FWIW, before all the examples are changed to data frame variants, I think there's fairly good reason to have at least _one_ example that does _not_ place variables in a data frame. The data argument in lm() is optional. And there is more than one way to manage data in a project. I personally don't much like lots of stray variables lurking about, but if those are the only variables out there and we can be sure they aren't affected by other code, it's hardly essential to create a data frame to hold something you already have. Also, attach() is still part of R, for those folk who have a data frame but want to reference the contents across a wider range of functions without using with() a lot. lm() can reasonably omit the data argument there, too. So while there are good reasons to use data frames, there are also good reasons to provide examples that don't. Steve Ellison> -----Original Message----- > From: R-devel [mailto:r-devel-bounces at r-project.org] On Behalf Of Ben > Bolker > Sent: 13 December 2018 20:36 > To: r-devel at r-project.org > Subject: Re: [Rd] Documentation examples for lm and glm > > > Agree. Or just create the data frame with those variables in it > directly ... > > On 2018-12-13 3:26 p.m., Thomas Yee wrote: > > Hello, > > > > something that has been on my mind for a decade or two has > > been the examples for lm() and glm(). They encourage poor style > > because of mismanagement of data frames. Also, having the > > variables in a data frame means that predict() > > is more likely to work properly. > > > > For lm(), the variables should be put into a data frame. > > As 2 vectors are assigned first in the general workspace they > > should be deleted afterwards. > > > > For the glm(), the data frame d.AD is constructed but not used. Also, > > its 3 components were assigned first in the general workspace, so they > > float around dangerously afterwards like in the lm() example. > > > > Rather than attached improved .Rd files here, they are put at > > www.stat.auckland.ac.nz/~yee/Rdfiles > > You are welcome to use them! > > > > Best, > > > > Thomas > > > > ______________________________________________ > > R-devel at r-project.org mailing list > > https://stat.ethz.ch/mailman/listinfo/r-devel > > ______________________________________________ > R-devel at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-devel******************************************************************* This email and any attachments are confidential. Any use...{{dropped:8}}
I would argue examples should encourage good practice. Beginners ought to learn to keep data in data frames and not to overuse attach(). Experts can do otherwise at their own risk, but they have less need of explicit examples. On Fri, 14 Dec 2018 at 14:51, S Ellison <S.Ellison at lgcgroup.com> wrote:> FWIW, before all the examples are changed to data frame variants, I think > there's fairly good reason to have at least _one_ example that does _not_ > place variables in a data frame. > > The data argument in lm() is optional. And there is more than one way to > manage data in a project. I personally don't much like lots of stray > variables lurking about, but if those are the only variables out there and > we can be sure they aren't affected by other code, it's hardly essential to > create a data frame to hold something you already have. > Also, attach() is still part of R, for those folk who have a data frame > but want to reference the contents across a wider range of functions > without using with() a lot. lm() can reasonably omit the data argument > there, too. > > So while there are good reasons to use data frames, there are also good > reasons to provide examples that don't. > > Steve Ellison > > > > -----Original Message----- > > From: R-devel [mailto:r-devel-bounces at r-project.org] On Behalf Of Ben > > Bolker > > Sent: 13 December 2018 20:36 > > To: r-devel at r-project.org > > Subject: Re: [Rd] Documentation examples for lm and glm > > > > > > Agree. Or just create the data frame with those variables in it > > directly ... > > > > On 2018-12-13 3:26 p.m., Thomas Yee wrote: > > > Hello, > > > > > > something that has been on my mind for a decade or two has > > > been the examples for lm() and glm(). They encourage poor style > > > because of mismanagement of data frames. Also, having the > > > variables in a data frame means that predict() > > > is more likely to work properly. > > > > > > For lm(), the variables should be put into a data frame. > > > As 2 vectors are assigned first in the general workspace they > > > should be deleted afterwards. > > > > > > For the glm(), the data frame d.AD is constructed but not used. Also, > > > its 3 components were assigned first in the general workspace, so they > > > float around dangerously afterwards like in the lm() example. > > > > > > Rather than attached improved .Rd files here, they are put at > > > www.stat.auckland.ac.nz/~yee/Rdfiles > > > You are welcome to use them! > > > > > > Best, > > > > > > Thomas > > > > > > ______________________________________________ > > > R-devel at r-project.org mailing list > > > https://stat.ethz.ch/mailman/listinfo/r-devel > > > > ______________________________________________ > > R-devel at r-project.org mailing list > > https://stat.ethz.ch/mailman/listinfo/r-devel > > > ******************************************************************* > This email and any attachments are confidential. Any u...{{dropped:12}}
A pragmatic solution could be to create a simple linear regression example
with variables in the global environment and then another example with a
data.frame.
The latter might be somewhat more complex, e.g., with several regressors
and/or mixed categorical and numeric covariates to illustrate how
regression and analysis of (co-)variance can be combined. I like to use
MASS's whiteside data for this:
data("whiteside", package = "MASS")
m1 <- lm(Gas ~ Temp, data = whiteside)
m2 <- lm(Gas ~ Insul + Temp, data = whiteside)
m3 <- lm(Gas ~ Insul * Temp, data = whiteside)
anova(m1, m2, m3)
Moreover, some binary response data.frame with a few covariates might be a
useful addition to "datasets". For example a more granular version of
the
"Titanic" data (in addition to the 4-way tabel ?Titanic). Or another
relatively straightforward data set, popular in econometrics and social
sciences is the "Mroz" data, see e.g., help("PSID1976",
package = "AER").
I would be happy to help with these if such additions were considered for
datasets/stats.
On Sat, 15 Dec 2018, David Hugh-Jones wrote:
> I would argue examples should encourage good practice. Beginners ought to
> learn to keep data in data frames and not to overuse attach(). Experts can
> do otherwise at their own risk, but they have less need of explicit
> examples.
>
> On Fri, 14 Dec 2018 at 14:51, S Ellison <S.Ellison at lgcgroup.com>
wrote:
>
>> FWIW, before all the examples are changed to data frame variants, I
think
>> there's fairly good reason to have at least _one_ example that does
_not_
>> place variables in a data frame.
>>
>> The data argument in lm() is optional. And there is more than one way
to
>> manage data in a project. I personally don't much like lots of
stray
>> variables lurking about, but if those are the only variables out there
and
>> we can be sure they aren't affected by other code, it's hardly
essential to
>> create a data frame to hold something you already have.
>> Also, attach() is still part of R, for those folk who have a data frame
>> but want to reference the contents across a wider range of functions
>> without using with() a lot. lm() can reasonably omit the data argument
>> there, too.
>>
>> So while there are good reasons to use data frames, there are also good
>> reasons to provide examples that don't.
>>
>> Steve Ellison
>>
>>
>>> -----Original Message-----
>>> From: R-devel [mailto:r-devel-bounces at r-project.org] On Behalf
Of Ben
>>> Bolker
>>> Sent: 13 December 2018 20:36
>>> To: r-devel at r-project.org
>>> Subject: Re: [Rd] Documentation examples for lm and glm
>>>
>>>
>>> Agree. Or just create the data frame with those variables in it
>>> directly ...
>>>
>>> On 2018-12-13 3:26 p.m., Thomas Yee wrote:
>>>> Hello,
>>>>
>>>> something that has been on my mind for a decade or two has
>>>> been the examples for lm() and glm(). They encourage poor style
>>>> because of mismanagement of data frames. Also, having the
>>>> variables in a data frame means that predict()
>>>> is more likely to work properly.
>>>>
>>>> For lm(), the variables should be put into a data frame.
>>>> As 2 vectors are assigned first in the general workspace they
>>>> should be deleted afterwards.
>>>>
>>>> For the glm(), the data frame d.AD is constructed but not used.
Also,
>>>> its 3 components were assigned first in the general workspace,
so they
>>>> float around dangerously afterwards like in the lm() example.
>>>>
>>>> Rather than attached improved .Rd files here, they are put at
>>>> www.stat.auckland.ac.nz/~yee/Rdfiles
>>>> You are welcome to use them!
>>>>
>>>> Best,
>>>>
>>>> Thomas
>>>>
>>>> ______________________________________________
>>>> R-devel at r-project.org mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/r-devel
>>>
>>> ______________________________________________
>>> R-devel at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-devel
>>
>>
>> *******************************************************************
>> This email and any attachments are confidential. Any u...{{dropped:12}}
>
> ______________________________________________
> R-devel at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-devel
>
>>>>> David Hugh-Jones >>>>> on Sat, 15 Dec 2018 08:47:28 +0100 writes:> I would argue examples should encourage good > practice. Beginners ought to learn to keep data in data > frames and not to overuse attach(). Note there's no attach() there in any of these examples! > otherwise at their own risk, but they have less need of > explicit examples. The glm examples are nice in sofar they show both uses. I agree the lm() example(s) are "didactically misleading" by not using data frames at all. I disagree that only data frame examples should be shown. If lm() is one of the first R functions a beginneR must use -- because they are in a basic stats class, say -- it may be *better* didactically to focus on lm() in the very first example, and use data frames in a next one ... .... and instead of next one, we have the pretty clear comment ### less simple examples in "See Also" above I'm not convinced (but you can try more) we should change those examples or add more there. Martin > On Fri, 14 Dec 2018 at 14:51, S Ellison > <S.Ellison at lgcgroup.com> wrote: >> FWIW, before all the examples are changed to data frame >> variants, I think there's fairly good reason to have at >> least _one_ example that does _not_ place variables in a >> data frame. >> >> The data argument in lm() is optional. And there is more >> than one way to manage data in a project. I personally >> don't much like lots of stray variables lurking about, >> but if those are the only variables out there and we can >> be sure they aren't affected by other code, it's hardly >> essential to create a data frame to hold something you >> already have. Also, attach() is still part of R, for >> those folk who have a data frame but want to reference >> the contents across a wider range of functions without >> using with() a lot. lm() can reasonably omit the data >> argument there, too. >> >> So while there are good reasons to use data frames, there >> are also good reasons to provide examples that don't. >> >> Steve Ellison >> >> >> > -----Original Message----- > From: R-devel >> [mailto:r-devel-bounces at r-project.org] On Behalf Of Ben > >> Bolker > Sent: 13 December 2018 20:36 > To: >> r-devel at r-project.org > Subject: Re: [Rd] Documentation >> examples for lm and glm >> > >> > >> > Agree. Or just create the data frame with those >> variables in it > directly ... >> > >> > On 2018-12-13 3:26 p.m., Thomas Yee wrote: > > Hello, >> > > >> > > something that has been on my mind for a decade or >> two has > > been the examples for lm() and glm(). They >> encourage poor style > > because of mismanagement of data >> frames. Also, having the > > variables in a data frame >> means that predict() > > is more likely to work properly. >> > > >> > > For lm(), the variables should be put into a data >> frame. > > As 2 vectors are assigned first in the >> general workspace they > > should be deleted afterwards. >> > > >> > > For the glm(), the data frame d.AD is constructed but >> not used. Also, > > its 3 components were assigned first >> in the general workspace, so they > > float around >> dangerously afterwards like in the lm() example. >> > > >> > > Rather than attached improved .Rd files here, they >> are put at > > www.stat.auckland.ac.nz/~yee/Rdfiles > > >> You are welcome to use them! >> > > >> > > Best, >> > > >> > > Thomas >> > > >> > > ______________________________________________ > > >> R-devel at r-project.org mailing list > > >> https://stat.ethz.ch/mailman/listinfo/r-devel >> > >> > ______________________________________________ > >> R-devel at r-project.org mailing list > >> https://stat.ethz.ch/mailman/listinfo/r-devel >> >> >> ******************************************************************* >> This email and any attachments are confidential. Any >> u...{{dropped:12}} > ______________________________________________ > R-devel at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-devel
Dear Martin, I think that everyone agrees that it?s generally preferable to use the data argument to lm() and I have nothing significant to add to the substance of the discussion, but I think that it?s a mistake not to add to the current examples, for the following reasons: (1) Relegating examples using the data argument to ?see also? doesn?t suggest that using the argument is a best practice. Most users won?t bother to click the links. (2) In my opinion, an new initial example using the data argument would more clearly suggest that this is the normally the best option. (3) I think that it would also be desirable to add a remark to the explanation of the data argument, something like, ?Although the argument is optional, it's generally preferable to specify it explicitly.? And similarly on the help page for glm(). My two (or three) cents. John ------------------------------------------------- John Fox, Professor Emeritus McMaster University Hamilton, Ontario, Canada Web: http::/socserv.mcmaster.ca/jfox> On Dec 17, 2018, at 3:05 AM, Martin Maechler <maechler at stat.math.ethz.ch> wrote: > >>>>>> David Hugh-Jones >>>>>> on Sat, 15 Dec 2018 08:47:28 +0100 writes: > >> I would argue examples should encourage good >> practice. Beginners ought to learn to keep data in data >> frames and not to overuse attach(). > > Note there's no attach() there in any of these examples! > >> otherwise at their own risk, but they have less need of >> explicit examples. > > The glm examples are nice in sofar they show both uses. > > I agree the lm() example(s) are "didactically misleading" by > not using data frames at all. > > I disagree that only data frame examples should be shown. > If lm() is one of the first R functions a beginneR must use -- > because they are in a basic stats class, say -- it may be > *better* didactically to focus on lm() in the very first > example, and use data frames in a next one ... > .... and instead of next one, we have the pretty clear comment > > ### less simple examples in "See Also" above > > I'm not convinced (but you can try more) we should change those > examples or add more there. > > Martin > >> On Fri, 14 Dec 2018 at 14:51, S Ellison >> <S.Ellison at lgcgroup.com> wrote: > >>> FWIW, before all the examples are changed to data frame >>> variants, I think there's fairly good reason to have at >>> least _one_ example that does _not_ place variables in a >>> data frame. >>> >>> The data argument in lm() is optional. And there is more >>> than one way to manage data in a project. I personally >>> don't much like lots of stray variables lurking about, >>> but if those are the only variables out there and we can >>> be sure they aren't affected by other code, it's hardly >>> essential to create a data frame to hold something you >>> already have. Also, attach() is still part of R, for >>> those folk who have a data frame but want to reference >>> the contents across a wider range of functions without >>> using with() a lot. lm() can reasonably omit the data >>> argument there, too. >>> >>> So while there are good reasons to use data frames, there >>> are also good reasons to provide examples that don't. >>> >>> Steve Ellison >>> >>> >>>> -----Original Message----- > From: R-devel >>> [mailto:r-devel-bounces at r-project.org] On Behalf Of Ben > >>> Bolker > Sent: 13 December 2018 20:36 > To: >>> r-devel at r-project.org > Subject: Re: [Rd] Documentation >>> examples for lm and glm >>>> >>>> >>>> Agree. Or just create the data frame with those >>> variables in it > directly ... >>>> >>>> On 2018-12-13 3:26 p.m., Thomas Yee wrote: > > Hello, >>>>> >>>>> something that has been on my mind for a decade or >>> two has > > been the examples for lm() and glm(). They >>> encourage poor style > > because of mismanagement of data >>> frames. Also, having the > > variables in a data frame >>> means that predict() > > is more likely to work properly. >>>>> >>>>> For lm(), the variables should be put into a data >>> frame. > > As 2 vectors are assigned first in the >>> general workspace they > > should be deleted afterwards. >>>>> >>>>> For the glm(), the data frame d.AD is constructed but >>> not used. Also, > > its 3 components were assigned first >>> in the general workspace, so they > > float around >>> dangerously afterwards like in the lm() example. >>>>> >>>>> Rather than attached improved .Rd files here, they >>> are put at > > www.stat.auckland.ac.nz/~yee/Rdfiles > > >>> You are welcome to use them! >>>>> >>>>> Best, >>>>> >>>>> Thomas >>>>> >>>>> ______________________________________________ > > >>> R-devel at r-project.org mailing list > > >>> https://stat.ethz.ch/mailman/listinfo/r-devel >>>> >>>> ______________________________________________ > >>> R-devel at r-project.org mailing list > >>> https://stat.ethz.ch/mailman/listinfo/r-devel >>> >>> >>> ******************************************************************* >>> This email and any attachments are confidential. Any >>> u...{{dropped:12}} > >> ______________________________________________ >> R-devel at r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-devel > > ______________________________________________ > R-devel at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-devel
Dear All,
do you think that use of a data argument is best practice in the example
below?
regards,
Heinz
### trivial example
plotwithline <- function(x, y) {
plot(x, y)
abline(lm(y~x)) ## data argument?
}
set.seed(25)
df0 <- data.frame(x=rnorm(20), y=rnorm(20))
plotwithline(df0[['x']], df0[['y']])
Fox, John wrote/hat geschrieben on/am 17.12.2018 15:21:> Dear Martin,
>
> I think that everyone agrees that it?s generally preferable to use the data
argument to lm() and I have nothing significant to add to the substance of the
discussion, but I think that it?s a mistake not to add to the current examples,
for the following reasons:
>
> (1) Relegating examples using the data argument to ?see also? doesn?t
suggest that using the argument is a best practice. Most users won?t bother to
click the links.
>
> (2) In my opinion, an new initial example using the data argument would
more clearly suggest that this is the normally the best option.
>
> (3) I think that it would also be desirable to add a remark to the
explanation of the data argument, something like, ?Although the argument is
optional, it's generally preferable to specify it explicitly.? And similarly
on the help page for glm().
>
> My two (or three) cents.
>
> John
>
> -------------------------------------------------
> John Fox, Professor Emeritus
> McMaster University
> Hamilton, Ontario, Canada
> Web: http::/socserv.mcmaster.ca/jfox
>
>> On Dec 17, 2018, at 3:05 AM, Martin Maechler <maechler at
stat.math.ethz.ch> wrote:
>>
>>>>>>> David Hugh-Jones
>>>>>>> on Sat, 15 Dec 2018 08:47:28 +0100 writes:
>>
>>> I would argue examples should encourage good
>>> practice. Beginners ought to learn to keep data in data
>>> frames and not to overuse attach().
>>
>> Note there's no attach() there in any of these examples!
>>
>>> otherwise at their own risk, but they have less need of
>>> explicit examples.
>>
>> The glm examples are nice in sofar they show both uses.
>>
>> I agree the lm() example(s) are "didactically misleading" by
>> not using data frames at all.
>>
>> I disagree that only data frame examples should be shown.
>> If lm() is one of the first R functions a beginneR must use --
>> because they are in a basic stats class, say -- it may be
>> *better* didactically to focus on lm() in the very first
>> example, and use data frames in a next one ...
>> .... and instead of next one, we have the pretty clear comment
>>
>> ### less simple examples in "See Also" above
>>
>> I'm not convinced (but you can try more) we should change those
>> examples or add more there.
>>
>> Martin
>>
>>> On Fri, 14 Dec 2018 at 14:51, S Ellison
>>> <S.Ellison at lgcgroup.com> wrote:
>>
>>>> FWIW, before all the examples are changed to data frame
>>>> variants, I think there's fairly good reason to have at
>>>> least _one_ example that does _not_ place variables in a
>>>> data frame.
>>>>
>>>> The data argument in lm() is optional. And there is more
>>>> than one way to manage data in a project. I personally
>>>> don't much like lots of stray variables lurking about,
>>>> but if those are the only variables out there and we can
>>>> be sure they aren't affected by other code, it's hardly
>>>> essential to create a data frame to hold something you
>>>> already have. Also, attach() is still part of R, for
>>>> those folk who have a data frame but want to reference
>>>> the contents across a wider range of functions without
>>>> using with() a lot. lm() can reasonably omit the data
>>>> argument there, too.
>>>>
>>>> So while there are good reasons to use data frames, there
>>>> are also good reasons to provide examples that don't.
>>>>
>>>> Steve Ellison
>>>>
>>>>
>>>>> -----Original Message----- > From: R-devel
>>>> [mailto:r-devel-bounces at r-project.org] On Behalf Of Ben >
>>>> Bolker > Sent: 13 December 2018 20:36 > To:
>>>> r-devel at r-project.org > Subject: Re: [Rd] Documentation
>>>> examples for lm and glm
>>>>>
>>>>>
>>>>> Agree. Or just create the data frame with those
>>>> variables in it > directly ...
>>>>>
>>>>> On 2018-12-13 3:26 p.m., Thomas Yee wrote: > > Hello,
>>>>>>
>>>>>> something that has been on my mind for a decade or
>>>> two has > > been the examples for lm() and glm(). They
>>>> encourage poor style > > because of mismanagement of data
>>>> frames. Also, having the > > variables in a data frame
>>>> means that predict() > > is more likely to work properly.
>>>>>>
>>>>>> For lm(), the variables should be put into a data
>>>> frame. > > As 2 vectors are assigned first in the
>>>> general workspace they > > should be deleted afterwards.
>>>>>>
>>>>>> For the glm(), the data frame d.AD is constructed but
>>>> not used. Also, > > its 3 components were assigned first
>>>> in the general workspace, so they > > float around
>>>> dangerously afterwards like in the lm() example.
>>>>>>
>>>>>> Rather than attached improved .Rd files here, they
>>>> are put at > > www.stat.auckland.ac.nz/~yee/Rdfiles >
>
>>>> You are welcome to use them!
>>>>>>
>>>>>> Best,
>>>>>>
>>>>>> Thomas
>>>>>>
>>>>>> ______________________________________________ >
>
>>>> R-devel at r-project.org mailing list > >
>>>> https://stat.ethz.ch/mailman/listinfo/r-devel
>>>>>
>>>>> ______________________________________________ >
>>>> R-devel at r-project.org mailing list >
>>>> https://stat.ethz.ch/mailman/listinfo/r-devel
>>>>
>>>>
>>>>
*******************************************************************
>>>> This email and any attachments are confidential. Any
>>>> u...{{dropped:12}}
>>
>>> ______________________________________________
>>> R-devel at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-devel
>>
>> ______________________________________________
>> R-devel at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-devel
>
> ______________________________________________
> R-devel at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-devel
Dear Heinz, ----------------------------------------------> On Dec 17, 2018, at 10:19 AM, Heinz Tuechler <tuechler at gmx.at> wrote: > > Dear All, > > do you think that use of a data argument is best practice in the example below?No, but it is *normally* or *usually* the best option, in my opinion. Best, John> > regards, > > Heinz > > ### trivial example > plotwithline <- function(x, y) { > plot(x, y) > abline(lm(y~x)) ## data argument? > } > > set.seed(25) > df0 <- data.frame(x=rnorm(20), y=rnorm(20)) > > plotwithline(df0[['x']], df0[['y']]) > > > > Fox, John wrote/hat geschrieben on/am 17.12.2018 15:21: >> Dear Martin, >> >> I think that everyone agrees that it?s generally preferable to use the data argument to lm() and I have nothing significant to add to the substance of the discussion, but I think that it?s a mistake not to add to the current examples, for the following reasons: >> >> (1) Relegating examples using the data argument to ?see also? doesn?t suggest that using the argument is a best practice. Most users won?t bother to click the links. >> >> (2) In my opinion, an new initial example using the data argument would more clearly suggest that this is the normally the best option. >> >> (3) I think that it would also be desirable to add a remark to the explanation of the data argument, something like, ?Although the argument is optional, it's generally preferable to specify it explicitly.? And similarly on the help page for glm(). >> >> My two (or three) cents. >> >> John >> >> ------------------------------------------------- >> John Fox, Professor Emeritus >> McMaster University >> Hamilton, Ontario, Canada >> Web: http::/socserv.mcmaster.ca/jfox >> >>> On Dec 17, 2018, at 3:05 AM, Martin Maechler <maechler at stat.math.ethz.ch> wrote: >>> >>>>>>>> David Hugh-Jones >>>>>>>> on Sat, 15 Dec 2018 08:47:28 +0100 writes: >>> >>>> I would argue examples should encourage good >>>> practice. Beginners ought to learn to keep data in data >>>> frames and not to overuse attach(). >>> >>> Note there's no attach() there in any of these examples! >>> >>>> otherwise at their own risk, but they have less need of >>>> explicit examples. >>> >>> The glm examples are nice in sofar they show both uses. >>> >>> I agree the lm() example(s) are "didactically misleading" by >>> not using data frames at all. >>> >>> I disagree that only data frame examples should be shown. >>> If lm() is one of the first R functions a beginneR must use -- >>> because they are in a basic stats class, say -- it may be >>> *better* didactically to focus on lm() in the very first >>> example, and use data frames in a next one ... >>> .... and instead of next one, we have the pretty clear comment >>> >>> ### less simple examples in "See Also" above >>> >>> I'm not convinced (but you can try more) we should change those >>> examples or add more there. >>> >>> Martin >>> >>>> On Fri, 14 Dec 2018 at 14:51, S Ellison >>>> <S.Ellison at lgcgroup.com> wrote: >>> >>>>> FWIW, before all the examples are changed to data frame >>>>> variants, I think there's fairly good reason to have at >>>>> least _one_ example that does _not_ place variables in a >>>>> data frame. >>>>> >>>>> The data argument in lm() is optional. And there is more >>>>> than one way to manage data in a project. I personally >>>>> don't much like lots of stray variables lurking about, >>>>> but if those are the only variables out there and we can >>>>> be sure they aren't affected by other code, it's hardly >>>>> essential to create a data frame to hold something you >>>>> already have. Also, attach() is still part of R, for >>>>> those folk who have a data frame but want to reference >>>>> the contents across a wider range of functions without >>>>> using with() a lot. lm() can reasonably omit the data >>>>> argument there, too. >>>>> >>>>> So while there are good reasons to use data frames, there >>>>> are also good reasons to provide examples that don't. >>>>> >>>>> Steve Ellison >>>>> >>>>> >>>>>> -----Original Message----- > From: R-devel >>>>> [mailto:r-devel-bounces at r-project.org] On Behalf Of Ben > >>>>> Bolker > Sent: 13 December 2018 20:36 > To: >>>>> r-devel at r-project.org > Subject: Re: [Rd] Documentation >>>>> examples for lm and glm >>>>>> >>>>>> >>>>>> Agree. Or just create the data frame with those >>>>> variables in it > directly ... >>>>>> >>>>>> On 2018-12-13 3:26 p.m., Thomas Yee wrote: > > Hello, >>>>>>> >>>>>>> something that has been on my mind for a decade or >>>>> two has > > been the examples for lm() and glm(). They >>>>> encourage poor style > > because of mismanagement of data >>>>> frames. Also, having the > > variables in a data frame >>>>> means that predict() > > is more likely to work properly. >>>>>>> >>>>>>> For lm(), the variables should be put into a data >>>>> frame. > > As 2 vectors are assigned first in the >>>>> general workspace they > > should be deleted afterwards. >>>>>>> >>>>>>> For the glm(), the data frame d.AD is constructed but >>>>> not used. Also, > > its 3 components were assigned first >>>>> in the general workspace, so they > > float around >>>>> dangerously afterwards like in the lm() example. >>>>>>> >>>>>>> Rather than attached improved .Rd files here, they >>>>> are put at > > www.stat.auckland.ac.nz/~yee/Rdfiles > > >>>>> You are welcome to use them! >>>>>>> >>>>>>> Best, >>>>>>> >>>>>>> Thomas >>>>>>> >>>>>>> ______________________________________________ > > >>>>> R-devel at r-project.org mailing list > > >>>>> https://stat.ethz.ch/mailman/listinfo/r-devel >>>>>> >>>>>> ______________________________________________ > >>>>> R-devel at r-project.org mailing list > >>>>> https://stat.ethz.ch/mailman/listinfo/r-devel >>>>> >>>>> >>>>> ******************************************************************* >>>>> This email and any attachments are confidential. Any >>>>> u...{{dropped:12}} >>> >>>> ______________________________________________ >>>> R-devel at r-project.org mailing list >>>> https://stat.ethz.ch/mailman/listinfo/r-devel >>> >>> ______________________________________________ >>> R-devel at r-project.org mailing list >>> https://stat.ethz.ch/mailman/listinfo/r-devel >> >> ______________________________________________ >> R-devel at r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-devel > > ______________________________________________ > R-devel at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-devel
Dear John, fully agreed! In the global environment I always keep my "data-variables" in a data.frame. However, if I look in help I like examples that start with the particular aspects of a function. It is important to know, if a function offers a data argument, but in the first line I don't need an example for the use of a data argument each time I look in help. best, Heinz Fox, John wrote/hat geschrieben on/am 17.12.2018 16:23:> Dear Heinz, > > ---------------------------------------------- >> On Dec 17, 2018, at 10:19 AM, Heinz Tuechler <tuechler at gmx.at> wrote: >> >> Dear All, >> >> do you think that use of a data argument is best practice in the example below? > > No, but it is *normally* or *usually* the best option, in my opinion. > > Best, > John > >> >> regards, >> >> Heinz >> >> ### trivial example >> plotwithline <- function(x, y) { >> plot(x, y) >> abline(lm(y~x)) ## data argument? >> } >> >> set.seed(25) >> df0 <- data.frame(x=rnorm(20), y=rnorm(20)) >> >> plotwithline(df0[['x']], df0[['y']]) >> >> >> >> Fox, John wrote/hat geschrieben on/am 17.12.2018 15:21: >>> Dear Martin, >>> >>> I think that everyone agrees that it?s generally preferable to use the data argument to lm() and I have nothing significant to add to the substance of the discussion, but I think that it?s a mistake not to add to the current examples, for the following reasons: >>> >>> (1) Relegating examples using the data argument to ?see also? doesn?t suggest that using the argument is a best practice. Most users won?t bother to click the links. >>> >>> (2) In my opinion, an new initial example using the data argument would more clearly suggest that this is the normally the best option. >>> >>> (3) I think that it would also be desirable to add a remark to the explanation of the data argument, something like, ?Although the argument is optional, it's generally preferable to specify it explicitly.? And similarly on the help page for glm(). >>> >>> My two (or three) cents. >>> >>> John >>> >>> ------------------------------------------------- >>> John Fox, Professor Emeritus >>> McMaster University >>> Hamilton, Ontario, Canada >>> Web: http::/socserv.mcmaster.ca/jfox >>> >>>> On Dec 17, 2018, at 3:05 AM, Martin Maechler <maechler at stat.math.ethz.ch> wrote: >>>> >>>>>>>>> David Hugh-Jones >>>>>>>>> on Sat, 15 Dec 2018 08:47:28 +0100 writes: >>>> >>>>> I would argue examples should encourage good >>>>> practice. Beginners ought to learn to keep data in data >>>>> frames and not to overuse attach(). >>>> >>>> Note there's no attach() there in any of these examples! >>>> >>>>> otherwise at their own risk, but they have less need of >>>>> explicit examples. >>>> >>>> The glm examples are nice in sofar they show both uses. >>>> >>>> I agree the lm() example(s) are "didactically misleading" by >>>> not using data frames at all. >>>> >>>> I disagree that only data frame examples should be shown. >>>> If lm() is one of the first R functions a beginneR must use -- >>>> because they are in a basic stats class, say -- it may be >>>> *better* didactically to focus on lm() in the very first >>>> example, and use data frames in a next one ... >>>> .... and instead of next one, we have the pretty clear comment >>>> >>>> ### less simple examples in "See Also" above >>>> >>>> I'm not convinced (but you can try more) we should change those >>>> examples or add more there. >>>> >>>> Martin >>>> >>>>> On Fri, 14 Dec 2018 at 14:51, S Ellison >>>>> <S.Ellison at lgcgroup.com> wrote: >>>> >>>>>> FWIW, before all the examples are changed to data frame >>>>>> variants, I think there's fairly good reason to have at >>>>>> least _one_ example that does _not_ place variables in a >>>>>> data frame. >>>>>> >>>>>> The data argument in lm() is optional. And there is more >>>>>> than one way to manage data in a project. I personally >>>>>> don't much like lots of stray variables lurking about, >>>>>> but if those are the only variables out there and we can >>>>>> be sure they aren't affected by other code, it's hardly >>>>>> essential to create a data frame to hold something you >>>>>> already have. Also, attach() is still part of R, for >>>>>> those folk who have a data frame but want to reference >>>>>> the contents across a wider range of functions without >>>>>> using with() a lot. lm() can reasonably omit the data >>>>>> argument there, too. >>>>>> >>>>>> So while there are good reasons to use data frames, there >>>>>> are also good reasons to provide examples that don't. >>>>>> >>>>>> Steve Ellison >>>>>> >>>>>> >>>>>>> -----Original Message----- > From: R-devel >>>>>> [mailto:r-devel-bounces at r-project.org] On Behalf Of Ben > >>>>>> Bolker > Sent: 13 December 2018 20:36 > To: >>>>>> r-devel at r-project.org > Subject: Re: [Rd] Documentation >>>>>> examples for lm and glm >>>>>>> >>>>>>> >>>>>>> Agree. Or just create the data frame with those >>>>>> variables in it > directly ... >>>>>>> >>>>>>> On 2018-12-13 3:26 p.m., Thomas Yee wrote: > > Hello, >>>>>>>> >>>>>>>> something that has been on my mind for a decade or >>>>>> two has > > been the examples for lm() and glm(). They >>>>>> encourage poor style > > because of mismanagement of data >>>>>> frames. Also, having the > > variables in a data frame >>>>>> means that predict() > > is more likely to work properly. >>>>>>>> >>>>>>>> For lm(), the variables should be put into a data >>>>>> frame. > > As 2 vectors are assigned first in the >>>>>> general workspace they > > should be deleted afterwards. >>>>>>>> >>>>>>>> For the glm(), the data frame d.AD is constructed but >>>>>> not used. Also, > > its 3 components were assigned first >>>>>> in the general workspace, so they > > float around >>>>>> dangerously afterwards like in the lm() example. >>>>>>>> >>>>>>>> Rather than attached improved .Rd files here, they >>>>>> are put at > > www.stat.auckland.ac.nz/~yee/Rdfiles > > >>>>>> You are welcome to use them! >>>>>>>> >>>>>>>> Best, >>>>>>>> >>>>>>>> Thomas >>>>>>>>