The :: is a case that we worked to get right with wrapr dot-pipe. I shared
notes on this S3/S4 pipe in the R journal
https://journal.r-project.org/archive/2018/RJ-2018-042/index.html
library(magrittr)
packageVersion("magrittr")
# [1] ‘2.0.1’
5 %>% base::sin
# Error in .::base : unused argument (
I would suggest stats::glm() should set "converged" to FALSE in the return
value in a few more situations. I believe the current returned converged ==
TRUE can be needlessly misleading when the algorithm has clearly failed (and
the algo even issued a warning, but the returned structure claims al
Forgot the url:
https://win-vector.com/2014/05/30/trimming-the-fat-from-glm-models-in-r/
On Aug 10, 2020, at 11:50 AM, John Mount
mailto:jmo...@win-vector.com>> wrote:
Thank you for your suggestion. I do know how to work around the issue. I
usually build a fresh environment as a ch
ents other than
global/base (such as those formed when building a formula in a function)
capturing references to unrelated structures.
> On Aug 10, 2020, at 11:34 AM, Duncan Murdoch wrote:
>
> On 10/08/2020 1:42 p.m., John Mount wrote:
>> I wish I had started with "I am disappoi
I wish I had started with "I am disappointed that lm() doesn't continue its
search for weights into the calling environment" or "the fact that lm() looks
only in the formula environment and data frame for weights doesn't seem
consistent with how other values are treated."
But I did not. So I do
is guaranteed
> to be found even if there's a conflicting variable in the formula
> environment, or the global environment.
>
> Duncan Murdoch
>
> On 09/08/2020 2:13 p.m., John Mount wrote:
>> I know this programmers can reason this out from R's late parameter
&g
I know this programmers can reason this out from R's late parameter evaluation
rules PLUS the explicit match.call()/eval() lm() does to work with the passed
in formula and data frame. But, from a statistical user point of view this
seems to be counter-productive. At best it works as if the user
> So yes, if one wants to use all the utilities or the various algos that the
> digest package provides, one should install and load it. But if one can live
> with MD5 hashes, why not use the built-in R function? (Well, without
> serializing an object to a file, calling tools::md5sum, and then
rds,
> Denes
>
>
> [1]: https://github.com/HenrikBengtsson/Wishlist-for-R/issues/21
> [2]:
> https://github.com/wch/r-source/blob/5a156a0865362bb8381dcd69ac335f5174a4f60c/src/library/tools/src/md5.c#L172
> [3]:
> https://github.com/wch/r-source/blob/5a156a0865362bb8381dcd69ac3
t;
> Iago
>
>
> [[alternative HTML version deleted]]
>
> __
> R-devel@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-devel
---
John Mount
http://www.win-vector.com/ <http://www.wi
>>>>>>> choose(4.001, 4)
>>>>>> [1] 4
>>>>>>> choose(4.01, 4)
>>>>>> [1] 1.000002
>>>>>>
>>>>>> Should base::choose(n, k) check whether n is within machine precision of
>>>>>> k and re
Except for the isolation of local() R pretty much already has the parsing
transformation you mention.
as.list(parse(text="
iris ->.;
group_by(., Species) ->.;
summarize(., mean_sl = mean(Sepal.Length)) ->.;
filter(., mean_sl > 5)
"))
#> [[1]]
#> . <- iris
#>
#> [[2]]
#> . <- group_by
t; Error: C stack usage 15923776 is too close to the limit
>
> What I propose on the other hand can always substitute any existing proper
> pipe in their standard feature, as long as the dot is made explicit.
>
> Best regards,
>
> Antoine
>
>
>
> L
t; f1 <- function(y) function() eval(quote(y))
> f2 <- x %.% f1(.)
> f2()
> #> [1] "a"
> ```
>
> Looking forward for your thoughts on this,
>
> Antoine
>
> [[alternative HTML version deleted]]
>
> __
> R-devel@r-p
/github.com/JuliaLang/julia/issues/28606
>> [2]: https://doi.org/10.1201/9781315381305
>> [3]: http://www.lua.org/manual/5.1/manual.html#lua_pcall
>> [4]: http://www.lua.org/manual/5.1/manual.html#lua_cpcall
>>
>> __
>
want this chaining;
and there are issues of quoting it out) in eval would be very valuable.
Obviously it is most useful where non-standard evaluation is emphasized
(plotting, formulas, and dplyr being the examples that I can immediately think
of).
---
John Mount
http://www.win
> Martin
>
That is potentially a very good outcome. Thank you so much for producing and
testing a patch.
---
John Mount
http://www.win-vector.com/ <http://www.win-vector.com/>
Our book: Practical Data Science with R http://www.manning
the pain point here
http://www.win-vector.com/blog/2016/08/my-criticism-of-r-numeric-summary/
<http://www.win-vector.com/blog/2016/08/my-criticism-of-r-numeric-summary/> (I
am not trying to be rude, more I am trying to emphasize why this can be
confusing to new users).
---
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