stringsAsFactors

Le mercredi 13 f?vrier 2013 ? 12:33 +0000, Michael Dewey a ?crit :

At 18:01 11/02/2013, Ista Zahn wrote:

FWIW my view is that for data cleaning and organizing factors just get
it the way. For modeling I like them because they make it easier to
understand what is happening. For example I can look at the levels()
to see what the reference group will be. With characters one has to
know a) that levels are created in alphabetical order and b) the
alphabetical order of the the unique values in the character vector.
Ugh. So my habit is to turn off stringsAsFactors, then explicitly
convert to factors before modeling (I also use factors to change the
order in which things are displayed in tables and graphs, another
place where converting to factors myself is useful but the creating
them in alphabetical order by default is not)

All this is to say that I would like options(stingsAsFactors=FALSE) to
be the default, but I like the warning about converting to factors in
modeling functions because it reminds me that I forgot to covert them,
which I like to do anyway...

I seem to be one of the few people who find the current default
helpful. When I read in a dataset I am nearly always going to follow
it with one or more of the modelling functions and so I do want to
treat the categorical variables as factors. I cannot off-hand think
of an example where I have had to convert them to characters.

If the changes to modeling functions that are discussed in this thread
can finally be applied (i.e. a solution is found), characters would be
converted to factors automatically, so you would not notice the
difference. And if you need to change the order of levels of your
factors, calling factor(myVar, levels=c(...)) is the same, be myVar a
character or a factor.

Incidentally xkcd has, while this discussion has been going on,
posted something relevant
http://www.xkcd.com/1172/

Truly hilarious, indeed. But beware, it sounds like an argument in favor
of the change, while you are lobbying against it. :-p


Regards

Best,
Ista

On Mon, Feb 11, 2013 at 12:50 PM, Duncan Murdoch
<murdoch.duncan at gmail.com> wrote:

On 11/02/2013 12:13 PM, William Dunlap wrote:

Note that changing this does not just mean getting rid of "silly
warnings".
Currently, predict.lm() can give wrong answers when stringsAsFactors is
FALSE.

    > d <- data.frame(x=1:10, f=rep(c("A","B","C"), c(4,3,3)), y=c(1:4,

15:17, 28.1,28.8,30.1))

    > fit_ab <- lm(y ~ x + f, data = d, subset = f!="B")

    Warning message:
    In model.matrix.default(mt, mf, contrasts) :
      variable 'f' converted to a factor

    > predict(fit_ab, newdata=d)

     1 2 3 4 5 6 7 8 9 10
     1  2  3  4 25 26 27  8  9 10
    Warning messages:
    1: In model.matrix.default(Terms, m, contrasts.arg = object$contrasts)
:
      variable 'f' converted to a factor
    2: In predict.lm(fit_ab, newdata = d) :
      prediction from a rank-deficient fit may be misleading

fit_ab is not rank-deficient and the predict should report
     1 2 3 4 NA NA NA 28 29 30

In R-devel, the two warnings about factor conversions are no longer given,
but the predictions are the same and the warning about rank

deficiency still

shows up.  If f is set to be a factor, an error is generated:

Error in model.frame.default(Terms, newdata, na.action = na.action, xlev =
object$xlevels) :
   factor f has new levels B

I think both the warning and error are somewhat reasonable responses.  The
fit is rank deficient relative to the model that includes f ==

"B",  because

the column of the design matrix corresponding to f level B would be
completely zero.  In this particular model, we could still do predictions
for the other levels, but it also seems reasonable to quit, given that
clearly something has gone wrong.

I do think that it's unfortunate that we don't get the same result in both
cases, and I'd like to have gotten the predictions you suggested, but I
don't think that's going to happen.  The reason for the difference is that
the subsetting is done before the conversion to a factor, but I think that
is unavoidable without really big changes.

Duncan Murdoch

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 Terry Therneau
Sent: Monday, February 11, 2013 5:50 AM
To: r-devel at r-project.org; Duncan Murdoch
Subject: Re: [Rd] stringsAsFactors

I think your idea to remove the warnings is excellent, and a good
compromise.
Characters
already work fine in modeling functions except for the silly warning.

It is interesting how often the defaults for a program reflect the data
sets in use at the
time the defaults were chosen.  There are some such in my own survival
package whose
proper value is no longer as "obvious" as it was when I chose them.
Factors are very
handy for variables which have only a few levels and will be used in
modeling.  Every
character variable of every dataset in "Statistical Models in S", which
introduced
factors, is of this type so auto-transformation made a lot of sense.
The "solder" data
set there is one for which Helmert contrasts are proper so guess what
the default
contrast
option was?  (I think there are only a few data sets in the world for
which Helmert makes
sense, however, and R eventually changed the default.)

For character variables that should not be factors such as a street
adress
stringsAsFactors can be a real PITA, and I expect that people's
preference for the option
depends almost entirely on how often these arise in their own work.  As
long as there is
an option that can be overridden I'm okay.  Yes, I'd prefer FALSE as the
default, partly
because the current value is a tripwire in the hallway that eventually
catches every new
user.

Terry Therneau

On 02/11/2013 05:00 AM, r-devel-request at r-project.org wrote:

Both of these were discussed by R Core.  I think it's unlikely the
default for stringsAsFactors will be changed (some R Core members like
the current behaviour), but it's fairly likely the show.signif.stars
default will change.  (That's if someone gets around to it:  I
personally don't care about that one.  P-values are commonly used
statistics, and the stars are just a simple graphical display of them.
I find some p-values to be useful, and the display to be harmless.)

I think it's really unlikely the more extreme changes (i.e. dropping
show.signif.stars completely, or dropping p-values) will happen.

Regarding stringsAsFactors:  I'm not going to defend keeping it as is,
I'll let the people who like it defend it.  What I will likely do is
make a few changes so that character vectors are automatically changed
to factors in modelling functions, so that operating with
stringsAsFactors=FALSE doesn't trigger silly warnings.

Michael Dewey
info at aghmed.fsnet.co.uk
http://www.aghmed.fsnet.co.uk/home.html