Geek asks and answers

Remember and vectorize user function

I want to know how to vectorize and memoize a user-defined function in R. It seems my way of thinking is not consistent with the R-mode of operation. So, I gladly welcome any links to good reading material. For example, R inferno is a good resource, but this did not help to define memoirization in R.

More generally, can you provide an appropriate use case for memoise or R.cache?

I could not find other discussions on this. a search for "memoise" or "memoize" on r-bloggers.com returns zero results. a search for these keywords http://r-project.markmail.org/ does not return discussions. I emailed a mailing list and did not receive the full Response.

I'm not only interested in the memory of the GC function, and I know the Bioconductor and the various packaging available there.

Here is my details:

seqs <- c("","G","C","CCC","T","","TTCCT","","C","CTC")

Some sequences are missing, so they are empty "".

I have a function to calculate the contents of the GC:

> GC <- function(s) {
    if (!is.character(s)) return(NA)
    n <- nchar(s)
    if (n == 0) return(NA)
    m <- gregexpr('[GCSgcs]', s)[[1]]
    if (m[1] < 1) return(0)
    return(100.0 * length(m) / n)
}

Works:

> GC('')
[1] NA
> GC('G')
[1] 100
> GC('GAG')
[1] 66.66667
> sapply(seqs, GC)
                  G         C       CCC         T               TTCCT           
       NA 100.00000 100.00000 100.00000   0.00000        NA  40.00000        NA 
        C       CTC 
100.00000  66.66667

I want to perpetuate it. Then I want to vectorize it.

Apparently, I should have the wrong setup for using memoiseeither R.cacheR packages:

> system.time(dummy <- sapply(rep(seqs,100), GC))
   user  system elapsed
  0.044   0.000   0.054
>
> library(memoise)
> GCm1 <- memoise(GC)
> system.time(dummy <- sapply(rep(seqs,100), GCm1))
   user  system elapsed
  0.164   0.000   0.173
>
> library(R.cache)
> GCm2 <- addMemoization(GC)
> system.time(dummy <- sapply(rep(seqs,100), GCm2))
   user  system elapsed
 10.601   0.252  10.926

, memoized .

hash, , , - , . C 100, NULL.

, has.key(s, cache) exists(s, cache) . , cache[s] <<- result cache[[s]] <<- result .

> cache <- hash()
> GCc <- function(s) {
    if (!is.character(s) || nchar(s) == 0) {
        return(NA)
    }
    if(exists(s, cache)) {
        return(cache[[s]])
    }
    result <- GC(s)
    cache[[s]] <<- result
    return(result)
}
> sapply(seqs,GCc)
[[1]]
[1] NA

$G
[1] 100

$C
NULL

$CCC
[1] 100

$T
NULL

[[6]]
[1] NA

$TTCCT
[1] 40

[[8]]
[1] NA

$C
NULL

$CTC
[1] 66.66667

, , :

> GCv <- Vectorize(GC)
> GCv(seqs)
                  G         C       CCC         T               TTCCT           
       NA 100.00000 100.00000 100.00000   0.00000        NA  40.00000        NA 
        C       CTC 
100.00000  66.66667

stackoverflow:

+3
2

, , , . , Joshua GC2 ( fixed = T, ):

GC2 <- function(s) {
  if(!is.character(s)) stop("'s' must be character")
  n <- nchar(s)
  m <- gregexpr('[GCSgcs]', s)
  len <- sapply(m, length)
  neg <- sapply(m, "[[", 1)
  len <- len*(neg > 0)
  100.0 * len/n
}

, :

GC3 <- function(s) {
  x <- factor(s)
  GC2(levels(x))[x]
}

system.time(GC2(rep(seqs, 50000)))
# user  system elapsed 
# 8.97    0.00    8.99 
system.time(GC3(rep(seqs, 50000)))
# user  system elapsed 
# 0.06    0.00    0.06
+2

, 4 , .

GC2 <- function(s) {
  if(!is.character(s)) stop("'s' must be character")
  n <- nchar(s)
  m <- gregexpr('[GCSgcs]', s)
  len <- sapply(m, length)
  neg <- sapply(m, "[[", 1)
  len <- len*(neg > 0)
  len/n
}
+1

More articles:


All Articles