R help - Jan 2019 - [FORGED] Newbie Question on R versus Matlab/Octave versus C

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

I would say your question is foolish -- you disagree no doubt! -- because
the point of using R (or Octave or C++) is to take advantage of the
packages (= "libraries" in some languages; a library is something
different
in R) it (or they) offers to simplify your task. Many of R's libraries are
written in C (or Fortran) an thus **are** fast as well as having
task-appropriate functionality and UI's .

So I think instead of pursuing this discussion you would do well to search.
I find rseek.org to be especially good for this sort of thing. Searching
there on "demography" brought up what appeared to be many appropriate
hits
-- including the "demography" package! -- which you could then examine
to
see whether and to what extent they provide the functionality you seek.

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 Mon, Jan 28, 2019 at 4:00 PM Alan Feuerbacher <alanf00 at 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 at 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]]

This would be a suitable application for NetLogo.  The R package
RNetLogo provides an interface.  In a few lines of code you get a
simulation with graphics.

On Mon, Jan 28, 2019 at 7:00 PM Alan Feuerbacher <alanf00 at 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 at 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.


-- 
Statistics & Software Consulting
GKX Group, GKX Associates Inc.
tel: 1-877-GKX-GROUP
email: ggrothendieck at gmail.com

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.

Bill Dunlap
TIBCO Software
wdunlap tibco.com


On Mon, Jan 28, 2019 at 4:00 PM Alan Feuerbacher <alanf00 at 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 at 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]]

If you forge on with your preconceptions of how such a simulation should be
implemented then you will be able to reproduce your failure just as
spectacularly using R as you did using Octave. It is crucial to employ
vectorization of your algorithms if you want good performance with either Octave
or R. That vectorization may either be over time or over separate simulations.

I am running simulations of a million cases of power plant performance over 25
years in about a minute. I know someone who used R to simulate a CFD river flow
problem in a class in a few minutes, while others using Fortran or Matlab were
struggling to get comparable runs completed in many hours. I believe the
difference was in how the data were structured and manipulated more than the
language that was being used. I think the strong capabilities for presenting
results using R makes using it advantageous over Octave, though.

If your problems truly need a compiled language, the Rcpp package lets you mix
C++ with R quite easily and then you get the best of both worlds. (C and Fortran
are supported, but they are a bit more finicky to setup than C++).

On January 28, 2019 4:00:07 PM PST, Alan Feuerbacher <alanf00 at
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 at 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.
-- 
Sent from my phone. Please excuse my brevity.

On 1/28/19 4:00 PM, Alan Feuerbacher 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?
>> >
> snpped
>> * 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?
>
This sounds like a problem that would fit into a stochastic differential 
equation.? There are at least three packages in CRAN (and I suspect a 
few more) that will handle simulations of stochastic differential 
equations. Bert's suggestion to use Rseek should serve you well.


-- 

David.

On 1/28/2019 5:17 PM, Bert Gunter wrote:
> I would say your question is foolish -- you disagree no doubt! -- 
> because the point of using R (or Octave or C++) is to take advantage of 
> the packages (= "libraries" in some languages; a library is
something
> different in R) it (or they) offers to simplify your task. Many of R's 
> libraries are written in C (or Fortran) an thus **are** fast as well as 
> having task-appropriate functionality and UI's .
Yes, I'm well aware of the libraries in Octave. But so far as I was able 
to see, none of them fit my needs. I used Octave at first because I'm 
familiar with it. But far from an expert.
> So I think instead of pursuing this discussion you would do well to 
> search. I find rseek.org <http://rseek.org> to be especially good for
> this sort of thing. Searching there on "demography" brought up
what
> appeared to be many appropriate hits -- including the
"demography"
> package! -- which you could then examine to see whether and to what 
> extent they provide the functionality you seek.
I looked over the demography package, and it indeed appears to do what I 
want. But it seems to be far more complicated than my simple problem, 
and has a large learning curve.

Alan
> 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 Mon, Jan 28, 2019 at 4:00 PM Alan Feuerbacher <alanf00 at comcast.net
> <mailto:alanf00 at 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 at r-project.org <mailto:R-help at 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.
>

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 <alanf00 at comcast.net
> <mailto:alanf00 at 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 at r-project.org <mailto:R-help at 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.
>

On 1/28/2019 7:51 PM, Jeff Newmiller wrote:
> If you forge on with your preconceptions of how such a simulation should be
implemented then you will be able to reproduce your failure just as
spectacularly using R as you did using Octave.
I think I've come to the same conclusion. :-)
> It is crucial to employ vectorization of your algorithms if you want good
performance with either Octave or R. That vectorization may either be over time
or over separate simulations.
Please explain further, if you don't mind. My background is not in 
programming, but in analog microchip circuit design (I'm now retired). 
Thus I'm a user of circuit simulators, not a programmer of them. Also, 
I'm running this stuff on my home computers, either Linux or Windows 
machines.
> I am running simulations of a million cases of power plant performance over
25 years in about a minute. I know someone who used R to simulate a CFD river
flow problem in a class in a few minutes, while others using Fortran or Matlab
were struggling to get comparable runs completed in many hours. I believe the
difference was in how the data were structured and manipulated more than the
language that was being used. I think the strong capabilities for presenting
results using R makes using it advantageous over Octave, though.
After my failed attempt at using Octave, I realized that most likely the 
main contributing factor was that I was not able to figure out an 
efficient data structure to model one person. But C lent itself 
perfectly to my idea of how to go about programming my simulation. So 
here's a simplified pseudocode sort of example of what I did:

To model a single reproducing woman I used this C construct:

typedef struct woman {
   int isAlive;
   int isPregnant;
   double age;
   . . .
} WOMAN;

Then I allocated memory for a big array of these things, using the C 
malloc() function, which gave me the equivalent of this statement:

WOMAN women[NWOMEN];  /* An array of NWOMEN woman-structs */

After some initialization I set up two loops:

for( j=0; j<numberOfYears; j++) {
   for(i=1; i< numberOfWomen; i++) {
     updateWomen();
   }
}

The function updateWomen() figures out things like whether the woman 
becomes pregnant or gives birth on a given day, dies, etc.

I added other refinements that are not relevant here, such as random 
variations of various parameters, using the GNU Scientific Library 
random number generator functions.

If you can suggest a data construct in R or Octave that does something 
like this, and uses your idea of vectorization, I'd like to hear it. I'd
like to implement it and compare results with my C implementation.
> If your problems truly need a compiled language, the Rcpp package lets you
mix C++ with R quite easily and then you get the best of both worlds. (C and
Fortran are supported, but they are a bit more finicky to setup than C++).
I don't know the answer to that, but perhaps you can help decide.

Alan

> On January 28, 2019 4:00:07 PM PST, Alan Feuerbacher <alanf00 at
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
>>
>>
>> ---
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>>
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>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>