
There are two somewhat different objectives highlighted in these posts:

How to best learn classical statistics?

How to best learn R?

One might argue that classical statistics texts (or any well written modern one) provide a more rudimentary knowledge of underlying theory than one geared to "learning R".  On the other hand, as mentioned earlier and from my own experience when I was an undergraduate, most students learn stats by "doing" and any text that provides an easier interface between the software, methods and theory will likely be more useful.  










> From: byrnes@msi.ucsb.edu
> To: r-sig-ecology@r-project.org
> Date: Mon, 10 Nov 2008 15:19:44 -0800
> Subject: Re: [R-sig-eco] classical statistics in R
> 
> While I agree with this statement in principal, I disagree in  
> practice.  I think one of the challenges of teaching classical (or  
> any) ecological stats and analysis of experiments to new students is  
> being able to allow them to begin to understand not just the specifics  
> and concepts of the methods as quickly as possible, but to also begin  
> to confront the real problems of the data analyst.  With a given set  
> of data, how will choice of method, violation of assumptions, that  
> seagull that ate half of the treatment plots, etc. really affect the  
> inferences I can draw?  A large hurdle I've seen in many classes,  
> regardless of the package they chose to use, is actually getting  
> students to learn and then work with the software.  This has often  
> involved whole separate labs or classes that, at worst, can be mere  
> exercises in button pushing and far abstracted from the course material.
> 
> Written languages, such as SAS and R, take some of that away, of  
> course, and having a text that at least features examples in said  
> language can more seamlessly integrate the two.  After working through  
> Gelman and Hill, I was struck by how the code and the conceptual text  
> worked together pretty seamlessly.  At the end, I was able to emerge  
> with a working understanding of the concepts of multilevel modeling,  
> Bayes, etc.  More importantly, I knew I had a toolset in hand that I  
> could always turn to and work with in order to carry forth my data  
> analysis.
> 
> And, indeed, that _always_ is one of the advantages to R.  It's free.   
> You can learn it as an undergrad, keep working with it in grad school,  
> end up working at a tiny research station with no money in the middle  
> of the North Sea, and you will always be able to use it.  No site  
> licensees, etc, needed.  I think this is an enormous practical  
> advantage in the long run.  And, indeed, thought it will grow and  
> change with time, as it is public domain, there is never any danger of  
> it disappearing from the earth, like many favorite canned statistical  
> packages of yore.
> 
> Hence, why not learn it early, and why not a good book that integrates  
> concept and practice?  It's what pleased me so much about G&H as well  
> as Ben's book.
> 
> Perhaps it is time for a classical statistics book for ecology that  
> both emphasized the conceptual meat of the material but is integrated  
> with R, allowing students to really explore that meat on their own?
> 
> On Nov 10, 2008, at 1:55 PM, Sebastian P. Luque wrote:
> 
> > In general, I would not choose a book to learn basic statistics  
> > based on
> > whether it has R content or not.  What's important is to learn the
> > concepts.  Learning how to use them in a particular software is  
> > useful,
> > but secondary.  If we're careless about this distinction, we risk
> > falling into habits promoted by most commercial software, where one
> > points and click without understanding what one is doing.  The risk is
> > there even in GNU R, as the number of functions and packages keeps
> > growing to help us save time developing procedures.  There's a balance
> > to be reached between the help received and intellectual independence.
> > For classical statistics, many books have long series of editions that
> > have made them superb with age (like good wine).  Zar's Biostatistical
> > Analysis is my favorite in this domain, but I enjoyed Sokal & Rolf  
> > too.
> >
> >
> > Seb
> >
> >
> >
> > On Mon, 10 Nov 2008 16:11:47 -0500,
> > Brian Campbell <jacarebrazil98@hotmail.com> wrote:
> >
> >> I conceded to R shift (mostly) last year and began Crawley (2005)
> >> Statistics: An Introduction using R.  Quinn and Keough: Experimental
> >> Design and Data Analysis for Biologists is very useful, but if  
> >> given a
> >> choice of the two with the emphasis on learning R, Crawley might be
> >> preferable.  Better yet might even be the "R Book".
> >
> >> -Brian
> >
> >>> Date: Mon, 10 Nov 2008 12:30:22 -0800 From:
> >>> cparker@pdx.edu To:
> >>> r-sig-ecology@r-project.org Subject: Re:
> >>> [R-sig-eco] classical statistics in R
> >
> >>> I agree with Jordan and will also throw in Gelman and Hill's "Data
> >>> Analysis Using Regression and Multilevel/Hierarchical Models". Its a
> >>> social science based book but is very relevant to ecologists and
> >>> includes R code (and bugs code).  -Chris
> >
> >
> >>> Jordan Mayor wrote:
> >>>> Personally, I found G&E to be very helpful at only a cursory
> >>> interest level.  > Quinn & Keough's "Experimental Design and Data
> >>> Analysis for Biologists" is > a practical in-depth text that covers
> >>> allot more detail - but, alas no > R-code is provided.  In fact, it
> >>> is quite program-independent.
> >
> >>>> Cheers
> >
> >>>> On Mon, Nov 10, 2008 at 3:10 PM, tyler  
> >>>> <tyler.smith@mail.mcgill.ca> wrote:
> >
> >
> >>>> Hi,
> >
> >>>>> I've just received my copy of Ben Bolker's new book, "Ecological
> >>> Models >>and Data in R". I was a little surprised to see he
> >>> recommended Sokal and >>Rohlf's "Biometry" as an introduction to
> >>> classical stats. Not because >>there's anything wrong with S&R, it's
> >>> comprehensive and well-written.  >>My problem with this book is that
> >>> it's written from the perspective of >>filling out tables of sums of
> >>> squares according to fixed recipes, while >>R is geared towards more
> >>> flexible linear models. Trying to translate the >>more complex
> >>> recipes into R code is not a trivial task.
> >
> >>>>> In response to an email, Ben suggested that Gotelli and Ellison's
> >>>>> "Primer of Ecological Statistics" provides a more modern take on
> >>> the >>subject than S&R. I have to agree, G&E is one of the best
> >>> intros I've >>seen for ecologists. But it doesn't really go very far
> >>> into the possible >>complexities of ANOVA and linear regression, and
> >>> doesn't specifically >>address implementing tests in R.
> >
> >>>>> Ben and I are both curious as to what other r-sig-eco readers  
> >>>>> think
> >>>>> about this issue. What are the best sources for learning about
> >>> classical >>statistics as implemented in R? S&R has been the  
> >>> standard
> >>> reference for >>quite a while, but it now appears to be dated. Is
> >>> there a good standard >>text that covers the same breadth of  
> >>> material
> >>> with a modern, R-compatible >>approach? Ben also recommended several
> >>> books by Michael Crawley - any >>strong feelings on these, or other
> >>> suggestions?
> >
> >>>>> Thanks!
> >
> >>>>> Tyler
> >
> >>>>> -- >>Research is what I'm doing when I don't know what I'm doing.
> >>>>> --Wernher von Braun
> >
> >>>>> _______________________________________________ >>R-sig-ecology
> >>> mailing list >>R-sig-ecology@r-project.org
> >>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
> >
> >
> >
> >
> >
> >
> >>> _______________________________________________ R-sig-ecology  
> >>> mailing
> >>> list R-sig-ecology@r-project.org
> >>> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
> >
> >> _________________________________________________________________
> >
> >
> >> 	[[alternative HTML version deleted]]
> >
> >
> >
> > Cheers,
> >
> > -- 
> > Seb
> >
> > _______________________________________________
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> > R-sig-ecology@r-project.org
> > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
> 
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