Re: [R] Learning advanced R

[Spencer Graves]

On 2018-03-14 11:52, Rich Shepard wrote:

On Wed, 14 Mar 2018, Duncan Murdoch wrote:

I'm all for learning more languages and using the one that's best for 
each
job, but for people who don't know Python, it would be helpful to 
list the

aspects in which it excels. When should an R user choose to write
something in Python instead?


Duncan,

  "Best" is subjective, but my view is the language most comfortable and
familiar to the developer/analyst should be the one used.

  In my environmental consulting business I use both R and Python. While
Python has support for many statistical models I'm more comfortable 
with the
ones available in R. For spatial analyses (separate from spatial 
statistics)
I've used GRASS for > 20 years and it heavily uses Python. I also use 
Python
(along with emacs, awk, sed, and grep) for cleaning and organizing 
data. For

writing, I use LaTeX (a markup language) and the LyX GUI front end.

  Python has a lot of support for scientific and financial analyses, 
as does

R. Considering there are a gazillion programming languages available (and
used for essential applications, such as GnuCash (written in guile, a 
scheme

variant) which I use for business and personal bookkeeping, picking the
"best" one is strictly a personal matter. I prefer emacs, my system and
network admin friends prefer vi. In linux, at least, there are so many
options for doing a task that sometimes it's difficult to decide which to
use in a given situation.

  If the languages you know do all you need then learn a new one only if
it's to scratch an itch. :-)



  My software development productivity increased by a factor of 
maybe 30 by using first S-Plus then R, including writing R packages, 
then RStudio and writing Rmarkdown vignettes.



        1.  I started writing Fortran in 1963.  I've written 
assembly language for multiple machines, Cobol, Lisp, and other 
language.  I started using S-Plus in the early 1990s and abandoned it 
for R when I needed "debug" for some S-Plus code.  Developing R packages 
improved my software development productivity by a factor of 10, because 
the discipline of creating unit tests in "\examples" made it so much 
easier to debug and maintain -- AND share with others.



        2.  I've also written some Python, though not much.  I used 
Emacs until I found RStudio.  Vi and Emacs are not tools you can give to 
someone, who is only marginally computer literate and expect them to be 
productive in a reasonable period of time.  By contrast, if someone 
knows enough to be able to install R and RStudio, I can give them some R 
code and be confident that they will get something useful from the 
experience in a relatively short period of time.  You can't do that with 
vi and Emacs unless they already know those applications.



        3.  Recently, I've started writing RMarkdown vignettes, and 
that further increased my productivity.



          3.1.  Two years ago, I told I client I was going to 
prepare and Rmarkdown vignette to document what I did with their data.  
My sales guy said absolutely, we were NOT going to give the client an 
Rmarkdown vignette.  I spent a week analyzing the data and 6 months 
answering questions from the team mostly by pointing them to certain 
lines in the vignette, occasionally by extending it.  In the middle of 
that, we learned that the client required our analysis to be 
verifiable.  After that, the vignette became a primary deliverable.



          3.2.  More recently, another client asked me to 
explain principal components.  This client was moderately facile with 
software but not with R nor vector spaces.  I gave him an Rmarkdown 
vignette that included a principal components on some data he gave me 
done both with a single command and step by step supplemented with a 
simple discussion of a one-dimensional subspace of two-dimensional 
space.  He was happy.



        4.  I invite all to review and improve the discussion in 
the Wikipedia article on "Software repository".  This a table with a 
discussion of "Selected repositories", much of which I wrote 8 years 
ago.  It's heavily biased toward CRAN, because that's what I know the 
best, and I've so far been unable to find anyone with the expertise and 
interest in improving it.  This article averaged 290 views per day over 
the past 90 days, over 26,000 in the past 3 months.  If you can improve 
that article, an audience that size might be worth talking to.



   Spencer Graves



Best regards,

Rich

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Re: [R] Learning advanced R

[Rich Shepard]

On Wed, 14 Mar 2018, Duncan Murdoch wrote:


I'm all for learning more languages and using the one that's best for each
job, but for people who don't know Python, it would be helpful to list the
aspects in which it excels. When should an R user choose to write
something in Python instead?


Duncan,

  "Best" is subjective, but my view is the language most comfortable and
familiar to the developer/analyst should be the one used.

  In my environmental consulting business I use both R and Python. While
Python has support for many statistical models I'm more comfortable with the
ones available in R. For spatial analyses (separate from spatial statistics)
I've used GRASS for > 20 years and it heavily uses Python. I also use Python
(along with emacs, awk, sed, and grep) for cleaning and organizing data. For
writing, I use LaTeX (a markup language) and the LyX GUI front end.

  Python has a lot of support for scientific and financial analyses, as does
R. Considering there are a gazillion programming languages available (and
used for essential applications, such as GnuCash (written in guile, a scheme
variant) which I use for business and personal bookkeeping, picking the
"best" one is strictly a personal matter. I prefer emacs, my system and
network admin friends prefer vi. In linux, at least, there are so many
options for doing a task that sometimes it's difficult to decide which to
use in a given situation.

  If the languages you know do all you need then learn a new one only if
it's to scratch an itch. :-)

Best regards,

Rich

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Re: [R] Learning advanced R

[Duncan Murdoch]

On 14/03/2018 12:07 PM, Rich Shepard wrote:

On Wed, 14 Mar 2018, Barry Rowlingson wrote:


Depending on your application, I'm not sure there's much point in being an
"advanced R programmer" these days. Become an adequate R programmer, and
learn C++ and Rcpp. Do basic data mashing in R, then do all your intensive
stuff in C++ with Rcpp. Eventually you'll probably get to the point where
you can express yourself in C++ as fast as you can in interpreted R, with
the bonus of C++ speed, type-safety etc.


Barry,

Allow me to offer an alternative to C++: Python. Compiled languages are
faster than interpreted ones, but unless you're doing time-critical
computations it really does not matter. R and Python provide proven
abilities in a broad range of applications and with today's hardware the
analytical/modeling code is not likely to be the limiting factor.



I'm all for learning more languages and using the one that's best for 
each job, but for people who don't know Python, it would be helpful to 
list the aspects in which it excels.  When should an R user choose to 
write something in Python instead?


Duncan Murdoch

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Re: [R] Learning advanced R

[Rich Shepard]

On Wed, 14 Mar 2018, Barry Rowlingson wrote:


Depending on your application, I'm not sure there's much point in being an
"advanced R programmer" these days. Become an adequate R programmer, and
learn C++ and Rcpp. Do basic data mashing in R, then do all your intensive
stuff in C++ with Rcpp. Eventually you'll probably get to the point where
you can express yourself in C++ as fast as you can in interpreted R, with
the bonus of C++ speed, type-safety etc.


Barry,

  Allow me to offer an alternative to C++: Python. Compiled languages are
faster than interpreted ones, but unless you're doing time-critical
computations it really does not matter. R and Python provide proven
abilities in a broad range of applications and with today's hardware the
analytical/modeling code is not likely to be the limiting factor.

Regards,

Rich

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Re: [R] Learning advanced R

[Barry Rowlingson]

Depending on your application, I'm not sure there's much point in being an
"advanced R programmer" these days. Become an adequate R programmer, and
learn C++ and Rcpp. Do basic data mashing in R, then do all your intensive
stuff in C++ with Rcpp. Eventually you'll probably get to the point where
you can express yourself in C++ as fast as you can in interpreted R, with
the bonus of C++ speed, type-safety etc.



On Wed, Mar 14, 2018 at 8:13 AM, Eric Berger  wrote:

> Bert's suggestion is good as a pointer to a variety of resources.
> Sticking to the book format there are two of Hadley Wickham's books, which
> have the advantage that they are freely available.
> You can either read them online or download the source from github and
> create your own copy (which you can then print, if desired.)
> 1. "R for Data Science"
>  online: http://r4ds.had.co.nz/
>  github: https://github.com/hadley/r4ds
> 2. "Advanced R"
>  online: https://adv-r.hadley.nz/
>  github: https://github.com/hadley/adv-r
>
> Best,
> Eric
>
>
>
> On Wed, Mar 14, 2018 at 12:13 AM, Rich Shepard 
> wrote:
>
> > On Tue, 13 Mar 2018, Mark Leeds wrote:
> >
> > See Hadley's advanced R
> >>
> >
> >   +1 A very well writte, highly useful book. Recommended.
> >
> > Rich
> >
> >
> > __
> > R-help@r-project.org 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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> and provide commented, minimal, self-contained, reproducible code.
>

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Re: [R] Learning advanced R

[Eric Berger]

Bert's suggestion is good as a pointer to a variety of resources.
Sticking to the book format there are two of Hadley Wickham's books, which
have the advantage that they are freely available.
You can either read them online or download the source from github and
create your own copy (which you can then print, if desired.)
1. "R for Data Science"
 online: http://r4ds.had.co.nz/
 github: https://github.com/hadley/r4ds
2. "Advanced R"
 online: https://adv-r.hadley.nz/
 github: https://github.com/hadley/adv-r

Best,
Eric



On Wed, Mar 14, 2018 at 12:13 AM, Rich Shepard 
wrote:

> On Tue, 13 Mar 2018, Mark Leeds wrote:
>
> See Hadley's advanced R
>>
>
>   +1 A very well writte, highly useful book. Recommended.
>
> Rich
>
>
> __
> R-help@r-project.org 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: [R] Learning advanced R

[Rich Shepard]

On Tue, 13 Mar 2018, Mark Leeds wrote:


See Hadley's advanced R


  +1 A very well writte, highly useful book. Recommended.

Rich

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Re: [R] Learning advanced R

[Bert Gunter]

See here for some suggestions:

https://www.rstudio.com/online-learning/#R

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 Tue, Mar 13, 2018 at 2:31 PM, Mark Leeds  wrote:

> See Hadley's advanced R along Thomas Mailund's books. I haven't gone
> through them carefully but they both
> seem  (from what I've looked at ) to be the best ones for that. Mentions of
> others are appreciated.
>
>
>
>
> On Tue, Mar 13, 2018 at 5:26 PM, Nik Tuzov 
> wrote:
>
> >
> > Hello:
> >
> > Could you please suggest the best way to become an "advanced" R
> programmer.
> > I went through "R for dummies" by de Vries and Meys and I can see two
> ways
> > to proceed:
> >
> > 1) Get a more advanced textbook. E.g. could you recommend Gentleman,
> > "R for Bioinformatics"?
> >
> > 2) Because textbooks are limited and become obsolete fast, I can focus on
> > learning state-of-the-art packages,
> > but for that I need to find a list of most useful general purpose
> packages
> > (foreach, doParallel, etc) that is
> > updated in real time. Does such list exist?
> >
> > Your recommendations are very welcome.
> >
> > Thanks,
> > Nik
> >
> > __
> > R-help@r-project.org 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]]
>
> __
> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
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> and provide commented, minimal, self-contained, reproducible code.
>

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Re: [R] Learning advanced R

[Mark Leeds]

See Hadley's advanced R along Thomas Mailund's books. I haven't gone
through them carefully but they both
seem  (from what I've looked at ) to be the best ones for that. Mentions of
others are appreciated.




On Tue, Mar 13, 2018 at 5:26 PM, Nik Tuzov  wrote:

>
> Hello:
>
> Could you please suggest the best way to become an "advanced" R programmer.
> I went through "R for dummies" by de Vries and Meys and I can see two ways
> to proceed:
>
> 1) Get a more advanced textbook. E.g. could you recommend Gentleman,
> "R for Bioinformatics"?
>
> 2) Because textbooks are limited and become obsolete fast, I can focus on
> learning state-of-the-art packages,
> but for that I need to find a list of most useful general purpose packages
> (foreach, doParallel, etc) that is
> updated in real time. Does such list exist?
>
> Your recommendations are very welcome.
>
> Thanks,
> Nik
>
> __
> R-help@r-project.org 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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[R] Learning advanced R

[Nik Tuzov]

Hello:

Could you please suggest the best way to become an "advanced" R programmer.
I went through "R for dummies" by de Vries and Meys and I can see two ways
to proceed:

1) Get a more advanced textbook. E.g. could you recommend Gentleman, 
"R for Bioinformatics"?

2) Because textbooks are limited and become obsolete fast, I can focus on 
learning state-of-the-art packages, 
but for that I need to find a list of most useful general purpose packages 
(foreach, doParallel, etc) that is
updated in real time. Does such list exist?

Your recommendations are very welcome.

Thanks,
Nik

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