Hi Sashi, Since I do not want to create a large fake data set and then painstakingly test and debug your code, why not try your code with a subset of the data, maybe only 400 rows. If that runs slowly, your code is very inefficient (it looks as though it is). You can then begin to identify where the efficiency of the code can be improved. Jim On Tue, Jun 14, 2016 at 10:41 PM, SHASHI SETH <sethshashi at rediffmail.com> wrote:> Dear Jim, > > Thanks for ur suggesion. Earlier problem is solved with ur advise. My code > is taking too long to > execute, more than 30 hours. there are 40309 rows and 26952 columns. file > size is 110 MB.Please guide > me what is wrong. > > Shashi > On Thu, 09 Jun 2016 14:27:17 +0530 Jim Lemon wrote >>Hi Shashi, > > Without trying to go through all that code, your error is something > > simple. When you read in "matrixdata" right in the beginning, you are > > getting a data frame, not a vector or a matrix (which in some cases > > can be treated like a vector). That will cause trouble at some point. > > Another thing is that when you call this: > > > > if((sum > 0 && sums1 > 0 && sums2 > 0) != NA) > > > > you seem to be asking for the union of three multi-valued vectors (?) > > which will probably cause at least a warning, but the error suggests > > that at least one of these objects has an NA value somewhere. This > > might be because "dtm_500_1.CSV" (whatever that is) has NA values in > > it. The code is fairly obscure and I can only say that your best bet > > is probably to check the initial data frame for NA values and then > > print out the results of each step, or least > > > > cat(sum(is.na(x)),"\n") > > > > where x is the object you have just created. That should allow you to > > find where in the tangle of code the NAs are appearing. > > > > Jim > > > > > > > > On Thu, Jun 9, 2016 at 4:49 PM, SHASHI SETH wrote: > >> Hi Jim, > >> > >> I am getting the following error: > >> Error in if ((sum > 0 && sums1 > 0 && sums2 > 0) != NA) { : > >> missing value where TRUE/FALSE needed > >> > >> > >> I have including my code below for your review: > >> > >> fitness_1_data <- c(); > >> > >> src="dtm_500_1.CSV" > >> matrixdata <- read.csv(src) > >> > >> #get no vector/column from file/matrix > >> noofvec <- length(matrixdata) > >> > >> #set no of records/rows/document > >> noofrecords <- length(matrixdata[,1]) > >> > >> #set row index > >> rindex<-1; > >> > >> #preapare header > >> colindex<-1; > >> colList <- colnames(matrixdata) > >> > >> combine<-""; > >> > >> vec_fitness_data<- c(); > >> > >> while(colindex <= length(colList)) > >> { > >> fitness_1_data <- append(fitness_1_data,colList[colindex]) > >> > >> colindex<- colindex+1 > >> } > >> > >> #add two additional vector for percentage and cluster > >> fitness_1_data <- append(fitness_1_data,"percentage") > >> fitness_1_data <- append(fitness_1_data,"Cluster") > >> > >> write.table(as.list(fitness_1_data), file ="Result_500_cycle1.csv",append >> > >> TRUE, > >> row.names=FALSE, col.names=FALSE, sep=",") > >> > >> #end header record > >> > >> nestedloopindex <- 2 > >> > >> > >> while( nestedloopindex <= noofrecords ) > >> { > >> > >> #init of temperory variables > >> sums1 <- 0; > >> sums2 <- 0; > >> sum <- 0; > >> > >> #set initial index of column 2 ,coloumn one hold document no not > >> actual data > >> colindex <- 2; > >> > >> # combine <-""; > >> > >> vec1 <- c(); > >> vec2 <- c(); > >> > >> #add document number in vector > >> vec1 <- append(vec1,matrixdata[rindex,1]); > >> vec2 <- append(vec2,matrixdata[nestedloopindex,1]); > >> > >> #declaration of temp -out variable for calculation > >> #out <- 0; > >> > >> > >> while(colindex <= noofvec ) > >> { > >> > >> > >> vec1 <- append(vec1,matrixdata[rindex,colindex]); > >> vec2 <- append(vec2,matrixdata[nestedloopindex,colindex]); > >> > >> sum = sum + > >> matrixdata[rindex,colindex]*matrixdata[nestedloopindex,colindex] > >> > >> sums1 <- sums1 + matrixdata[rindex,colindex]^2; > >> > >> sums2 <- sums2 + matrixdata[nestedloopindex,colindex]^2; > >> > >> colindex <- colindex+1 > >> } > >> > >> if((sum > 0 && sums1 > 0 && sums2 > 0) != NA) > >> { > >> > >> out <- sum / ((sqrt(sums1) * sqrt(sums2))) > >> }else > >> { > >> out <-0 > >> } > >> > >> vec1 <- append(vec1,out); > >> vec1 <-append(vec1, "1") > >> vec2 <- append(vec2, out); > >> > >> > >> > >> if(nestedloopindex==2) > >> { > >> write.table(as.list(vec1), file ="Result_500_cycle1.csv",append > >> TRUE, row.names=FALSE, col.names=FALSE, sep=",") > >> write.table(as.list(vec2), file ="Result_500_cycle1.csv",append > >> TRUE, row.names=FALSE, col.names=FALSE, sep=",") > >> nestedloopindex<- nestedloopindex+1 > >> } else > >> { > >> write.table(as.list(vec2), file ="Result_500_cycle1.csv",append > >> TRUE, row.names=FALSE, col.names=FALSE, sep=",") > >> nestedloopindex<- nestedloopindex+1 > >> } > >> > >> } > >> > >> > >> With Best Regards, > >> Shashi > >> > >> On Thu, 09 Jun 2016 04:45:09 +0530 Jim Lemon wrote > >>>Hi John, > >> > >> With due respect to the other respondents, here is something that might > >> help: > >> > >> > >> > >> # get a vector of values > >> > >> foo<-rnorm(100) > >> > >> # get a vector of increasing indices (aka your "recent" values) > >> > >> bar<-sort(sample(1:100,40)) > >> > >> # write a function to "clump" the adjacent index values > >> > >> clump_adj_int<-function(x) { > >> > >> index_list<-list(x[1]) > >> > >> list_index<-1 > >> > >> for(i in 2:length(x)) { > >> > >> if(x[i]==x[i-1]+1) > >> > >> index_list[[list_index]]<-c(index_list[[list_index]],x[i]) > >> > >> else { > >> > >> list_index<-list_index+1 > >> > >> index_list[[list_index]]<-x[i] > >> > >> } > >> > >> } > >> > >> return(index_list) > >> > >> } > >> > >> index_clumps<-clump_adj_int(bar) > >> > >> # write another function to sum the values > >> > >> sum_subsets<-function(indices,vector) > >> return(sum(vector[indices],na.rm=TRUE)) > >> > >> # now "apply" the function to the list of indices > >> > >> lapply(index_clumps,sum_subsets,foo) > >> > >> > >> > >> Jim > >> > >> > >> > >> > >> > >> On Thu, Jun 9, 2016 at 2:41 AM, John Logsdon > >> > >> wrote: > >> > >>> Folks > >> > >>> > >> > >>> Is there any way to get the row index into apply as a variable? > >> > >>> > >> > >>> I want a function to do some sums on a small subset of some very long > >> > >>> vectors, rolling through the whole vectors. > >> > >>> > >> > >>> apply(X,1,function {do something}, other arguments) > >> > >>> > >> > >>> seems to be the way to do it. > >> > >>> > >> > >>> The subset I want is the most recent set of measurements only - perhaps a > >> > >>> couple of hundred out of millions - but I can't see how to index each > >> > >>> value. The ultimate output should be a matrix of results the length of > >> > >>> the input vector. But to do the sum I need to access the current row > >> > >>> number. > >> > >>> > >> > >>> It is easy in a loop but that will take ages. Is there any vectorised > >> > >>> apply-like solution to this? > >> > >>> > >> > >>> Or does apply etc only operate on each row at a time, independently of > >> > >>> other rows? > >> > >>> > >> > >>> > >> > >>> Best wishes > >> > >>> > >> > >>> John > >> > >>> > >> > >>> John Logsdon > >> > >>> Quantex Research Ltd > >> > >>> +44 161 445 4951/+44 7717758675 > >> > >>> > >> > >>> ______________________________________________ > >> > >>> R-help at 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. > >> > >> > >> > >> ______________________________________________ > >> > >> R-help at 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. > >> > >> > >