[Rd] duplicated factor labels.
ligges at statistik.tu-dortmund.de
Fri Jun 23 14:20:19 CEST 2017
On 23.06.2017 11:51, peter dalgaard wrote:
> Hmm, the danger in this is that duplicated factor levels _used_ to be allowed (i.e. multiple codes with the same level). Disallowing it is what broke read.spss() on some files, because SPSS's concept of value labels is not 1-to-1 with factors.
> Reallowing it with different semantics could be premature. I mean, if we hadn't had the "forbidden" step, read.spss() could have changed behaviour unnoticed. So what if there is code relying on duplicate factor levels, which hasn't been run for some time?
The read.spss code now allows for two things, one is to do what Martin
implemented, the other one is to keep the labels seperated and rename
them to be unique (the latter is the default now, explanation follows
Quite often we found something like the following example in SPSS files,
translated into R speak:
factor(c(1,3,2,4,5,3,1), levels=1:5, labels=c("Strongly disagree",
"Disagree", "Neither agree nor disagree", "Agree", "Agree"))
where the last is a simple copy and paste error and should be "Strongly
I had the chance to look at > 1300 SPSS files our consulting center
collected during the last 20 year, and in several hundred cases we found
such a problem that was copy & paste error and simply wrong.
Only in < 5 cases condensing several levels into one was appropriate,
hence we decided to keep duplicated levels by changing the names as the
Based on this experience I'd propose no to touch factor but rather add a
function that easily allows for this reduction, if we do not have that
>> On 23 Jun 2017, at 10:42 , Martin Maechler <maechler at stat.math.ethz.ch> wrote:
>>>>>>> Martin Maechler <maechler at stat.math.ethz.ch>
>>>>>>> on Thu, 22 Jun 2017 11:43:59 +0200 writes:
>>>>>>> Paul Johnson <pauljohn32 at gmail.com>
>>>>>>> on Fri, 16 Jun 2017 11:02:34 -0500 writes:
>>>> On Fri, Jun 16, 2017 at 2:35 AM, Joris Meys <jorismeys at gmail.com> wrote:
>>>>> To extwnd on Martin 's explanation :
>>>>> In factor(), levels are the unique input values and labels the unique output
>>>>> values. So the function levels() actually displays the labels.
>>>> Dear Joris
>>>> I think we agree. Currently, factor insists both levels and labels be unique.
>>>> I wish that it would not accept nonunique labels. I also understand it
>>>> is impractical to change this now in base R.
>>>> I don't think I succeeded in explaining why this would be nicer.
>>>> Here's another example. Fairly often, we see input data like
>>>> x <- c("Male", "Man", "male", "Man", "Female")
>>>> The first four represent the same value. I'd like to go in one step
>>>> to a new factor variable with enumerated types "Male" and "Female".
>>>> This fails
>>>> xf <- factor(x, levels = c("Male", "Man", "male", "Female"),
>>>> labels = c("Male", "Male", "Male", "Female"))
>>>> Instead, we need 2 steps.
>>>> xf <- factor(x, levels = c("Male", "Man", "male", "Female"))
>>>> levels(xf) <- c("Male", "Male", "Male", "Female")
>>>> I think it is quirky that `levels<-.factor` allows the duplicated
>>>> labels, whereas factor does not.
>>>> I wrote a function rockchalk::combineLevels to simplify combining
>>>> levels, but most of the students here like plyr::mapvalues to do it.
>>>> The use of levels() can be tricky because one must enumerate all
>>>> values, not just the ones being changed.
>>>> But I do understand Martin's point. Its been this way 25 years, it
>>>> won't change. :).
>>> Well.. the above is a bit out of context.
>>> Your first example really did not make a point to me (and Joris)
>>> and I showed that you could use even two different simple factor() calls to
>>> produce what you wanted
>>> yc <- factor(c("1",NA,NA,"4","4","4"))
>>> yn <- factor(c( 1, NA,NA, 4, 4, 4))
>>> Your new example is indeed much more convincing !
>>> (Note though that the two steps that are needed can be written
>>> more shortly
>>> The "been this way 25 years" is one a reason to be very
>>> cautious(*) with changes, but not a reason for no changes!
>>> (*) Indeed as some of you have noted we really should not "break behavior".
>>> This means to me we cannot accept a change there which gives
>>> an error or a different result in cases the old behavior gave a valid factor.
>>> I'm looking at a possible change currently
>>> [not promising that a change will happen ...]
>> In the end, I've liked the change (after 2-3 iterations), and
>> now been brave to commit to R-devel (svn 72845).
>> With the change, I had to disable one of our own regression
>> checks (tests/reg-tests-1b.R, line 726):
>> The following is now (in R-devel -> R 3.5.0) valid:
>>> factor(1:2, labels = c("A","A"))
>>  A A
>> Levels: A
>> I wonder how many CRAN package checks will "break" from
>> this (my guess is in the order of a dozen), but I hope
>> that these breakages will be benign, e.g., similar to the above
>> case where before an error was expected via tools :: assertError(.)
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