>
> G'day
>
> I just tried to introduce a naive user to R via doing a t-test on her
> data using Rcmdr on OS X. Now I'm not sure if this is the right  
> mailing
> list but I must say that  the introduction was a complete and total
> failure because of the OSX data editor that is called by Rcmdr. I  
> think
> this is the one that comes with the R GUI. We tried to enter her data
> into R and I couldn't do it using the OS X data editor. When I had  
> to go
> to the command line she was clearly initialy put off by the poor  
> editor
> and by the need for the command line to fix it.  As she said, "how  
> can I
> trust a stats program with my important data if they can't get data
> entry working?". She promptly went back to Instat

> I know that the criticism she voiced isn't valid and that some  
> learning
> is required for any program, but I think she has a point. The data
> editor as it stands is a failure. The idea that you can add a column  
> but
> not change the variable name and that you have to double click on each
> cell to enter data is crazy. It is simply a pretty looking window with
> less functionality than the old x11 interface.
>
Yes, the data editor is  not that useful, and I be surprised if anyone  
uses it much at all.
> I'm aware that the current model is "prepare your data elsewhere and
> import it into R" but this is an absurd stance.

Why is this an absurd stance? Entering a small data set like the one  
you describe can be done in TextEdit or any other text editor:

"Variable1", "Variable2"
x1,	y1
x2, 	y2
...  	...

Or, if you must have a spreadsheet, you can use OpenOffice and save  
to .csv.

> It works for large data
> sets that statistical experts deal with but then the  casual t-test
> requires several programs and an import step. A silly approach
> especially when the data sets are small (about 10-20 entries) and all
> the user wants to do is a simple t-test.
> If braoder adoption of R is an aim then the OS X data editor needs  
> to be
> at least as functional as the X11 one. The ability to double click to
> change variable names and right click to change the variable type (or
> menu entries to do this) is important if not essential. The ability to
> tab to the next data entry slot is also a simple but important  
> function
> that needs to be included.  Right now it is a barrier to new OS X  
> users
> who want to try out R using a simple t-test on a small data set.
>
>
This is probably true. At the end of the day R is a command driven  
program. The Rcmdr GUI is an important learning tool, but it was never  
designed to replace the command line. I hated this at first, being  
trained as I was on SPSS, but in the long run I suspect you're going  
to be better off if you give up on R as a GUI driven tool.

This is not to say that the Rcmdr interface is not useful: it is very  
useful, but primarily as a teaching tool (i.e., a way to learn R  
commands).

>
> From: "Peter Cowan" <cowan.pd@gmail.com>
> Date: March 11, 2008 1:18:20 AM EDT
> To: r-sig-mac@stat.math.ethz.ch
> Subject: Re: [R-SIG-Mac] OSX R Gui Data editor
>
>
> John,
>
> I think you may be mixing up the R package Rcmdr [^1] with the Mac OS
> X R gui.  I've haven't used Rcmdr in years so I cannot comment on your
> issue, but I think you were commenting on the Mac gui.
>
He clearly stated he was using the OS X data editor, called from Rcmdr.

> <snip>
>
> If your colleague isn't interesting in learning to use a command line
> program, then I suspect that R is not the correct choice for her.
>
Yes, exactly, although I agree this can be a problem because it scares  
people off.
>
> From: "Byron Ellis" <byron.ellis@gmail.com>
> Date: March 11, 2008 2:59:36 AM EDT
> To: john.s.walker@uchsc.edu
> Cc: r-sig-mac@stat.math.ethz.ch
> Subject: Re: [R-SIG-Mac] OSX R Gui Data editor
>
>
> Your colleague's reaction is completely correct. If all you need to do
> is hand enter 20 data points and run a t-test then even Instat is
> probably overkill and R is definitely overkill.
> <snip>
I strongly disagree with this sentiment. I started using R as a  
calculator believe it or not. R is very good for simple as well as  
complex statistics.

> Think of R like a big industrial
> CNC mill or something. It's big, clunky and kind of a pain, but if you
> need to make a set of intricate wheel rims to very high tolerances
> there is nothing on this earth that can match it. However, not so
> great for paper snowflakes, unless you've been using it forever in
> which case its mostly out of habit... and you've lost your scissors.
Again, I think this is just wrong. Doing simple t-tests in R is easy  
once you get used to the program. What exactly is so "big and clunky"  
about R?
<snip>

Ista 
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