**librestats » R**, and kindly contributed to R-bloggers)

I was hoping to begin tinkering a bit with the multicore package in R beyond some extremely trivial examples. Thanks to a combination of R’s dumb quirkiness (for example, being worthless on loops), my poor planning, and general bad programming, my Saturday afternoon tinkering project is ultimately worthless in fulfilling that purpose.

I was really hoping to take the prime searcher I had been using to solve Project Euler problems and use it to make a prime testing function which would utilize the 4 cores on my adorable little core i5. I succeeded in creating it, but it wasn’t until I had finished (which thankfully was only about an hour of time wasted) that I realized that my plan was stupid and I was going to have to loop over entries (or use sapply(), which is the same thing; don’t kid yourself). This, as we all know, is where R is fantastically bad.

A very hasty test showed that relying on a single core, the original way I was doing things is a little over 2 times faster than this new implementation. This doesn’t bode well for the possible speedup when scaling up to 4 cores, and since I have more pressing projects at the moment, I’m going to have to dump this one barring some illumination.

However, it is a workable prime tester. And for testing against single primes, it’s honestly not *that* slow. But, as pointed out above, if you’re using it to test primality of a (reasonably large) range of (reasonably large) values, then you’re going to have to loop and R is going to catch fire in the process.

The syntax is simple enough; simply conjure your integer of choice and ask R

IsPrime()

which is like the old Maple syntax back when I used it extensively (which was ~ Maple 8 or 9, I believe). Functions and sample output below:

IsPrime <- function(n){ # n=Integer you want to know if is/not prime if ((n-floor(n)) > 0){ cat(sprintf("Error: function only accepts natural number inputs\n")) } else if (n < 1){ cat(sprintf("Error: function only accepts natural number inputs\n")) } else # Prime list exists if (try(is.vector(primes), silent=TRUE) == TRUE){ # Prime list is already big enough if (n %in% primes){ TRUE } else if (n < tail(primes,1)){ FALSE } else if (n <= (tail(primes,1))^2){ flag <- 0 for (prime in primes){ if (n%%prime == 0){ flag <- 1 break } } if (flag == 0){ TRUE } else { FALSE } } # Prime list is too small; get more primes else { last.known <- tail(primes,1) while ((last.known)^2 < n){ assign("primes", c(primes,GetNextPrime(primes)), envir=.GlobalEnv) last.known <- tail(primes,1) } IsPrime(n) } } else { # Prime list does not exist assign("primes", PrimesBelow(n,below.sqrt=TRUE), envir=.GlobalEnv) IsPrime(n) } } # Get next prime GetNextPrime <- function(primes){ # primes=Known prime list i <- tail(primes,1) while (TRUE){ flag <- 0 i <- i+2 if (i%%6 == 3){ flag <- 1 } if (flag == 0){ s <- sqrt(i)+1 possible.primes <- primes[primes<s] for (prime in possible.primes){ if ((i%%prime == 0)){ flag <- 1 break } } if (flag == 0){ break } } } i } # Primes below specified integer n; optionally only those below sqrt(n) PrimesBelow <- function(n, below.sqrt=FALSE){ if (below.sqrt == TRUE){ m <- ceiling(sqrt(n)) } else { m <- n } primes <- c(2,3) i <- 3 while (i < m-1){ flag <- 0 i <- i+2 if (i%%6 == 3){ flag <- 1 } if (flag == 0){ s <- sqrt(i)+1 possible.primes <- primes[primes<s] for (prime in possible.primes){ if ((i%%prime == 0)){ flag <- 1 break } } if (flag == 0){ primes <- c(primes, i) } } } primes }

Some sample output:

> primes Error: object 'primes' not found > IsPrime(100) [1] FALSE > IsPrime(101) [1] TRUE > primes [1] 2 3 5 7 11 >

After screwing around with the prime tester for a few minutes, I was able to find this adorable little gem

1> IsPrime(1234567891) [1] TRUE

So 1234567891 is prime, but none of 1, 12, 123, 1234, 12345, 123456, 1234567, 12345678, 123456789, 12345678910, 123456789101, or 1234567891011 are (of course, nearly half of those are even, but the point stands).

**leave a comment**for the author, please follow the link and comment on their blog:

**librestats » R**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...