On Sat, 15 Dec 2018, frederik at ofb.net wrote:> I agree with Steve and Achim that we should keep some examples with no > data frame. That's Objectively Simpler, whether or not it leads to > clutter in the wrong hands. As Steve points out, we have attach() > which is an excellent language feature - not to mention with().Just for the record: Personally, I wouldn't recommend using lm() with attach() or with() but would always encourage using data= instead. In my previous e-mail I just wanted to point out that a pragmatic step for the man page could be to keep one example without data= argument when adding examples with data=.> I would go even further and say that the examples that are in lm() now > should stay at the top. Because people may be used to referring to > them, and also because Historical Order is generally a good order in > which to learn things. However, if there is an important function > argument ("data=") not in the examples, then we should add examples > which use it. Likewise if there is a popular programming style > (putting things in a data frame). So let's do something along the > lines of what Thomas is requesting, but put it after the existing > documentation? Please? > > On a bit of a tangent, I would like to see an example in lm() which > plots my data with a fitted line through it. I'm probably betraying my > ignorance here, but I was asked how to do this when showing R to a > friend and I thought it should be in lm(), after all it seems a bit > more basic than displaying a Normal Q-Q plot (whatever that is! > gasp...). Similarly for glm(). Perhaps all this can be accomplished > with merely doubling the size of the existing examples. > > Thanks. > > Frederick > > On Sat, Dec 15, 2018 at 02:15:52PM +0100, Achim Zeileis wrote: >> 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 >>> >> >> ______________________________________________ >> R-devel at r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-devel >> >
Thanks for the discussion. I do feel quite strongly that the variables should always be a part of a data frame. Then functions such as summary() and pairs() can operate on them all simultaneously.... regression is only one part of the analysis. And what if there are lots of variables? Have them all scattered about the workspace? One of them could be easily overwritten. The generic predict() will still work when lm() was not assigned a data frame, but then the 'newdata' argument needs be assigned a data.frame. So this suggests that the original fit should have used a data frame too. BTW I believe attach() should be discouraged. Functions like with() and within() are safer. Many users of attach() do not seem to detach(), and subtle problems can arise with attach()---quite dangerous really. The online help has a section called "Good practice" which is good but I think it should go a little further by actively discouraging its use in the first place. I do not wish to be contentious on all this... just encouraging good practice that's all. cheers Thomas On 17/12/18 12:26 PM, Achim Zeileis wrote:> On Sat, 15 Dec 2018, frederik at ofb.net wrote: > >> I agree with Steve and Achim that we should keep some examples with no >> data frame. That's Objectively Simpler, whether or not it leads to >> clutter in the wrong hands. As Steve points out, we have attach() >> which is an excellent language feature - not to mention with(). > > Just for the record: Personally, I wouldn't recommend using lm() with > attach() or with() but would always encourage using data= instead. > > In my previous e-mail I just wanted to point out that a pragmatic step > for the man page could be to keep one example without data= argument > when adding examples with data=. > >> I would go even further and say that the examples that are in lm() now >> should stay at the top. Because people may be used to referring to >> them, and also because Historical Order is generally a good order in >> which to learn things. However, if there is an important function >> argument ("data=") not in the examples, then we should add examples >> which use it. Likewise if there is a popular programming style >> (putting things in a data frame). So let's do something along the >> lines of what Thomas is requesting, but put it after the existing >> documentation? Please? >> >> On a bit of a tangent, I would like to see an example in lm() which >> plots my data with a fitted line through it. I'm probably betraying my >> ignorance here, but I was asked how to do this when showing R to a >> friend and I thought it should be in lm(), after all it seems a bit >> more basic than displaying a Normal Q-Q plot (whatever that is! >> gasp...). Similarly for glm(). Perhaps all this can be accomplished >> with merely doubling the size of the existing examples. >> >> Thanks. >> >> Frederick >> >> On Sat, Dec 15, 2018 at 02:15:52PM +0100, Achim Zeileis wrote: >>> 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 >>>> >>> >>> ______________________________________________ >>> R-devel at r-project.org mailing list >>> https://stat.ethz.ch/mailman/listinfo/r-devel >>> >>
> From: Thomas Yee [mailto:t.yee at auckland.ac.nz] > > Thanks for the discussion. I do feel quite strongly that > the variables should always be a part of a data frame.This seems pretty much a decision for R core, and I think it's useful to have raised the issue. But I, er, feel strongly that strong feelings and 'always' are unsafe in a best practice argument. First, other folk with different use-cases or work practice may see 'best practice' quite differently. So I would pretty much always expect exceptions. Second, for examples of capability, there are too many exceptions in this instance. For example: glm() can take a two-column matrix as a single response variable. lm() can take a matrix as a response variable. lm() can take a complete data frame as a predictor (see ?stackloss) None of these work naturally if everything is in a data frame, and some won?t work at all. Steve E ******************************************************************* This email and any attachments are confidential. Any use, copying or disclosure other than by the intended recipient is unauthorised. If you have received this message in error, please notify the sender immediately via +44(0)20 8943 7000 or notify postmaster at lgcgroup.com and delete this message and any copies from your computer and network. LGC Limited. Registered in England 2991879. Registered office: Queens Road, Teddington, Middlesex, TW11 0LY, UK
Dear Steve, Since this relates as well to the message I posted a couple of minutes before yours, I agree that it?s possible to phrase ?best practices? too categorically. In the current case, I believe that it?s reasonable to say that specifying the data argument is ?generally? or ?usually? the best option. That doesn?t rule out exceptions. Best, John ------------------------------------------------- John Fox, Professor Emeritus McMaster University Hamilton, Ontario, Canada Web: http::/socserv.mcmaster.ca/jfox> On Dec 17, 2018, at 7:49 AM, S Ellison <S.Ellison at LGCGroup.com> wrote: > > > >> From: Thomas Yee [mailto:t.yee at auckland.ac.nz] >> >> Thanks for the discussion. I do feel quite strongly that >> the variables should always be a part of a data frame. > > This seems pretty much a decision for R core, and I think it's useful to have raised the issue. > > But I, er, feel strongly that strong feelings and 'always' are unsafe in a best practice argument. > > First, other folk with different use-cases or work practice may see 'best practice' quite differently. So I would pretty much always expect exceptions. > > Second, for examples of capability, there are too many exceptions in this instance. For example: > glm() can take a two-column matrix as a single response variable. > lm() can take a matrix as a response variable. > lm() can take a complete data frame as a predictor (see ?stackloss) > > None of these work naturally if everything is in a data frame, and some won?t work at all. > > Steve E > > > > > ******************************************************************* > This email and any attachments are confidential. Any use, copying or > disclosure other than by the intended recipient is unauthorised. If > you have received this message in error, please notify the sender > immediately via +44(0)20 8943 7000 or notify postmaster at lgcgroup.com > and delete this message and any copies from your computer and network. > LGC Limited. Registered in England 2991879. > Registered office: Queens Road, Teddington, Middlesex, TW11 0LY, UK > ______________________________________________ > R-devel at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-devel