On 1/28/2019 6:07 PM, William Dunlap wrote:
S (R's predecessor) was designed by and for data analysts. R generally
follows that tradition. I think that simulations such as yours are not
its strength, although it can make analyzing (graphically and
numerically) the results of the simulation fun.
At this point I think you're right on all counts.
Alan
Bill Dunlap
TIBCO Software
wdunlap tibco.com <http://tibco.com>
On Mon, Jan 28, 2019 at 4:00 PM Alan Feuerbacher <alan...@comcast.net
<mailto:alan...@comcast.net>> wrote:
On 1/28/2019 4:20 PM, Rolf Turner wrote:
>
> On 1/29/19 10:05 AM, Alan Feuerbacher wrote:
>
>> Hi,
>>
>> I recently learned of the existence of R through a physicist friend
>> who uses it in his research. I've used Octave for a decade, and
C for
>> 35 years, but would like to learn R. These all have advantages and
>> disadvantages for certain tasks, but as I'm new to R I hardly
know how
>> to evaluate them. Any suggestions?
>
> * C is fast, but with a syntax that is (to my mind) virtually
> incomprehensible. (You probably think differently about this.)
I've been doing it long enough that I have little problem with it,
except for pointers. :-)
> * In C, you essentially have to roll your own for all tasks; in R,
> practically anything (well ...) that you want to do has already
> been programmed up. CRAN is a wonderful resource, and there's
more
> on github.
>
> * The syntax of R meshes beautifully with *my* thought patterns;
YMMV.
>
> * Why not just bog in and try R out? It's free, it's readily
available,
> and there are a number of good online tutorials.
I just installed R on my Linux Fedora system, so I'll do that.
I wonder if you'd care to comment on my little project that prompted
this? As part of another project, I wanted to model population growth
starting from a handful of starting individuals. This is exponential in
the long run, of course, but I wanted to see how a few basic parameters
affected the outcome. Using Octave, I modeled a single person as a
"cell", which in Octave has a good deal of overhead. The program
basically looped over the entire population, and updated each person
according to the parameters, which included random statistical
variations. So when the total population reached, say 10,000, and an
update time of 1 day, the program had to execute 10,000 x 365 update
operations for each year of growth. For large populations, say 100,000,
the program did not return even after 24 hours of run time.
So I switched to C, and used its "struct" declaration and an array of
structs to model the population. This allowed the program to
complete in
under a minute as opposed to 24 hours+. So in line with your
comments, C
is far more efficient than Octave.
How do you think R would fare in this simulation?
Alan
---
This email has been checked for viruses by Avast antivirus software.
https://www.avast.com/antivirus
______________________________________________
R-help@r-project.org <mailto: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.
______________________________________________
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.
