[R] glm.fit: fitted probabilities numerically 0 or 1 occurred & glm.fit: algorithm did not converge
Shivi Bhatia
shivipmp82 at gmail.com
Fri Aug 12 20:32:09 CEST 2016
Hi Michael,
In all the masking process some of the variables were missed. Please find
the updated file.
Also here is the updated code: (i am removed one of the var as it had
missing information):
glm.fit= glm(survey ~ support_cat + region+ support_lvl+ skill_group+
application_area+ functional_area+
repS+ case_age+ case_status+ severity_level+
sla_status, data = new, family = binomial)
Kindly assist with the same.
On Fri, Aug 12, 2016 at 11:05 PM, Michael Dewey <lists at dewey.myzen.co.uk>
wrote:
> Your example code refers to a variable which is not in your dataset (repS)
> so I get an error message. If I assume repS is in fact rep_score I get
> another variable not found (delivery_segmentation).
>
> I am afraid that I am unable to sort that one out so this is going to
> remain a mystery. I endorse Bert's suggestion of getting local help.
>
> On 12/08/2016 17:24, Shivi Bhatia wrote:
>
>> Hi Bert,
>>
>> Does this text file help. Apologies if this does not help as i have a
>> hard time on many occasions to get a reproducible example.
>>
>> If this doesn't work a CSV with only 100kb of data i can share.
>>
>> Regards, Shivi
>>
>> On Fri, Aug 12, 2016 at 8:50 PM, Shivi Bhatia <shivipmp82 at gmail.com
>> <mailto:shivipmp82 at gmail.com>> wrote:
>>
>> Sure Burt, i will share the data after masking it. it isn't big
>>
>> regards, Shivi
>>
>> On Fri, Aug 12, 2016 at 8:36 PM, Bert Gunter <bgunter.4567 at gmail.com
>> <mailto:bgunter.4567 at gmail.com>> wrote:
>>
>> 1. No, changing to factor will make no difference.
>>
>> 2. I think that most likely your problem is your model is not
>> estimable/your design matrix is singular. You should resolve
>> this by
>> consulting with a local statistical expert or, if your data set
>> is not
>> too large or confidential, posting your full dataset using
>> dput() (see
>> ?dput for how to do this).
>>
>> Cheers,
>> Bert
>> Bert Gunter
>>
>> "The trouble with having an open mind is that people keep coming
>> along
>> and sticking things into it."
>> -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
>>
>>
>> On Fri, Aug 12, 2016 at 7:58 AM, Shivi Bhatia
>> <shivipmp82 at gmail.com <mailto:shivipmp82 at gmail.com>> wrote:
>> > Hi Michael,
>> >
>> > There is no output as the model does not generate any
>> coefficients and
>> > simply throws this error.
>> >
>> > I hope you are not asking for a reproducible example.
>> >
>> > On Fri, Aug 12, 2016 at 7:30 PM, Michael Dewey
>> <lists at dewey.myzen.co.uk <mailto:lists at dewey.myzen.co.uk>>
>>
>> > wrote:
>> >
>> >> Dear Shivi
>> >>
>> >> Can you show us the output?
>> >>
>> >> And please do not post in HTML as it will mangle your post into
>> >> unreadability.
>> >>
>> >> On 12/08/2016 10:10, Shivi Bhatia wrote:
>> >>
>> >>> Hi Team,
>> >>>
>> >>> I am creating *my first* Logistic regression on R Studio. I
>> am working on
>> >>> a
>> >>>
>> >>> C-SAT data where rating (score) 0-8 is a dis-sat whereas
>> 9-10 are SAT. As
>> >>> these were in numeric form so i had as below created 2
>> classes:
>> >>>
>> >>> new$survey[new$score>=0 & new$score<=8]<- 0
>> >>> new$survey[new$score>=9]<- 1
>> >>> This works fine however the class still shows as "numeric"
>> and levels
>> >>> shows
>> >>> as "NULL". Do i still need to use "as.factor" to let R know
>> these are
>> >>> categorical variables.
>> >>>
>> >>> Also i have used the below code to run a logistic regression
>> with all the
>> >>> possible predictor variables:
>> >>> glm.fit= glm(survey ~ support_cat + region+ support_lvl+
>> skill_group+
>> >>> application_area+ functional_area+
>> >>> repS+ case_age+ case_status+ severity_level+
>> >>> sla_status+ delivery_segmentation, data = SFDC,
>> family =
>> >>> binomial)
>> >>>
>> >>> But it throws an error:-
>> >>> Warning messages:
>> >>> 1: glm.fit: algorithm did not converge
>> >>> 2: glm.fit: fitted probabilities numerically 0 or 1 occurred
>> >>>
>> >>> I checked online for the error and it says:
>> >>> "glm() uses an iterative re-weighted least squares
>> algorithm. The
>> >>> algorithm
>> >>> hit the maximum number of allowed iterations before signalling
>> >>> convergence.
>> >>> The default,
>> >>> documented in ?glm.control is 25."
>> >>>
>> >>> Kindly suggest on the above case and if i have to change my
>> outcome var as
>> >>> as.factor.
>> >>>
>> >>> Thank you, Shivi
>> >>>
>> >>> [[alternative HTML version deleted]]
>> >>>
>> >>> ______________________________________________
>> >>> R-help at r-project.org <mailto:R-help at r-project.org> mailing
>> list -- To UNSUBSCRIBE and more, see
>> >>> https://stat.ethz.ch/mailman/listinfo/r-help
>> <https://stat.ethz.ch/mailman/listinfo/r-help>
>> >>> PLEASE do read the posting guide
>> http://www.R-project.org/posti
>> >>> ng-guide.html
>> >>> and provide commented, minimal, self-contained, reproducible
>> code.
>> >>>
>> >>>
>> >> --
>> >> Michael
>> >> http://www.dewey.myzen.co.uk/home.html
>> <http://www.dewey.myzen.co.uk/home.html>
>> >>
>> >
>> > [[alternative HTML version deleted]]
>> >
>> > ______________________________________________
>> > R-help at r-project.org <mailto:R-help at r-project.org> mailing
>> list -- To UNSUBSCRIBE and more, see
>> > https://stat.ethz.ch/mailman/listinfo/r-help
>> <https://stat.ethz.ch/mailman/listinfo/r-help>
>> > PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> <http://www.R-project.org/posting-guide.html>
>> > and provide commented, minimal, self-contained, reproducible
>> code.
>>
>>
>>
>>
> --
> Michael
> http://www.dewey.myzen.co.uk/home.html
>
-------------- next part --------------
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3L, 3L, 3L, 3L, 2L, 5L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L,
3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 6L, 6L, 4L, 5L, 3L, 3L,
3L, 3L, 3L, 3L, 5L, 3L, 5L, 5L, 3L, 3L, 3L, 5L, 3L, 3L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 4L, 2L, 3L,
3L, 3L, 3L, 5L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 4L, 4L, 6L, 4L, 6L, 4L, 6L, 6L, 4L, 6L,
5L), .Label = c("", "a", "b", "c", "d", "e", "f", "g"), class = "factor"),
support_lvl = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L), .Label = c("", "basc12", "basic1", "Other"), class = "factor"),
skill_group = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 3L,
2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 3L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 3L, 2L,
2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 3L,
2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L,
3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 3L,
2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 3L,
2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 3L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L,
2L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L,
3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 2L,
3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 2L,
2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L,
3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L,
3L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 3L,
2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L,
2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 3L,
2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L,
3L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 3L, 2L, 3L,
3L, 3L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L,
2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 3L,
3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L,
3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 3L, 3L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 3L), .Label = c("", "one", "two"), class = "factor"),
application_area = structure(c(2L, 11L, 2L, 8L, 2L, 2L, 2L,
3L, 2L, 4L, 3L, 4L, 2L, 8L, 2L, 2L, 4L, 4L, 2L, 3L, 16L,
4L, 4L, 8L, 2L, 2L, 4L, 2L, 2L, 2L, 8L, 2L, 2L, 3L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 15L, 2L, 3L, 4L, 4L, 2L, 2L,
3L, 3L, 4L, 2L, 2L, 2L, 2L, 2L, 6L, 10L, 4L, 4L, 4L, 10L,
3L, 8L, 4L, 2L, 2L, 2L, 14L, 4L, 4L, 2L, 2L, 3L, 2L, 2L,
2L, 6L, 2L, 2L, 4L, 4L, 2L, 2L, 10L, 2L, 3L, 2L, 2L, 2L,
3L, 2L, 4L, 2L, 2L, 3L, 10L, 2L, 2L, 2L, 4L, 3L, 2L, 4L,
2L, 3L, 2L, 2L, 3L, 2L, 3L, 4L, 2L, 2L, 4L, 2L, 2L, 2L, 4L,
4L, 3L, 3L, 2L, 16L, 3L, 2L, 4L, 8L, 2L, 4L, 2L, 2L, 2L,
3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 4L, 3L, 2L, 4L, 2L, 2L, 2L,
2L, 3L, 2L, 4L, 2L, 4L, 14L, 4L, 2L, 3L, 8L, 4L, 3L, 2L,
2L, 2L, 2L, 12L, 2L, 2L, 3L, 2L, 2L, 2L, 4L, 3L, 4L, 4L,
2L, 2L, 4L, 2L, 4L, 16L, 2L, 2L, 2L, 2L, 4L, 2L, 2L, 2L,
2L, 2L, 3L, 2L, 11L, 4L, 2L, 2L, 2L, 2L, 4L, 2L, 4L, 2L,
3L, 2L, 3L, 2L, 8L, 3L, 4L, 3L, 2L, 2L, 2L, 4L, 3L, 8L, 3L,
2L, 4L, 3L, 4L, 10L, 2L, 8L, 2L, 4L, 2L, 2L, 13L, 3L, 2L,
2L, 4L, 2L, 2L, 2L, 4L, 13L, 2L, 14L, 4L, 2L, 3L, 4L, 2L,
2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 13L, 4L, 2L,
2L, 4L, 2L, 3L, 2L, 2L, 4L, 2L, 10L, 10L, 2L, 2L, 2L, 2L,
3L, 6L, 3L, 2L, 4L, 2L, 2L, 3L, 4L, 2L, 3L, 6L, 11L, 2L,
2L, 2L, 3L, 2L, 2L, 6L, 2L, 3L, 2L, 4L, 2L, 4L, 4L, 2L, 8L,
4L, 10L, 2L, 2L, 2L, 3L, 2L, 2L, 4L, 3L, 3L, 2L, 4L, 2L,
8L, 2L, 4L, 4L, 4L, 2L, 13L, 2L, 2L, 2L, 2L, 2L, 2L, 4L,
2L, 2L, 4L, 2L, 4L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 16L, 13L,
4L, 4L, 2L, 4L, 2L, 13L, 2L, 8L, 4L, 2L, 2L, 2L, 4L, 2L,
3L, 2L, 2L, 2L, 8L, 3L, 3L, 4L, 2L, 4L, 2L, 15L, 2L, 10L,
10L, 2L, 2L, 2L, 3L, 10L, 3L, 8L, 10L, 3L, 3L, 4L, 4L, 2L,
16L, 3L, 2L, 4L, 2L, 2L, 2L, 14L, 4L, 2L, 3L, 3L, 3L, 16L,
8L, 2L, 4L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 4L, 2L, 8L,
2L, 2L, 3L, 2L, 2L, 4L, 2L, 2L, 3L, 4L, 4L, 3L, 16L, 8L,
3L, 3L, 4L, 3L, 2L, 16L, 7L, 2L, 2L, 2L, 4L, 4L, 3L, 2L,
2L, 4L, 2L, 4L, 2L, 4L, 4L, 2L, 2L, 2L, 4L, 2L, 2L, 2L, 17L,
4L, 2L, 2L, 16L, 3L, 2L, 13L, 2L, 4L, 2L, 2L, 3L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 4L, 3L, 2L, 2L,
2L, 10L, 4L, 4L, 2L, 2L, 2L, 3L, 8L, 3L, 3L, 4L, 2L, 2L,
10L, 4L, 2L, 3L, 3L, 2L, 4L, 4L, 2L, 4L, 2L, 2L, 14L, 2L,
2L, 6L, 6L, 4L, 4L, 2L, 2L, 2L, 4L, 3L, 3L, 4L, 3L, 3L, 2L,
2L, 2L, 2L, 4L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 4L, 2L, 2L,
2L, 3L, 4L, 2L, 10L, 2L, 2L, 3L, 4L, 12L, 2L, 3L, 4L, 4L,
10L, 3L, 4L, 4L, 2L, 6L, 2L, 2L, 10L, 4L, 3L, 2L, 4L, 8L,
8L, 3L, 2L, 2L, 3L, 4L, 4L, 10L, 11L, 2L, 10L, 4L, 2L, 11L,
2L, 16L, 4L, 3L, 2L, 3L, 2L, 2L, 3L, 4L, 3L, 2L, 2L, 2L,
2L, 3L, 14L, 2L, 2L, 2L, 4L, 2L, 8L, 4L, 2L, 4L, 2L, 2L,
2L, 4L, 2L, 4L, 4L, 4L, 2L, 15L, 2L, 4L, 3L, 4L, 2L, 4L,
4L, 3L, 2L, 2L, 2L, 8L, 2L, 2L, 2L, 6L, 2L, 3L, 2L, 2L, 15L,
10L, 16L, 2L, 4L, 2L, 3L, 4L, 13L, 3L, 2L, 3L, 3L, 3L, 3L,
8L, 2L, 4L, 2L, 3L, 2L, 2L, 3L, 2L, 2L, 4L, 15L, 4L, 2L,
2L, 4L, 2L, 10L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L,
2L, 8L, 2L, 3L, 2L, 4L, 10L, 2L, 2L, 16L, 12L, 2L, 2L, 2L,
2L, 6L, 6L, 4L, 2L, 2L, 2L, 2L, 4L, 4L, 3L, 2L, 2L, 2L, 2L,
3L, 2L, 2L, 2L, 2L, 2L, 2L, 10L, 2L, 3L, 10L, 10L, 3L, 2L,
10L, 2L, 4L, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 6L, 2L, 2L, 4L,
2L, 4L, 15L, 8L, 2L, 2L, 2L, 2L, 4L, 3L, 4L, 8L, 2L, 2L,
2L, 3L, 2L, 2L, 2L, 3L, 4L, 2L, 4L, 2L, 2L, 3L, 4L, 2L, 4L,
2L, 2L, 2L, 3L, 8L, 8L, 2L, 3L, 4L, 4L, 10L, 2L, 2L, 11L,
2L, 2L, 2L, 3L, 2L, 4L, 13L, 4L, 2L, 2L, 2L, 4L, 4L, 10L,
3L, 14L, 2L, 8L, 2L, 4L, 16L, 4L, 2L, 13L, 2L, 3L, 3L, 2L,
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2L, 2L, 4L, 3L, 3L, 4L, 2L, 2L, 2L, 2L, 2L, 6L, 3L, 3L, 6L,
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3L, 2L, 3L, 2L, 14L, 2L, 16L, 2L, 10L, 2L, 3L, 2L, 13L, 4L,
2L, 2L, 4L, 2L, 2L, 3L, 2L, 2L, 3L, 4L, 2L, 3L, 3L, 2L, 4L,
4L, 2L, 2L, 2L, 2L, 10L, 8L, 2L, 2L, 2L, 4L), .Label = c("",
"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l",
"m", "n", "o", "p"), class = "factor"), functional_area = structure(c(6L,
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23L, 24L, 52L, 46L, 22L, 24L, 45L, 6L, 2L, 6L, 44L, 24L,
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29L, 18L, 24L, 24L, 18L, 9L, 52L, 20L, 54L, 62L, 20L, 8L,
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52L, 17L, 18L, 24L, 24L, 62L, 24L, 18L, 24L, 24L, 28L, 6L,
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24L, 6L, 24L, 18L, 18L, 24L, 18L, 67L, 24L, 24L, 22L, 2L,
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44L, 6L, 20L, 29L, 44L, 24L, 6L, 57L), .Label = c("", ".COM",
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"APP", "AUTH", "B", "bbb", "BROWSER", "BULK", "C", "Canvas",
"CCC", "cccc", "cert", "Change Sets", "CODE", "Community",
"COMMUNITY", "CONNECT", "DATABASE", "DEPLOYMENT", "DESIGN",
"DEV", "Development", "DOMAIN", "Email", "hello", "hmm",
"IDE", "ISP", "LLM", "LOG", "maintain", "manager", "message",
"NEW", "NEW1", "new11", "new11111", "New2", "new3", "no",
"Oauth", "on", "open", "Other", "post", "quick", "REST",
"SAML", "secure", "Site.com", "SLOWNES", "soo", "SOSL", "sql",
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10L, 10L, 10L, 6L, 10L, 10L, 10L, 10L, 10L, 1L, 9L, 7L, 10L,
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10L, 8L, 8L, 10L, 10L, 8L, 8L, 10L, 4L, 10L, 8L, 10L, 9L,
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10L, 10L, 10L, 10L, 9L, 10L, 6L, 7L, 10L, 10L, 10L, 10L,
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10L, 10L, 10L, 8L, 9L, 10L, 10L, 1L, 10L, 9L, 10L, 10L, 10L,
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10L, 10L, 0L, 0L, 10L, 8L, 8L, 10L, 10L, 8L, 9L, 9L, 10L,
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8L, 10L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L,
10L, 10L, 10L, 10L, 10L, 9L, 9L, 7L, 8L, 10L, 8L, 10L, 8L,
10L, 8L, 7L, 10L, 10L, 8L, 9L, 10L, 10L, 9L, 10L, 9L, 10L,
10L, 10L, 9L, 10L, 9L, 9L, 10L, 10L, 8L, 9L, 10L, 10L, 0L,
10L, 10L, 10L, 9L, 10L, 10L, 9L, 10L, 10L, 10L, 6L, 10L,
10L, 10L, 10L, 10L, 9L, 4L, 9L, 10L, 1L, 9L, 9L, 10L, 7L,
9L, 10L, 10L, 10L, 10L, 9L, 10L, 9L, 10L, 2L, 10L, 9L, 7L,
9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 1L), rep_score = c(9.5,
10, 2, 10, 10, 3.5, 7.5, 10, 10, 10, 10, 10, 10, 2, 10, 7.5,
6, 10, 9.5, 10, 9, 10, 10, 5.5, 9, 10, 8, 10, 10, 10, 10,
10, 9.5, 1.5, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 9.5,
10, 10, 8, 10, 10, 9.5, 6, 9, 9, 10, 10, 7.5, 10, 10, 10,
7.5, 10, 10, 8, 9, 10, 10, 10, 10, 10, 10, 7.5, 7.5, 7.5,
9, 10, 10, 10, 10, 7.5, 10, 9, 7.5, 8, 10, 10, 10, 7.5, 9.5,
10, 9.5, 10, 10, 8.5, 10, 9, 9.5, 9.5, 8, 10, 10, 9, 9, 10,
10, 9, 10, 10, 10, 10, 9.5, 2, 10, 9, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 8, 10, 9.3, 10, 10, 7.5, 9.3,
8, 9, 10, 10, 9.5, 6.5, 7.5, 6, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 6, 7, 8.5, 9.5, 9.5, 5, 7.5,
10, 10, 10, 8, 10, 6, 8, 6.5, 10, 10, 10, 9, 10, 10, 8, 8.5,
10, 10, 10, 7.5, 9, 8, 10, 10, 10, 10, 10, 10, 9, 7, 10,
10, 10, 9, 7.5, 9.5, 10, 9, 10, 10, 10, 8, 9.3, 10, 9, 10,
10, 10, 9.5, 10, 10, 8, 9, 9, 10, 10, 10, 8, 10, 10, 9.5,
10, 9, 7.5, 10, 10, 10, 8.5, 10, 9.5, 10, 10, 10, 10, 10,
10, 9, 10, 10, 10, 10, 10, 5, 10, 10, 10, 10, 10, 4, 9, 7,
10, 4, 8.5, 8.5, 6.5, 7.5, 8.5, 10, 10, 10, 10, 8.5, 8, 9,
10, 9, 8, 10, 10, 8, 10, 10, 8.5, 10, 8, 10, 9, 9.5, 10,
9, 7.5, 10, 4.5, 9.5, 8, 10, 10, 9.5, 8.5, 8, 10, 10, 10,
10, 10, 10, 9, 10, 9, 10, 9.5, 8, 8, 10, 10, 9.5, 10, 8.5,
10, 9, 10, 10, 7.5, 7, 10, 9.5, 10, 10, 10, 7, 9.5, 10, 10,
10, 10, 4, 10, 10, 10, 10, 7, 10, 10, 10, 10, 9.3, 10, 10,
10, 10, 10, 10, 9, 10, 10, 10, 10, 10, 10, 7.5, 10, 10, 9,
6, 10, 10, 9.5, 10, 8.5, 10, 10, 10, 10, 10, 10, 10, 9.5,
9.5, 10, 9, 10, 9.5, 10, 10, 10, 10, 10, 10, 10, 10, 10,
9.5, 9, 10, 6, 9, 10, 10, 9.5, 10, 10, 10, 9.5, 10, 8, 10,
10, 10, 8.5, 10, 9.5, 10, 10, 10, 10, 8, 10, 10, 10, 4.5,
10, 10, 10, 10, 10, 9, 9, 10, 6, 10, 9.5, 8, 10, 10, 10,
9, 10, 10, 10, 9.5, 7, 9.3, 10, 10, 8.5, 10, 10, 8, 10, 9.5,
10, 10, 10, 10, 9.5, 5.5, 8, 10, 7.5, 10, 7.5, 10, 8, 10,
10, 8, 8.5, 9.3, 8, 8, 10, 10, 10, 9.5, 1.5, 8, 10, 7, 10,
10, 10, 10, 10, 10, 10, 7.5, 8, 10, 10, 10, 10, 10, 10, 10,
10, 7.5, 10, 10, 10, 10, 7.5, 10, 10, 10, 8, 9, 9, 10, 10,
9, 10, 9.5, 10, 10, 10, 10, 10, 10, 9, 10, 10, 10, 10, 8,
10, 10, 8.5, 8, 10, 10, 10, 10, 10, 10, 10, 10, 10, 7, 7,
9, 10, 9, 10, 8, 10, 10, 7.5, 10, 10, 10, 10, 10, 6.5, 10,
10, 10, 9.5, 10, 10, 9, 10, 10, 10, 9, 10, 9.5, 10, 10, 10,
10, 10, 10, 10, 9.5, 10, 9, 10, 9, 10, 9, 10, 10, 10, 9.5,
7.5, 10, 10, 10, 4.5, 10, 9, 9.5, 10, 8, 10, 1, 10, 8, 8,
10, 10, 10, 10, 10, 10, 10, 8, 3.5, 10, 10, 8.5, 10, 9.5,
10, 10, 10, 3, 10, 10, 10, 10, 10, 10, 10, 10, 5, 10, 10,
10, 8, 7, 10, 10, 9, 5.5, 8, 5, 5, 9, 10, 10, 8, 9, 10, 9,
10, 10, 10, 9.5, 10, 10, 10, 10, 10, 1, 7.5, 10, 9, 10, 9.3,
10, 10, 10, 8.5, 10, 10, 10, 10, 9, 10, 10, 8, 7.5, 8, 10,
8.5, 10, 9, 8.5, 7.5, 8.5, 10, 9, 10, 10, 3.5, 10, 10, 10,
10, 10, 6, 10, 8, 10, 10, 10, 10, 10, 10, 10, 8, 8.5, 9,
9, 8, 8.5, 5.5, 7.5, 10, 10, 10, 10, 9, 10, 9.5, 8, 5, 10,
10, 10, 10, 8, 10, 9, 10, 10, 10, 9, 10, 10, 10, 10, 10,
10, 10, 5.5, 10, 10, 9, 4.5, 10, 10, 10, 10, 10, 9.5, 10,
10, 10, 9, 10, 10, 10, 9.5, 10, 10, 10, 10, 10, 10, 9.5,
10, 10, 8.5, 10, 8.5, 10, 10, 10, 9.5, 10, 8, 10, 9.5, 9.5,
10, 9.5, 10, 10, 10, 10, 10, 10, 5.5, 8, 10, 10, 9, 10, 10,
10, 10, 10, 10, 8.5, 9.5, 10, 8.5, 10, 5, 10, 9.5, 9.5, 10,
10, 10, 8, 10, 10, 10, 8.5, 10, 10, 10, 10, 10, 6, 10, 10,
7.5, 8.5, 10, 10, 10, 10, 10, 10, 10, 10, 9, 10, 8.5, 8,
9.5, 10, 10, 9.3, 9.3, 8, 9, 10, 10, 8, 10, 8, 10, 10, 10,
10, 8.5, 10, 10, 10, 5, 10, 9, 10, 10, 9, 9.3, 10, 10, 10,
10, 2.5, 9, 10, 10, 2, 9.5, 10, 8, 10, 10, 10, 10, 10, 9,
8.5, 5.5, 10, 8, 9.5, 10, 10, 6, 10, 10, 10, 9.5, 10, 9.5,
6.5, 10, 10, 10, 10, 10, 10, 10, 10, 6, 9.3, 8, 8.5, 9, 10,
9.5, 10, 10, 10, 10, 10, 10, 10, 10, 9, 10, 10, 10, 10, 10,
9.5, 9.5, 8.5, 9, 10, 8, 10, 9, 10, 10, 9, 10, 10, 8.5, 9.5,
10, 10, 9.5, 10, 9, 8, 10, 10, 9, 10, 8, 9, 10, 10, 6, 9.5,
9.5, 10, 9.3, 10, 10, 10, 9, 10, 10, 10, 9.5, 10, 10, 6,
10, 10, 10, 10, 10, 9, 4.5, 8.5, 10, 5, 9, 9, 10, 6, 9, 10,
9.5, 9.5, 10, 9.5, 10, 7.5, 10, 3, 10, 9.5, 10, 9, 9, 9.5,
10, 10, 10, 10, 10, 10, 10, 2), product_know = structure(c(5L,
5L, 6L, 5L, 2L, 4L, 12L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 5L,
11L, 10L, 5L, 5L, 5L, 12L, 5L, 5L, 10L, 13L, 5L, 12L, 5L,
5L, 5L, 5L, 5L, 13L, 4L, 13L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 12L, 5L, 5L, 5L, 10L, 13L, 13L, 5L, 5L,
12L, 5L, 5L, 5L, 12L, 5L, 5L, 13L, 13L, 5L, 5L, 5L, 5L, 5L,
5L, 12L, 9L, 13L, 13L, 5L, 5L, 5L, 5L, 12L, 5L, 13L, 11L,
12L, 5L, 5L, 5L, 11L, 13L, 5L, 13L, 5L, 5L, 12L, 5L, 12L,
13L, 13L, 12L, 5L, 5L, 13L, 13L, 5L, 5L, 13L, 5L, 5L, 5L,
5L, 13L, 6L, 5L, 13L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 12L, 5L, 3L, 5L, 5L, 11L, 3L, 12L, 13L, 5L, 5L,
13L, 10L, 13L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 10L, 11L, 12L, 5L, 5L, 10L, 11L, 5L,
5L, 5L, 12L, 5L, 10L, 12L, 9L, 5L, 5L, 5L, 13L, 5L, 5L, 12L,
13L, 5L, 5L, 5L, 12L, 13L, 12L, 5L, 5L, 5L, 5L, 5L, 5L, 13L,
11L, 5L, 5L, 5L, 12L, 12L, 13L, 5L, 13L, 5L, 5L, 5L, 12L,
3L, 5L, 12L, 5L, 5L, 5L, 13L, 5L, 5L, 12L, 5L, 13L, 5L, 5L,
5L, 12L, 5L, 5L, 13L, 5L, 5L, 13L, 5L, 5L, 5L, 11L, 5L, 5L,
5L, 5L, 5L, 5L, 2L, 5L, 12L, 5L, 5L, 5L, 5L, 5L, 12L, 5L,
5L, 5L, 5L, 5L, 9L, 13L, 11L, 5L, 8L, 13L, 13L, 10L, 11L,
12L, 5L, 5L, 5L, 5L, 13L, 12L, 13L, 5L, 13L, 12L, 5L, 5L,
12L, 5L, 5L, 11L, 5L, 2L, 5L, 13L, 13L, 5L, 5L, 12L, 5L,
9L, 13L, 12L, 5L, 5L, 13L, 13L, 10L, 5L, 5L, 5L, 5L, 5L,
5L, 13L, 5L, 13L, 5L, 13L, 11L, 12L, 5L, 5L, 5L, 5L, 13L,
5L, 13L, 5L, 5L, 13L, 12L, 5L, 13L, 5L, 5L, 5L, 12L, 13L,
5L, 5L, 5L, 5L, 7L, 5L, 5L, 5L, 5L, 12L, 5L, 5L, 5L, 5L,
3L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 11L,
5L, 5L, 13L, 10L, 5L, 5L, 13L, 5L, 12L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 13L, 5L, 13L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 13L, 5L, 10L, 12L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 12L, 5L, 5L, 5L, 13L, 5L, 13L, 5L, 5L, 5L, 5L, 2L,
5L, 5L, 5L, 10L, 5L, 5L, 5L, 5L, 5L, 13L, 13L, 5L, 10L, 5L,
13L, 12L, 5L, 5L, 5L, 13L, 5L, 5L, 5L, 13L, 10L, 3L, 5L,
5L, 13L, 5L, 5L, 12L, 5L, 13L, 5L, 5L, 2L, 5L, 13L, 13L,
13L, 5L, 12L, 5L, 12L, 5L, 12L, 5L, 5L, 12L, 13L, 3L, 12L,
12L, 5L, 5L, 5L, 13L, 6L, 12L, 5L, 12L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 12L, 12L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 10L, 5L,
5L, 5L, 5L, 11L, 5L, 5L, 5L, 12L, 13L, 12L, 5L, 5L, 13L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 13L, 5L, 5L, 5L, 5L, 12L,
5L, 5L, 5L, 12L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 2L, 12L,
11L, 13L, 5L, 13L, 5L, 12L, 5L, 5L, 12L, 5L, 5L, 5L, 5L,
5L, 10L, 5L, 5L, 5L, 13L, 5L, 5L, 13L, 5L, 5L, 5L, 13L, 5L,
13L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 13L, 5L, 13L, 5L,
13L, 5L, 5L, 5L, 13L, 11L, 5L, 5L, 5L, 10L, 5L, 13L, 5L,
5L, 12L, 5L, 3L, 5L, 12L, 12L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
12L, 6L, 5L, 5L, 13L, 5L, 5L, 5L, 5L, 5L, 7L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 9L, 5L, 5L, 5L, 12L, 11L, 5L, 5L, 13L,
7L, 10L, 9L, 10L, 13L, 5L, 5L, 12L, 12L, 5L, 13L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 12L, 5L, 13L, 5L, 3L, 5L,
5L, 5L, 12L, 5L, 5L, 5L, 5L, 13L, 5L, 5L, 12L, 11L, 12L,
5L, 12L, 5L, 13L, 13L, 11L, 13L, 5L, 13L, 5L, 5L, 10L, 5L,
5L, 5L, 5L, 5L, 10L, 5L, 12L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
12L, 13L, 13L, 13L, 12L, 13L, 9L, 12L, 5L, 5L, 5L, 5L, 13L,
5L, 13L, 13L, 9L, 5L, 5L, 5L, 5L, 12L, 5L, 13L, 5L, 5L, 5L,
13L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 8L, 5L, 5L, 13L, 11L, 5L,
5L, 5L, 5L, 5L, 13L, 5L, 5L, 5L, 12L, 5L, 5L, 5L, 13L, 5L,
5L, 5L, 5L, 5L, 5L, 13L, 5L, 5L, 13L, 5L, 12L, 5L, 5L, 5L,
5L, 5L, 12L, 5L, 13L, 5L, 5L, 13L, 5L, 5L, 5L, 5L, 5L, 5L,
9L, 12L, 5L, 5L, 13L, 5L, 5L, 5L, 5L, 5L, 5L, 13L, 13L, 5L,
12L, 5L, 10L, 5L, 5L, 5L, 5L, 5L, 5L, 12L, 5L, 5L, 5L, 12L,
5L, 5L, 5L, 5L, 5L, 10L, 5L, 5L, 11L, 11L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 13L, 5L, 13L, 12L, 13L, 5L, 5L, 3L, 3L, 13L,
13L, 5L, 5L, 12L, 5L, 12L, 5L, 5L, 5L, 5L, 12L, 5L, 5L, 5L,
10L, 5L, 5L, 5L, 5L, 13L, 3L, 5L, 5L, 5L, 5L, 6L, 13L, 5L,
5L, 6L, 5L, 5L, 12L, 5L, 5L, 5L, 5L, 5L, 13L, 13L, 7L, 5L,
13L, 13L, 5L, 5L, 6L, 5L, 5L, 5L, 13L, 5L, 5L, 11L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 10L, 3L, 12L, 13L, 13L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 13L, 5L, 5L, 5L, 5L, 5L, 13L,
5L, 11L, 13L, 5L, 12L, 5L, 13L, 5L, 5L, 13L, 5L, 5L, 12L,
13L, 5L, 5L, 5L, 5L, 12L, 5L, 5L, 5L, 13L, 5L, 13L, 12L,
5L, 5L, 10L, 5L, 13L, 5L, 3L, 5L, 5L, 5L, 13L, 5L, 5L, 5L,
5L, 5L, 5L, 10L, 5L, 5L, 5L, 5L, 5L, 13L, 9L, 13L, 5L, 8L,
13L, 13L, 5L, 10L, 13L, 5L, 13L, 5L, 5L, 13L, 5L, 11L, 5L,
6L, 5L, 13L, 5L, 13L, 13L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
7L), .Label = c("", "-", "0", "1", "10", "2", "3", "4", "5",
"6", "7", "8", "9"), class = "factor"), understanding_issue = structure(c(13L,
5L, 6L, 5L, 5L, 10L, 11L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 5L,
12L, 10L, 5L, 13L, 5L, 5L, 5L, 5L, 9L, 13L, 5L, 12L, 2L,
5L, 5L, 5L, 5L, 5L, 6L, 13L, 12L, 5L, 5L, 5L, 5L, 5L, 5L,
2L, 5L, 13L, 5L, 5L, 12L, 5L, 5L, 13L, 10L, 13L, 13L, 5L,
5L, 11L, 5L, 5L, 5L, 11L, 5L, 5L, 11L, 13L, 5L, 5L, 5L, 5L,
5L, 5L, 11L, 5L, 10L, 13L, 5L, 5L, 5L, 5L, 11L, 5L, 13L,
12L, 12L, 5L, 5L, 5L, 12L, 5L, 5L, 5L, 5L, 5L, 13L, 5L, 5L,
5L, 5L, 12L, 5L, 5L, 13L, 13L, 5L, 5L, 13L, 5L, 5L, 5L, 5L,
5L, 6L, 5L, 13L, 5L, 2L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 12L, 5L, 3L, 5L, 2L, 12L, 3L, 12L, 13L, 5L, 5L, 5L,
11L, 10L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 2L, 5L, 10L, 2L, 13L, 13L, 13L, 8L, 12L, 5L, 5L,
2L, 12L, 5L, 10L, 12L, 12L, 5L, 5L, 5L, 13L, 5L, 5L, 12L,
12L, 5L, 5L, 5L, 11L, 13L, 12L, 5L, 5L, 5L, 5L, 5L, 5L, 13L,
11L, 5L, 5L, 5L, 5L, 11L, 5L, 5L, 13L, 5L, 5L, 5L, 12L, 3L,
5L, 5L, 2L, 5L, 5L, 5L, 5L, 5L, 12L, 12L, 13L, 5L, 5L, 5L,
12L, 5L, 5L, 5L, 5L, 12L, 10L, 5L, 5L, 5L, 5L, 5L, 13L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 5L, 5L, 5L,
5L, 5L, 7L, 13L, 11L, 5L, 8L, 12L, 12L, 11L, 12L, 13L, 5L,
5L, 5L, 5L, 12L, 12L, 13L, 5L, 13L, 12L, 5L, 5L, 12L, 5L,
5L, 5L, 5L, 12L, 5L, 13L, 5L, 5L, 12L, 11L, 5L, 8L, 5L, 12L,
5L, 5L, 5L, 12L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 13L, 5L, 13L,
5L, 5L, 13L, 2L, 5L, 5L, 13L, 5L, 12L, 5L, 13L, 5L, 5L, 10L,
10L, 5L, 5L, 5L, 5L, 5L, 10L, 5L, 5L, 5L, 5L, 5L, 9L, 5L,
5L, 5L, 5L, 10L, 5L, 5L, 5L, 5L, 3L, 5L, 5L, 5L, 5L, 5L,
5L, 12L, 5L, 5L, 5L, 5L, 5L, 5L, 12L, 5L, 5L, 13L, 3L, 5L,
5L, 5L, 5L, 13L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 13L, 5L, 2L,
13L, 5L, 13L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 13L, 13L,
5L, 10L, 5L, 5L, 5L, 13L, 5L, 5L, 5L, 13L, 5L, 2L, 2L, 5L,
5L, 12L, 5L, 5L, 5L, 5L, 5L, 5L, 12L, 5L, 5L, 5L, 7L, 5L,
5L, 5L, 5L, 5L, 13L, 2L, 5L, 10L, 5L, 5L, 12L, 5L, 5L, 5L,
13L, 5L, 5L, 5L, 5L, 12L, 3L, 5L, 5L, 12L, 5L, 5L, 12L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 6L, 11L, 5L, 11L, 5L, 11L, 5L, 12L,
5L, 2L, 12L, 12L, 3L, 12L, 12L, 5L, 5L, 5L, 5L, 4L, 12L,
5L, 10L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 11L, 12L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 13L, 5L, 5L, 5L, 5L, 12L, 2L, 5L, 5L,
12L, 13L, 5L, 5L, 5L, 13L, 5L, 13L, 2L, 5L, 5L, 5L, 5L, 5L,
13L, 5L, 5L, 5L, 5L, 12L, 5L, 5L, 11L, 12L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 10L, 11L, 13L, 5L, 13L, 5L, 12L, 5L,
5L, 11L, 5L, 5L, 5L, 5L, 5L, 11L, 5L, 5L, 5L, 5L, 5L, 5L,
13L, 5L, 5L, 5L, 13L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
13L, 5L, 13L, 5L, 13L, 5L, 13L, 5L, 5L, 2L, 5L, 12L, 5L,
5L, 5L, 7L, 5L, 13L, 13L, 5L, 12L, 5L, 4L, 5L, 12L, 12L,
5L, 5L, 2L, 5L, 5L, 5L, 5L, 12L, 9L, 5L, 5L, 12L, 5L, 13L,
5L, 5L, 5L, 7L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 9L, 5L, 5L,
5L, 12L, 11L, 5L, 5L, 13L, 12L, 5L, 9L, 8L, 13L, 5L, 5L,
12L, 5L, 5L, 13L, 5L, 5L, 5L, 13L, 5L, 5L, 5L, 5L, 5L, 4L,
11L, 5L, 13L, 5L, 3L, 5L, 5L, 5L, 13L, 5L, 5L, 5L, 5L, 13L,
5L, 5L, 12L, 12L, 12L, 5L, 13L, 5L, 13L, 12L, 12L, 12L, 5L,
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5L, 5L, 5L, 5L, 5L, 12L, 12L, 13L, 13L, 12L, 12L, 10L, 11L,
5L, 5L, 5L, 5L, 13L, 5L, 5L, 11L, 9L, 5L, 5L, 5L, 5L, 12L,
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5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 12L,
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12L, 5L, 10L, 13L, 13L, 5L, 10L, 13L, 5L, 5L, 13L, 2L, 5L,
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5L, 5L, 5L, 4L), .Label = c("", "-", "0", "1", "10", "2",
"3", "4", "5", "6", "7", "8", "9"), class = "factor"), case_age = c(24.84,
0.05, 13.38, 0.15, 11.11, 4.16, 8.13, 0.07, 3.61, 0, 3.11,
20.94, 0.21, 17.49, 1.11, 6.15, 4.32, 4.03, 0.08, 3.06, 4.74,
12.07, 4.79, 5.29, 0.21, 0.06, 3.95, 0.12, 7.27, 4.18, 2.49,
20.95, 0.15, 10.96, 6.99, 47.42, 4.96, 0.06, 4.92, 0.06,
6.84, 0.3, 0.01, 0.07, 15.74, 5.8, 2.85, 0.17, 16.02, 1.33,
7.91, 5.95, 1.48, 14.7, 1.98, 0.07, 12.89, 0.09, 0.11, 6.96,
0.19, 6.23, 5.62, 6.81, 6.98, 0.03, 0.12, 9.21, 0.8, 8.93,
1.87, 6.01, 0.34, 28.06, 16.36, 0.74, 5.82, 2.23, 0.18, 1.21,
0.06, 22.21, 1.97, 0.88, 0.21, 0.86, 6.02, 8.9, 1.75, 0.1,
1.15, 0.01, 0.17, 0.03, 7.99, 0.08, 0.05, 2.92, 1.02, 0.1,
0.86, 18.07, 0.06, 10.22, 1.1, 1.25, 0.03, 0.03, 0.96, 0.88,
0.96, 16.42, 1.06, 10.11, 0.97, 0.02, 0.12, 2.12, 7.29, 0.06,
0.27, 10.01, 1.96, 0.27, 0.04, 2.1, 9.34, 2.05, 22.29, 0,
6.98, 9.17, 15.55, 31.06, 0.06, 2.96, 11.04, 0.14, 17.36,
24.16, 41.89, 0.07, 0.01, 0.09, 5.1, 3.26, 1.85, 19.09, 10.88,
3.05, 0.87, 0.01, 2.78, 26.05, 14.23, 19.27, 0.09, 4.16,
3.77, 1.84, 11.06, 12.54, 3.26, 0.19, 0.15, 3.89, 19.85,
0.04, 13.18, 2.05, 0.89, 1.04, 0.09, 20.2, 0.82, 0.94, 4.99,
4.15, 0.04, 5.84, 15.75, 0.78, 1.92, 4.28, 6.08, 0.15, 83.05,
13.9, 2.75, 2.05, 0.02, 4.14, 6.72, 11.12, 1.82, 0.79, 0.12,
7.91, 0.16, 2.07, 2.1, 0.09, 0.14, 2.04, 2.22, 1.01, 0.06,
1.07, 1.98, 2.42, 6.39, 0.52, 6.93, 1.28, 2.02, 3.74, 34.86,
0.01, 7.15, 2.97, 1.15, 0.17, 0.18, 7.25, 2.98, 1.11, 85.99,
2.34, 0.67, 45.13, 1.03, 0.04, 0.13, 9.87, 7.03, 4.29, 0.1,
0.04, 0.07, 8.26, 0.2, 1.21, 1.81, 0.12, 1.12, 20.28, 0.05,
18.29, 2.78, 15.36, 4, 6.12, 0.04, 9.14, 0, 7.1, 19.07, 4.04,
3.9, 4.01, 11.22, 0, 0.09, 2.1, 3.05, 3.86, 15.21, 0.54,
0.01, 31.19, 2.17, 4.07, 20.07, 3.17, 14.38, 0.27, 13.89,
3.29, 2.82, 0.01, 2.58, 5.15, 9.85, 1.06, 14.24, 3.05, 5.96,
0.05, 0.01, 0.04, 0.07, 0.16, 49.02, 10.89, 4.91, 0.02, 5.31,
0.01, 0.02, 0.04, 3.99, 1.02, 6.18, 1.15, 6.18, 27.18, 5.16,
3.29, 3.97, 4.79, 0, 19.92, 0, 18.3, 11.01, 0.01, 4.09, 6.44,
10.98, 13.02, 0.1, 2.04, 1.3, 7.22, 1.82, 7.02, 0.91, 0.31,
0.95, 0.01, 12.09, 0.04, 0.84, 0.91, 28.75, 0.99, 1.12, 0.22,
5.12, 26.98, 1.01, 1.27, 1.29, 7.16, 0.07, 0.05, 1.01, 2.35,
0.17, 0.4, 0.89, 2.26, 6.1, 0.89, 1.16, 0.07, 5.69, 9.1,
1.88, 0.03, 15.21, 1.76, 14.14, 15.33, 1.02, 13.18, 0.09,
19.76, 1.99, 8.79, 2.81, 0.73, 0.24, 22.85, 0.12, 0.2, 0.18,
0.84, 20.92, 15.8, 0.04, 21.58, 0.04, 1.06, 1.06, 15.76,
9.29, 16.2, 0.09, 2.81, 10.82, 0.02, 41.96, 1.17, 0.35, 1.99,
4.93, 0.14, 0.95, 16.02, 4.05, 0.09, 0.02, 2.05, 2.02, 3.01,
0.02, 8.05, 0.17, 3.08, 0.99, 0.91, 1.31, 7.13, 1.05, 9.14,
36.25, 3.92, 3, 2.02, 11.14, 17.04, 1.08, 5.85, 4.27, 29.07,
25.07, 2.74, 24.28, 15.11, 0.85, 0.97, 38.02, 0.08, 102.44,
19.36, 3.77, 3.32, 0.04, 3.01, 3.86, 8.27, 7.11, 11.95, 1.21,
3.33, 0.15, 0, 4.68, 4.16, 12.94, 0.79, 7.81, 5.82, 6.29,
0.96, 13.75, 0.89, 0.97, 35.83, 1.03, 9.24, 0.08, 41.18,
0.79, 0.07, 1.19, 6.96, 6.9, 3.25, 2.28, 8.81, 1.21, 0.04,
5.94, 0.04, 3.95, 3.72, 1.72, 3.73, 0.12, 8.52, 41.09, 0.74,
0.02, 15.16, 2.03, 0.03, 4.1, 4.3, 0.01, 2.05, 0.02, 154.08,
6.11, 1.04, 1.01, 0.17, 2.43, 8.18, 77.15, 0.09, 4.97, 6.04,
44.74, 0.08, 6.94, 2.39, 2.11, 1.13, 5.33, 4.88, 2.78, 8.07,
1.63, 0, 0.18, 24.09, 0.04, 1.08, 7.91, 2.11, 1.32, 6.86,
8.04, 1.12, 21.89, 1.22, 18.03, 0.98, 1.8, 9.34, 8.25, 2.87,
0.03, 6.12, 27.89, 1.78, 3.82, 0, 21.36, 5.86, 0.01, 0.01,
0.02, 0.07, 38.29, 89.9, 4.99, 4, 6.14, 0.02, 0, 6.06, 5.44,
0.01, 0.07, 0.03, 27.24, 0, 4.22, 0.04, 5.98, 0.02, 10.12,
95.08, 0.11, 0.17, 0.08, 12.87, 6.31, 10.89, 0.25, 0, 1.08,
0.26, 0, 61.47, 0.21, 3.92, 6.18, 11.4, 11.27, 0.97, 0.08,
1.17, 7.73, 7.02, 0.08, 1.43, 0.26, 1.14, 21.26, 0.01, 0.3,
1.22, 0.01, 7.24, 7.4, 0.07, 1.86, 15.29, 5.43, 1.05, 4.2,
0.04, 6.12, 6.96, 0.05, 0.01, 78.46, 25.93, 1.8, 0.04, 1.58,
8.24, 22.1, 6.67, 43.13, 2.77, 4.29, 23.01, 3.05, 0.17, 70.22,
23.22, 0.07, 2.07, 1.89, 13.61, 21.64, 3.74, 0.18, 18.29,
12.23, 8.09, 7.01, 0.92, 8.86, 9.15, 15.14, 8.16, 1.27, 0.85,
0.06, 0.24, 2.11, 5.3, 78.17, 1.81, 0.13, 0.37, 0.01, 20.66,
49.08, 3.73, 2.77, 0.32, 0.12, 36.77, 0.03, 21.49, 5.65,
4.74, 26.99, 0.04, 12.56, 2.25, 4.24, 1.87, 5.32, 49.12,
1.99, 3.98, 1.16, 0.16, 5.02, 0.1, 2.92, 34.15, 57.01, 0.29,
0.84, 0, 0.82, 15.27, 0.01, 5.09, 205.09, 12.02, 5.94, 0.05,
3.01, 2.23, 0.19, 4.97, 4.89, 0.75, 6.18, 0.88, 26.17, 7.43,
32.88, 1.67, 10.28, 1.08, 0.23, 11.5, 0, 4.81, 1.83, 33.09,
0.33, 8.75, 1.97, 0.08, 1.03, 33.74, 0.03, 4.61, 40.9, 14.69,
5.26, 4.72, 5.68, 0.24, 1.31, 5.86, 0.16, 0.13, 0.95, 2.23,
1.22, 13.94, 5.31, 0.1, 2.14, 0.16, 0.16, 5.14, 2.82, 0,
0.15, 7.9, 0.19, 1.12, 0.02, 22.05, 0.05, 2.12, 0, 0.31,
0.05, 2.84, 0.9, 5.08, 15.07, 2.21, 0.1, 0.01, 19.88, 0.02,
13, 20.92, 2.02, 1.15, 6.37, 0.83, 3.1, 0.85, 1.22, 0.87,
1.95, 7.21, 0.12, 0.11, 8.15, 0.01, 6.11, 3.01, 3.61, 0.01,
0.13, 0.03, 3.1, 0.08, 1, 46.36, 0.29, 21.17, 1.84, 1.01,
29.89, 0.98, 2.18, 0.13, 17, 3.33, 0.02, 1.01, 1.03, 0.91,
4.02, 0.78, 0, 8.24, 0.13, 0.05, 2.17, 1.02, 2.07, 0.07,
0.15, 1.36, 8.01, 1.8, 0.01, 7.13, 0, 3.9, 3.71, 3.75, 4.98,
36.27, 6.96, 2.88, 0.14, 4.07, 3.74, 6.4, 9.15, 8.53, 67,
16.3, 29.12, 10.39, 15.83, 0.35, 0.33, 28.21, 0.05, 0.01,
54.93, 0.17, 4.04, 3.03, 0.02, 12.07, 10.4, 0.05, 6.26, 5.75,
4.86, 15, 40.39, 20.11, 0.03, 0.95, 17.69, 2.43, 10.4, 3.82,
26.37, 5.36, 0, 0.12, 19.32, 5.25, 0.05, 0.01, 0.12, 7.01,
1.01, 3.87, 4.05, 5.86, 1.25, 7.22, 1.11, 0.69, 8.94, 13.89,
0.07, 1.05, 1.22, 0.07, 0.02, 12.85, 0.04, 12.03, 8.84, 0.99,
0.02, 0.14, 0.01, 5.04, 1.23, 27.99, 8.97, 24.05, 113.85,
20.15, 10.06, 1.15, 0.05, 12.79, 10.04, 0.1, 2.19, 0.01,
0.01, 18.23, 1.94, 0.31, 0.03, 7.83, 0.09, 3.41, 1.16, 0.12,
2.16, 2.99, 13.87, 1.15, 1, 0.96, 3.08, 6.13, 1.05, 0.06,
0.23, 0.07, 0.89, 1.94, 0.04, 1.01, 19.78, 1.9, 1.01, 7.11,
0.02, 0, 0.03, 0.07, 0.06, 8.01, 3.44, 16.77, 1.76, 1.06,
13.07, 1.01, 52.08, 1.02, 0.27, 0.04, 0.85, 4.15, 0.01, 0.1,
0.02, 10.14, 5.88, 0.01, 4.22, 53.82, 4.96, 0.1, 14.1, 0.05,
0.07, 32.79, 0.03, 1.4, 7.01, 15.21, 18.44, 0.07, 9.02, 6.9,
25.62, 2.58, 5.93, 15.02, 0.01, 16.89, 4.96, 10.14, 7.11,
0.11, 2.7, 4.07, 1.09, 0.06, 0.25, 0.88, 21.88, 0, 0.04,
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3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 5L, 3L, 5L, 3L, 2L,
5L, 3L, 5L, 5L, 3L, 5L, 3L, 5L, 3L, 2L, 3L, 3L, 5L, 3L, 3L,
2L, 2L, 3L, 5L, 3L, 3L, 5L, 3L, 3L, 2L, 5L, 3L, 5L, 5L, 3L,
2L, 3L, 3L, 2L, 5L, 3L, 3L, 2L, 3L, 3L, 5L, 2L, 3L, 3L, 3L,
5L, 2L, 5L, 2L, 2L, 3L, 3L, 3L, 2L, 5L, 5L, 5L, 2L, 3L, 2L,
2L, 5L, 3L, 5L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 5L,
3L, 5L, 2L, 3L, 3L, 5L, 3L, 3L, 5L, 5L, 3L, 3L, 3L, 3L, 3L,
5L, 3L, 3L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L,
3L, 3L, 2L, 3L, 2L), .Label = c("", "high", "medium", "no",
"none"), class = "factor"), case_status = structure(c(2L,
6L, 3L, 6L, 6L, 6L, 6L, 6L, 8L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
3L, 3L, 6L, 8L, 6L, 6L, 8L, 4L, 6L, 6L, 6L, 4L, 6L, 6L, 6L,
6L, 6L, 4L, 8L, 6L, 6L, 8L, 6L, 8L, 6L, 6L, 6L, 6L, 6L, 6L,
8L, 8L, 6L, 8L, 6L, 6L, 10L, 6L, 6L, 6L, 8L, 6L, 6L, 6L,
6L, 6L, 3L, 6L, 6L, 6L, 8L, 3L, 8L, 6L, 8L, 8L, 6L, 6L, 2L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 8L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 8L, 6L, 6L, 6L, 6L, 4L,
8L, 6L, 6L, 6L, 4L, 6L, 4L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 8L, 4L, 6L, 3L, 6L, 3L, 6L, 8L, 3L, 6L, 2L, 6L,
8L, 6L, 6L, 4L, 6L, 6L, 6L, 4L, 8L, 6L, 6L, 6L, 6L, 6L, 6L,
8L, 6L, 8L, 8L, 8L, 8L, 6L, 8L, 6L, 6L, 6L, 4L, 6L, 6L, 6L,
8L, 6L, 6L, 3L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
8L, 6L, 8L, 6L, 8L, 8L, 6L, 4L, 4L, 8L, 6L, 10L, 8L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 8L, 6L, 6L, 6L, 8L, 6L,
6L, 6L, 8L, 6L, 6L, 6L, 6L, 2L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
8L, 6L, 6L, 8L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 4L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 4L,
6L, 8L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 4L, 6L, 6L,
6L, 4L, 6L, 6L, 6L, 3L, 6L, 6L, 6L, 4L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 8L, 6L, 6L, 6L, 6L, 6L, 8L, 6L, 3L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 8L, 6L, 3L, 6L, 6L, 6L, 4L, 6L, 6L, 6L, 2L,
6L, 6L, 6L, 8L, 10L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 8L, 4L,
6L, 6L, 8L, 6L, 6L, 6L, 3L, 6L, 6L, 6L, 6L, 8L, 6L, 4L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
8L, 6L, 6L, 8L, 6L, 8L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 8L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 4L, 6L, 4L, 6L,
6L, 8L, 8L, 6L, 6L, 6L, 8L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 4L, 8L, 6L, 6L, 6L, 6L, 6L, 4L, 6L, 10L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 4L, 6L, 6L, 6L, 8L, 6L, 4L, 6L, 6L, 6L,
6L, 6L, 2L, 6L, 6L, 6L, 6L, 4L, 6L, 6L, 6L, 4L, 2L, 6L, 6L,
6L, 4L, 6L, 2L, 8L, 6L, 6L, 8L, 8L, 6L, 6L, 6L, 6L, 6L, 4L,
6L, 6L, 4L, 6L, 6L, 6L, 4L, 6L, 6L, 8L, 6L, 6L, 6L, 6L, 6L,
3L, 6L, 6L, 8L, 8L, 8L, 8L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 8L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 8L, 6L, 2L,
6L, 6L, 8L, 6L, 6L, 8L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 4L, 6L,
8L, 6L, 6L, 6L, 6L, 6L, 8L, 6L, 3L, 4L, 6L, 6L, 6L, 3L, 8L,
6L, 6L, 6L, 6L, 4L, 6L, 6L, 8L, 6L, 3L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 4L, 6L, 6L, 6L, 6L, 8L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 4L, 10L, 6L, 6L,
6L, 4L, 6L, 6L, 6L, 6L, 2L, 6L, 6L, 6L, 6L, 2L, 6L, 4L, 6L,
6L, 10L, 6L, 6L, 8L, 6L, 6L, 6L, 6L, 6L, 2L, 6L, 6L, 6L,
6L, 6L, 8L, 6L, 6L, 10L, 6L, 3L, 6L, 4L, 6L, 8L, 8L, 6L,
4L, 6L, 6L, 6L, 8L, 6L, 8L, 4L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 3L, 4L, 6L, 8L, 6L, 8L, 8L, 2L, 4L, 6L, 6L, 4L, 6L, 6L,
6L, 6L, 6L, 4L, 6L, 6L, 6L, 6L, 8L, 3L, 8L, 4L, 6L, 6L, 8L,
4L, 6L, 2L, 4L, 8L, 6L, 6L, 4L, 6L, 6L, 8L, 2L, 6L, 6L, 6L,
8L, 8L, 6L, 6L, 6L, 8L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 10L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 8L, 6L,
8L, 8L, 6L, 8L, 8L, 6L, 6L, 6L, 6L, 6L, 8L, 8L, 8L, 4L, 3L,
6L, 6L, 6L, 6L, 6L, 8L, 6L, 6L, 6L, 4L, 6L, 6L, 6L, 6L, 6L,
4L, 6L, 4L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 8L, 8L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 8L, 6L, 6L, 6L, 6L,
4L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 8L, 6L, 8L, 8L, 6L, 8L,
8L, 6L, 6L, 6L, 6L, 6L, 8L, 6L, 6L, 8L, 6L, 6L, 4L, 6L, 6L,
6L, 6L, 4L, 6L, 6L, 6L, 6L, 10L, 6L, 4L, 8L, 6L, 6L, 6L,
6L, 6L, 8L, 8L, 8L, 6L, 6L, 6L, 6L, 6L, 6L, 2L, 6L, 6L, 3L,
3L, 10L, 6L, 4L, 6L, 6L, 6L, 2L, 6L, 6L, 4L, 6L, 4L, 6L,
6L, 4L, 6L, 10L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 8L, 6L, 6L,
6L, 6L, 2L, 6L, 4L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 4L, 3L,
8L, 8L, 6L, 8L, 6L, 6L, 6L, 6L, 6L, 4L, 6L, 6L, 8L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 4L, 6L, 6L, 6L, 6L, 8L, 6L,
8L, 6L, 6L, 6L, 6L, 6L, 4L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 4L,
6L, 6L, 2L, 6L, 6L, 6L, 6L, 6L, 6L, 8L, 6L, 6L, 6L, 8L, 8L,
4L, 6L, 8L, 6L, 6L, 6L, 6L, 2L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
2L, 6L, 8L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 4L, 8L, 6L, 4L, 10L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 8L, 6L, 8L, 6L, 6L, 6L, 8L, 6L, 6L,
6L, 8L, 8L, 6L, 6L, 2L), .Label = c("", "closed", "no resp",
"oos", "pending", "Resolved", "routed", "self closed", "Working",
"yes"), class = "factor"), account_segment = structure(c(4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L), .Label = c("", "-", "Flagship",
"Large", "Low", "Medium", "Mega", "N/A", "Platinum", "Small",
"Top", "Very Small"), class = "factor"), sla_status = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("", "Met",
"Missed", "N/A", "Pending"), class = "factor"), survey = c(1L,
1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L,
0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L,
1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L,
1L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L,
1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L,
1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L,
1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L,
1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L,
0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L,
1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L,
1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L,
1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L,
1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L,
1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L,
1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L,
1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L,
1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L,
1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L,
0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L,
1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L,
0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L,
1L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L), repS = c(1L, 1L, 0L,
1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L,
0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L,
0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L,
1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L,
NA, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L,
0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 0L, NA, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L,
0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, NA, 1L, 1L, 1L, 0L,
1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L,
1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
1L, 1L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, NA, NA,
0L, 0L, NA, 1L, 1L, 1L, 1L, NA, 0L, 1L, 1L, 1L, 0L, 1L, 1L,
0L, 1L, 1L, NA, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L,
0L, 1L, 1L, 1L, NA, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, NA, 1L, 1L, 1L, 1L, 0L, 0L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L,
1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, NA,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, NA, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L,
NA, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L,
1L, 0L, 1L, 0L, 1L, 1L, 0L, NA, 1L, 0L, 0L, 1L, 1L, 1L, 1L,
0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L,
0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 1L, 1L, NA, 0L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L,
1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L,
1L, NA, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L,
1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, 0L, 0L, 1L, NA, 1L, 1L, NA, 0L, NA, 1L, 1L, 1L,
1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, NA, 1L, 1L, 0L, NA, 0L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, NA, 1L, NA, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, NA, 1L, 1L, NA, 1L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 1L, 1L, 1L, NA, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
1L, 0L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, 0L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L,
1L, NA, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L,
NA, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, NA, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, NA, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, 1L, 1L,
1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, NA, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 0L)), .Names = c("support_cat", "region",
"support_lvl", "skill_group", "application_area", "functional_area",
"score", "rep_score", "product_know", "understanding_issue",
"case_age", "severity_level", "case_status", "account_segment",
"sla_status", "survey", "repS"), row.names = c(NA, 1000L), class = "data.frame")
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