On Sat, Apr 23, 2011 at 11:35 PM, Thomas Levine <thomas.lev...@gmail.com> wrote:
> This should do the same thing
Did you actually test it? I get very different things.
>
> random.del <- function (x, n.keeprows, del.percent){
> del<-function(col){
> col[sample.int(length(col),length(col)*del.percent/100)]<-NA
> col
> }
> change<-n.keeprows:nrow(x)
> x[change,]<-lapply(x[change,],del)
but a data frame is a list of vectors column wise, while Sergey's
function went row by row. However, using sample.int() is a much
better idea than what I did with sample().
> x
> }
>
> This is faster because it's vectorized.
but in such a way that you cannot guarantee the same number of cells
are missing from each row. Try:
rowSums(is.na("Mine"))
>
> [1] "Mine"
> user system elapsed
> 0.004 0.000 0.002
> [1] "Yours"
> user system elapsed
> 1.172 0.020 1.193
>
> Tom
>
> On Sat, Apr 23, 2011 at 8:37 PM, sneaffer <sneaf...@mail.ru> wrote:
>>
>> Hello R-world,
>> Please, help me to get round my little mess
>> I have a data.frame in which I'd rather like some values to be NA for the
>> future imputation process.
>>
>> I've come up with the following piece of code:
>>
>> random.del <- function (x, n.keeprows, del.percent){
>> n.items <- ncol(x)
>> k <- n.items*(del.percent/100)
>> x.del <- x
>> for (i in (n.keeprows+1):nrow(x)){
>> j <- sample(1:n.items, k)
>> x.del[i,j] <- NA
>> }
>> return (x.del)
>> }
>>
>> The problems is that random.del turns out to be slow on huge samples.
>> Is there any other more effective/charming way to do the same?
>>
>> Thanks,
>> Sergey
>>
>> --
>> View this message in context:
>> http://r.789695.n4.nabble.com/How-to-erase-replace-certain-elements-in-the-data-frame-tp3470883p3470883.html
>> Sent from the R help mailing list archive at Nabble.com.
>>
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>
> ______________________________________________
> R-help@r-project.org mailing list
> 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.
>
--
Joshua Wiley
Ph.D. Student, Health Psychology
University of California, Los Angeles
http://www.joshuawiley.com/
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