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 <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 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 ______________________________________________ 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.
