It might be CRAN's business to try to work out whether my code breaks CRAN's policies, eg by causing R to crash horribly-- that's presumably what Warnings are for (but see below). And maybe there could be circumstances where an automatic check might be "worried" enough to alert the CRANia and require manual explanation and emails etc from a developer, but even that seems doomed given the growing deluge of packages. RCMD CHECK currently functions both as a "sanitizer" for CRAN, and as a developer-tool. But the fact that the one programl does both things seems accidental to me, and I think this dual-use is muddying the discussion. There's a big distinction between (i) code-checks that developers themselves might or might not find useful-- which should be left to the developer, and will vary from person to person-- and (ii) code-checks that CRAN enforces for its own peace-of-mind. Maybe it's convenient to have both functions in the same place, and it'd be fine to use Notes for one and Warnings for the other, but the different purposes should surely be kept clear. Personally, in building over 10 packages (only 2 on CRAN), I haven't found RCMD CHECK to be of any use, except for the code-documentation and example-running bits. I know other people have different opinions, but that's the point: one-size-does-not-fit-all when it comes to coding tools. And wrto the Warnings themselves: I feel compelled to point out that it's logically impossible to fully check whether R code will do bad things. One has to wonder at what point adding new checks becomes futile or counterproductive. There must be over 2000 people who have written CRAN packages by now; every extra check and non-back- compatible additional requirement runs the risk of generating false-negatives and incurring many extra person-hours to "fix" non-problems. Plus someone needs to document and explain the check (adding to the rule mountain), plus there is the time spent in discussions like this..! Mark Mark Bravington CSIRO CMIS Marine Lab Hobart Australia
________________________________________
From: r-devel-bounces at r-project.org [r-devel-bounces at r-project.org] On Behalf Of
Hadley Wickham [hadley at rice.edu]
Sent: 30 March 2012 07:42
To: William Dunlap
Cc: r-devel at stat.math.ethz.ch; Spencer Graves
Subject: Re: [Rd] CRAN policies
Most of that stuff is already in codetools, at least when it is checking functions
with checkUsage(). E.g., arguments of ~ are not checked. The expr argument
to with() will not be checked if you add skipWith=FALSE to the call to checkUsage.
> library(codetools)
> checkUsage(function(dataFrame) with(dataFrame, {Num/Den ; Resp ~ Pred}))
<anonymous>: no visible binding for global variable 'Num' (:1)
<anonymous>: no visible binding for global variable 'Den' (:1)
> checkUsage(function(dataFrame) with(dataFrame, {Num/Den ; Resp ~ Pred}),
skipWith=TRUE)
> checkUsage(function(dataFrame) with(DataFrame, {Num/Den ; Resp ~ Pred}),
skipWith=TRUE)
<anonymous>: no visible binding for global variable 'DataFrame'
The only part that I don't see is the mechanism to add code-walker functions to
the environment in codetools that has the standard list of them for functions with
nonstandard evaluation:
> objects(codetools:::collectUsageHandlers, all=TRUE)
[1] "$" "$<-" ".Internal"
[4] "::" ":::" "@"
[7] "@<-" "{" "~"
[10] "<-" "<<-" "="
[13] "assign" "binomial" "bquote"
[16] "data" "detach" "expression"
[19] "for" "function" "Gamma"
[22] "gaussian" "if" "library"
[25] "local" "poisson" "quasi"
[28] "quasibinomial" "quasipoisson" "quote"
[31] "Quote" "require" "substitute"
[34] "with"
It seems like we really need a standard way to add metadata to functions:
attr(with, "special_args") <- "expr"
attr(lm, "special_args") <- c("formula", "weights", "subset")
This would be useful because it could automatically contribute to the
documentation.
Similarly,
attr(my.new.method, "s3method") <- c("my.new", "method")
could be useful.
Hadley
--
Assistant Professor / Dobelman Family Junior Chair
Department of Statistics / Rice University
http://had.co.nz/
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