R data.table speed up SI / Metric Conversion

So here's the situation. I've got an 85 Million row table with 18 columns. Three of these columns have values in Metric Prefix / SI notation (See Metric Prefix on Wikipedia).

This means I have number like :

.1M instead of 100000 or 1e+5, or
1K instead of 1000 or 1e+3

Sample data.table is

          V1     V2   V3  V4  V5  V6 V7 V8 V9  V10 V11 V12 V13 V14 V15 V16 V17 V18
 1: 2014-03-25 12:15:12 58300 3010 44.0  4.5 0.0   0   0  0.8  50 0.8 10K 303 21K   0     a   56
 2: 2014-03-25 12:15:12 56328 3010 28.0 12.0 0.0   0   0  0.3  60 0.0  59  62 .1M   0     a   66
 3: 2014-03-25 12:15:12 21082 3010 10.0  1.7 0.0   0   0 14.0  72 0.3  4K 208  8K   1     a   80
 4: 2014-03-25 12:15:12 59423 3010 12.0  0.0 0.2   0   0 88.0   0 0.0  20  16  71   0     a   26
 5: 2014-03-25 12:15:12 59423 3010  9.6  1.4 0.0   0   0 60.0  29 0.2  2K 251  6K   0     a   56
 6: 2014-03-25 12:15:12 24193 3010  8.3  1.9 0.0   0   0  9.9  80 0.3  3K 264  8K   1     a   71
 7: 2014-03-25 12:15:12 21082 3010  7.1  1.7 0.4   0   0  6.3  83 0.3  3K 197  7K   0     a   71
 8: 2014-03-25 12:15:12 59423 3010  4.6  1.2 0.0   0   0 57.0  37 0.1 998  81  7K   0     a  118

I modified a function written by Hans-Jörg Bibiko who used it to modify ggplot2 scales. See website here if you are iterested. The function I ended up using is :

sitor <- function(x)
{
  conv <- paste("E", c(seq(-24 ,-3, by=3), -2, -1, 0, seq(3, 24, by=3)), sep="")
  names(conv) <- c("y","z","a","f","p","n","µ","m","c","d","","K","M","G","T","P","E","Z","Y")
  x <- as.character(x)
  num <- function(x) as.numeric(
      paste(
        strsplit(x,"[A-z|µ]")[[1]][3],
        ifelse(substr(paste(strsplit(x,"[0-9|\\.]")[[1]], sep="", collapse=""), 1, 1) == "",
               "",
               conv[substr(paste(strsplit(x,"[0-9|\\.]")[[1]], sep="", collapse=""), 1, 1)]
        ),
        sep=""
      )
    )
  return(lapply(x,num))
}

I apply it to by data table to update 3 columns like

temp[ ,`:=`(V13=sitor(V13),V14=sitor(V14),V15=sitor(V15)) ]

I have applied a data.table key vector to the temp table with

setkeyv(temp,c("V1","V2","V3","V18"))

Any 61 minutes later I am still here waiting for a result... Some tips on how to speed up this conversion would be really handy given that my data size is about to grow 4 to 5 times.

Solution

Why don't you try sitools library?

library(data.table)
dt<-data.table(var = sample(x=1:1e5, size=1e6, replace=T))
library(sitools)
> system.time(dt[, var2 := f2si(var)])
   user  system elapsed 
  10.08    0.09   10.89

EDIT: this is a data.table based function that reverse f2si from sitools package:

si2f<-function(x){
  if(is.numeric(x)) return(x)
  require(data.table)
  dt<-data.table(lab=c("y","z","a","f","p","n","µ","m","c","d","", "da", "h", "k","M","G","T","P","E","Z","Y"),
                 mul=c(1e-24, 1e-21, 1e-18, 1e-15, 1e-12, 1e-9, 1e-6, 1e-3, 1e-2, 1e-1, 1L, 10L, 1e2, 1e3, 1e6, 1e9, 1e12, 1e15, 1e18, 1e21, 1e24),
                 key="lab")
  res<-as.numeric(gsub("[^0-9|\\.]","", x))
  x<-gsub("[0-9]|\\s+|\\.","", x)
  .subset2(dt[.(x)], "mul")*res
}

> system.time(dt[, var3 := si2f(var2)])
   user  system elapsed 
  13.18    0.03   13.31 

> dt[, all.equal(var,var3)]
[1] TRUE