Data cleaning & Data preparation, what do R users want?

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Data cleaning & Data preparation, what do R users want?

Robert Wilkins-2
R has a very wide audience, clinical research, astronomy, psychology, and
so on and so on.
I would consider data analysis work to be three stages: data preparation,
statistical analysis, and producing the report.
This regards the process of getting the data ready for analysis and
reporting, sometimes called "data cleaning" or "data munging" or "data
wrangling".

So as regards tools for data preparation, speaking to the highly diverse
audience mentioned, here is my question:

What do you want?
Or are you already quite happy with the range of tools that is currently
before you?

[BTW,  I posed the same question last week to the r-devel list, and was
advised that r-help might be a more suitable audience by one of the
moderators.]

Robert Wilkins

        [[alternative HTML version deleted]]

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Re: Data cleaning & Data preparation, what do R users want?

Bert Gunter-2
I don't think my view is of interest to many, so offlist.

I reject this:

" I would consider data analysis work to be three stages: data preparation,
statistical analysis, and producing the report."

For example, there is no such thing as "outliers" -- data to be removed as
part of cleaning/preparation -- without a statistical model to be an
"outlier" **from**, which is part of the statistical analysis. And the
structure of the data (data preparation) may need to change depending on
the course of the analysis (including graphics, also part of the analysis).
So I think your view reflects a naïve view of the nature of data analysis,
which is an iterative and holistic process. I suspect your training is as a
computer scientist and you have not done much 1-1 consulting with
researchers, though you should certainly feel free to reject this canard.
Building software for large scale automated analysis of data required a
much different analytical paradigm than the statistical consulting model,
which is largely my background.

No reply necessary. Just my opinion, which you are of course free to trash.

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 Wed, Nov 29, 2017 at 8:37 AM, Robert Wilkins <[hidden email]>
wrote:

> R has a very wide audience, clinical research, astronomy, psychology, and
> so on and so on.
> I would consider data analysis work to be three stages: data preparation,
> statistical analysis, and producing the report.
> This regards the process of getting the data ready for analysis and
> reporting, sometimes called "data cleaning" or "data munging" or "data
> wrangling".
>
> So as regards tools for data preparation, speaking to the highly diverse
> audience mentioned, here is my question:
>
> What do you want?
> Or are you already quite happy with the range of tools that is currently
> before you?
>
> [BTW,  I posed the same question last week to the r-devel list, and was
> advised that r-help might be a more suitable audience by one of the
> moderators.]
>
> Robert Wilkins
>
>         [[alternative HTML version deleted]]
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/
> posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

        [[alternative HTML version deleted]]

______________________________________________
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Re: Data cleaning & Data preparation, what do R users want?

Bert Gunter-2
Oh Crap! I mistakenly replied onlist. PLEASE IGNORE -- these are only my
ignorant opinions.

-- 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 Wed, Nov 29, 2017 at 8:48 AM, Bert Gunter <[hidden email]> wrote:

> I don't think my view is of interest to many, so offlist.
>
> I reject this:
>
> " I would consider data analysis work to be three stages: data preparation,
> statistical analysis, and producing the report."
>
> For example, there is no such thing as "outliers" -- data to be removed as
> part of cleaning/preparation -- without a statistical model to be an
> "outlier" **from**, which is part of the statistical analysis. And the
> structure of the data (data preparation) may need to change depending on
> the course of the analysis (including graphics, also part of the analysis).
> So I think your view reflects a naïve view of the nature of data analysis,
> which is an iterative and holistic process. I suspect your training is as a
> computer scientist and you have not done much 1-1 consulting with
> researchers, though you should certainly feel free to reject this canard.
> Building software for large scale automated analysis of data required a
> much different analytical paradigm than the statistical consulting model,
> which is largely my background.
>
> No reply necessary. Just my opinion, which you are of course free to trash.
>
> 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 Wed, Nov 29, 2017 at 8:37 AM, Robert Wilkins <[hidden email]>
> wrote:
>
>> R has a very wide audience, clinical research, astronomy, psychology, and
>> so on and so on.
>> I would consider data analysis work to be three stages: data preparation,
>> statistical analysis, and producing the report.
>> This regards the process of getting the data ready for analysis and
>> reporting, sometimes called "data cleaning" or "data munging" or "data
>> wrangling".
>>
>> So as regards tools for data preparation, speaking to the highly diverse
>> audience mentioned, here is my question:
>>
>> What do you want?
>> Or are you already quite happy with the range of tools that is currently
>> before you?
>>
>> [BTW,  I posed the same question last week to the r-devel list, and was
>> advised that r-help might be a more suitable audience by one of the
>> moderators.]
>>
>> Robert Wilkins
>>
>>         [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
>> 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.
>>
>
>

        [[alternative HTML version deleted]]

______________________________________________
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Re: Data cleaning & Data preparation, what do R users want?

Christopher W. Ryan
In reply to this post by Robert Wilkins-2
Great question. What do I want? I want my co-workers to stop using Excel
spreadsheets for data entry, storage, and sharing! I want them to
understand the value of data discipline. But alas . . . .

I work in a county health department in the US. Between dplyr, stringr,
grep, grepl, and the base R read() functions, I'm doing OK.

I need to learn more about APIs, so I can see if I can make R directly
grab data from, e.g. our state health department sources. My biggest
hassle is having to download a data file, save it somewhere, and then
open R and read it in. I'd like to be able to do it all in R. Would make
the generation of recurring reports easier.

--Chris Ryan

Robert Wilkins wrote:

> R has a very wide audience, clinical research, astronomy, psychology, and
> so on and so on.
> I would consider data analysis work to be three stages: data preparation,
> statistical analysis, and producing the report.
> This regards the process of getting the data ready for analysis and
> reporting, sometimes called "data cleaning" or "data munging" or "data
> wrangling".
>
> So as regards tools for data preparation, speaking to the highly diverse
> audience mentioned, here is my question:
>
> What do you want?
> Or are you already quite happy with the range of tools that is currently
> before you?
>
> [BTW,  I posed the same question last week to the r-devel list, and was
> advised that r-help might be a more suitable audience by one of the
> moderators.]
>
> Robert Wilkins
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
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and provide commented, minimal, self-contained, reproducible code.
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Re: Data cleaning & Data preparation, what do R users want?

Robert Wilkins-2
Christopher,

OK, well what about a range of functions in an R package that
automatically, with very little syntax, pulls in data from a variety of
formats (CSV, SQLite, and so on) and converts them to an R data frame. You
seem to be pointing to something like that.
Something like that, in some form or another, probably already exists,
though it might be either imperfect (not as user-friendly as possible) or
not well publicised, or both.
Or another tangent: your co-workers are not going to stop using Excel,
whether you like it or not, and many end-users are stuck in the exact same
position as you (co-workers who deliver the data in Excel). I will guess
that data stored in Excel tends to be dirty in somewhat predictable ways.
(And again, those other end-user's coworkers are not going to change their
behaviour). And so: a data munging tool that makes it as easy as possible
to clean up the data in Excel spreadsheets and export them to R data
frames. One prerequisite: an understanding of what tends to go wrong with
data with Excel ( the data in Excel tends to be dirty, but dirty in what
way?).

Thank you for your response Christopher. What state are you in?


On Wed, Nov 29, 2017 at 11:52 AM, Christopher W. Ryan <[hidden email]>
wrote:

> Great question. What do I want? I want my co-workers to stop using Excel
> spreadsheets for data entry, storage, and sharing! I want them to
> understand the value of data discipline. But alas . . . .
>
> I work in a county health department in the US. Between dplyr, stringr,
> grep, grepl, and the base R read() functions, I'm doing OK.
>
> I need to learn more about APIs, so I can see if I can make R directly
> grab data from, e.g. our state health department sources. My biggest
> hassle is having to download a data file, save it somewhere, and then
> open R and read it in. I'd like to be able to do it all in R. Would make
> the generation of recurring reports easier.
>
> --Chris Ryan
>
> Robert Wilkins wrote:
> > R has a very wide audience, clinical research, astronomy, psychology, and
> > so on and so on.
> > I would consider data analysis work to be three stages: data preparation,
> > statistical analysis, and producing the report.
> > This regards the process of getting the data ready for analysis and
> > reporting, sometimes called "data cleaning" or "data munging" or "data
> > wrangling".
> >
> > So as regards tools for data preparation, speaking to the highly diverse
> > audience mentioned, here is my question:
> >
> > What do you want?
> > Or are you already quite happy with the range of tools that is currently
> > before you?
> >
> > [BTW,  I posed the same question last week to the r-devel list, and was
> > advised that r-help might be a more suitable audience by one of the
> > moderators.]
> >
> > Robert Wilkins
> >
> >       [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide http://www.R-project.org/
> posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
> >
>

        [[alternative HTML version deleted]]

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
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and provide commented, minimal, self-contained, reproducible code.
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Re: Data cleaning & Data preparation, what do R users want?

Jim Lemon-4
In reply to this post by Robert Wilkins-2
Hi Robert,
People want different levels of automation in the software they use.
What concerns many of us is the desire for the function
"figure-out-what-this-data-is-import-it-and-get-rid-of-bad-values".
Such users typically want something that justifies its use by being
written by someone who seems to know what they're doing and lots of
other people use it. One advantage of many R functions is their
modular construction. This encourages users to at least consider the
steps that are taken rather than just accept what comes out of that
long tube.

Take the contentious problem of outlier identification. If I just let
the black box peel off some values, I don't know what I have lost. On
the other hand, if I import data and examine it with a summary
function, I may find that one woman has a height of 5.2 meters. I can
range check by looking up the Guinness Book of Records. It's an
outlier. I can estimate the probability of such a height.  Hmm, about
4 standard deviations above the mean. It's an outlier. I can attempt a
Sherlock Holmes. "Watson, I conclude that an imperial measure (5'2")
has been recorded as a metric value". It's not an outlier.

The more R gravitates toward "black box" functions, the more some
users are encouraged to let them do the work.You pays your money and
you takes your chances.

Jim


On Thu, Nov 30, 2017 at 3:37 AM, Robert Wilkins <[hidden email]> wrote:

> R has a very wide audience, clinical research, astronomy, psychology, and
> so on and so on.
> I would consider data analysis work to be three stages: data preparation,
> statistical analysis, and producing the report.
> This regards the process of getting the data ready for analysis and
> reporting, sometimes called "data cleaning" or "data munging" or "data
> wrangling".
>
> So as regards tools for data preparation, speaking to the highly diverse
> audience mentioned, here is my question:
>
> What do you want?
> Or are you already quite happy with the range of tools that is currently
> before you?
>
> [BTW,  I posed the same question last week to the r-devel list, and was
> advised that r-help might be a more suitable audience by one of the
> moderators.]
>
> Robert Wilkins
>
>         [[alternative HTML version deleted]]
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
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Re: Data cleaning & Data preparation, what do R users want?

Jim Lemon-4
Hi again,
Typo in the last email. Should read "about 40 standard deviations".

Jim

On Thu, Nov 30, 2017 at 10:54 AM, Jim Lemon <[hidden email]> wrote:

> Hi Robert,
> People want different levels of automation in the software they use.
> What concerns many of us is the desire for the function
> "figure-out-what-this-data-is-import-it-and-get-rid-of-bad-values".
> Such users typically want something that justifies its use by being
> written by someone who seems to know what they're doing and lots of
> other people use it. One advantage of many R functions is their
> modular construction. This encourages users to at least consider the
> steps that are taken rather than just accept what comes out of that
> long tube.
>
> Take the contentious problem of outlier identification. If I just let
> the black box peel off some values, I don't know what I have lost. On
> the other hand, if I import data and examine it with a summary
> function, I may find that one woman has a height of 5.2 meters. I can
> range check by looking up the Guinness Book of Records. It's an
> outlier. I can estimate the probability of such a height.  Hmm, about
> 4 standard deviations above the mean. It's an outlier. I can attempt a
> Sherlock Holmes. "Watson, I conclude that an imperial measure (5'2")
> has been recorded as a metric value". It's not an outlier.
>
> The more R gravitates toward "black box" functions, the more some
> users are encouraged to let them do the work.You pays your money and
> you takes your chances.
>
> Jim
>
>
> On Thu, Nov 30, 2017 at 3:37 AM, Robert Wilkins <[hidden email]> wrote:
>> R has a very wide audience, clinical research, astronomy, psychology, and
>> so on and so on.
>> I would consider data analysis work to be three stages: data preparation,
>> statistical analysis, and producing the report.
>> This regards the process of getting the data ready for analysis and
>> reporting, sometimes called "data cleaning" or "data munging" or "data
>> wrangling".
>>
>> So as regards tools for data preparation, speaking to the highly diverse
>> audience mentioned, here is my question:
>>
>> What do you want?
>> Or are you already quite happy with the range of tools that is currently
>> before you?
>>
>> [BTW,  I posed the same question last week to the r-devel list, and was
>> advised that r-help might be a more suitable audience by one of the
>> moderators.]
>>
>> Robert Wilkins
>>
>>         [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
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Re: Data cleaning & Data preparation, what do R users want?

dschneiderch
I would agree that getting data into R from various sources is the biggest
pain point. Even if there is an api, the results are not always consistent
and you have to do lots of dimension checking to get it right. Or there
isn't an open api at all and you have to hack it by web scraping or
otherwise- http://enpiar.com/2017/08/11/one-hour-package/

On Thu, Nov 30, 2017 at 1:00 AM, Jim Lemon <[hidden email]> wrote:

> Hi again,
> Typo in the last email. Should read "about 40 standard deviations".
>
> Jim
>
> On Thu, Nov 30, 2017 at 10:54 AM, Jim Lemon <[hidden email]> wrote:
> > Hi Robert,
> > People want different levels of automation in the software they use.
> > What concerns many of us is the desire for the function
> > "figure-out-what-this-data-is-import-it-and-get-rid-of-bad-values".
> > Such users typically want something that justifies its use by being
> > written by someone who seems to know what they're doing and lots of
> > other people use it. One advantage of many R functions is their
> > modular construction. This encourages users to at least consider the
> > steps that are taken rather than just accept what comes out of that
> > long tube.
> >
> > Take the contentious problem of outlier identification. If I just let
> > the black box peel off some values, I don't know what I have lost. On
> > the other hand, if I import data and examine it with a summary
> > function, I may find that one woman has a height of 5.2 meters. I can
> > range check by looking up the Guinness Book of Records. It's an
> > outlier. I can estimate the probability of such a height.  Hmm, about
> > 4 standard deviations above the mean. It's an outlier. I can attempt a
> > Sherlock Holmes. "Watson, I conclude that an imperial measure (5'2")
> > has been recorded as a metric value". It's not an outlier.
> >
> > The more R gravitates toward "black box" functions, the more some
> > users are encouraged to let them do the work.You pays your money and
> > you takes your chances.
> >
> > Jim
> >
> >
> > On Thu, Nov 30, 2017 at 3:37 AM, Robert Wilkins <[hidden email]>
> wrote:
> >> R has a very wide audience, clinical research, astronomy, psychology,
> and
> >> so on and so on.
> >> I would consider data analysis work to be three stages: data
> preparation,
> >> statistical analysis, and producing the report.
> >> This regards the process of getting the data ready for analysis and
> >> reporting, sometimes called "data cleaning" or "data munging" or "data
> >> wrangling".
> >>
> >> So as regards tools for data preparation, speaking to the highly diverse
> >> audience mentioned, here is my question:
> >>
> >> What do you want?
> >> Or are you already quite happy with the range of tools that is currently
> >> before you?
> >>
> >> [BTW,  I posed the same question last week to the r-devel list, and was
> >> advised that r-help might be a more suitable audience by one of the
> >> moderators.]
> >>
> >> Robert Wilkins
> >>
> >>         [[alternative HTML version deleted]]
> >>
> >> ______________________________________________
> >> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> >> https://stat.ethz.ch/mailman/listinfo/r-help
> >> PLEASE do read the posting guide http://www.R-project.org/
> posting-guide.html
> >> and provide commented, minimal, self-contained, reproducible code.
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/
> posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

        [[alternative HTML version deleted]]

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
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and provide commented, minimal, self-contained, reproducible code.
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Re: Data cleaning & Data preparation, what do R users want?

Robert Wilkins-2
Dominik (and others)

If it is indeed still the biggest paint point, even in 2017, then maybe we
can do something about that, with more efforts at different user interface
design and try-outs with them on specialized datasets.
[ The fact that in some specialties, such as clinical trials, for example,
getting access to public domain datasets (and not having to use a tiny
"toy" dataset, which nobody will pay attention to, does make it harder].

It would help if academia (both comp-sci and statistics departments) would
support those who invest resources in drafting and test-driving new product
designs. If, in the year 2017, it is still a big pain point, doesn't that
make sense. More speculative work in statistical programming language
design has not been a priority in academia since before 1980.

On Thu, Nov 30, 2017 at 4:11 AM, Dominik Schneider <
[hidden email]> wrote:

> I would agree that getting data into R from various sources is the biggest
> pain point. Even if there is an api, the results are not always consistent
> and you have to do lots of dimension checking to get it right. Or there
> isn't an open api at all and you have to hack it by web scraping or
> otherwise- http://enpiar.com/2017/08/11/one-hour-package/
>
> On Thu, Nov 30, 2017 at 1:00 AM, Jim Lemon <[hidden email]> wrote:
>
>> Hi again,
>> Typo in the last email. Should read "about 40 standard deviations".
>>
>> Jim
>>
>> On Thu, Nov 30, 2017 at 10:54 AM, Jim Lemon <[hidden email]> wrote:
>> > Hi Robert,
>> > People want different levels of automation in the software they use.
>> > What concerns many of us is the desire for the function
>> > "figure-out-what-this-data-is-import-it-and-get-rid-of-bad-values".
>> > Such users typically want something that justifies its use by being
>> > written by someone who seems to know what they're doing and lots of
>> > other people use it. One advantage of many R functions is their
>> > modular construction. This encourages users to at least consider the
>> > steps that are taken rather than just accept what comes out of that
>> > long tube.
>> >
>> > Take the contentious problem of outlier identification. If I just let
>> > the black box peel off some values, I don't know what I have lost. On
>> > the other hand, if I import data and examine it with a summary
>> > function, I may find that one woman has a height of 5.2 meters. I can
>> > range check by looking up the Guinness Book of Records. It's an
>> > outlier. I can estimate the probability of such a height.  Hmm, about
>> > 4 standard deviations above the mean. It's an outlier. I can attempt a
>> > Sherlock Holmes. "Watson, I conclude that an imperial measure (5'2")
>> > has been recorded as a metric value". It's not an outlier.
>> >
>> > The more R gravitates toward "black box" functions, the more some
>> > users are encouraged to let them do the work.You pays your money and
>> > you takes your chances.
>> >
>> > Jim
>> >
>> >
>> > On Thu, Nov 30, 2017 at 3:37 AM, Robert Wilkins <[hidden email]>
>> wrote:
>> >> R has a very wide audience, clinical research, astronomy, psychology,
>> and
>> >> so on and so on.
>> >> I would consider data analysis work to be three stages: data
>> preparation,
>> >> statistical analysis, and producing the report.
>> >> This regards the process of getting the data ready for analysis and
>> >> reporting, sometimes called "data cleaning" or "data munging" or "data
>> >> wrangling".
>> >>
>> >> So as regards tools for data preparation, speaking to the highly
>> diverse
>> >> audience mentioned, here is my question:
>> >>
>> >> What do you want?
>> >> Or are you already quite happy with the range of tools that is
>> currently
>> >> before you?
>> >>
>> >> [BTW,  I posed the same question last week to the r-devel list, and was
>> >> advised that r-help might be a more suitable audience by one of the
>> >> moderators.]
>> >>
>> >> Robert Wilkins
>> >>
>> >>         [[alternative HTML version deleted]]
>> >>
>> >> ______________________________________________
>> >> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
>> >> 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.
>>
>> ______________________________________________
>> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
>> 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.
>>
>
>

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______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.