r - Collecting an unknown number of results in a loop -

what idiomatic way collect results in loop in r if number of final results not known beforehand? here's toy example:

results = vector('integer') i=1l while (i < bigbigbignumber)  {     if (somecondition(i)) results = c(results, i)     = i+1 } results 

the problem example (i assume) have quadratic complexity vector needs re-allocated @ every append. (is correct?) i'm looking solution avoids this.

i found filter, requires pre-generating 1:bigbigbignumber want avoid conserve memory. (question: for (i in 1:n) pre-generate 1:n , keep in memory?)

i make linked list this:

results = list() i=1l while (i < bigbigbignumber)  {     if (somecondition(i)) results = list(results, i)     = i+1 } unlist(results) 

(note not concatenation. it's building structure list(list(list(1),2),3), flattening unlist.)

is there better way this? idiomatic way that's typically used? (i new r.) i'm looking suggestion on how tackle type of problem. suggestions both compact (easy write) , fast code welcome! (but i'd focus on fast , memory efficient.)

here algorithm doubles size of output list fills up, achieving linear computation times show benchmark tests:

test <- function(bigbigbignumber = 1000) {    n <- 10l   results <- vector("list", n)   m <- 0l   <- 1l   while (i < bigbigbignumber)  {     if (runif(1) > 0.5) {       m <- m + 1l       results[[m]] <-       if (m == n) {         results <- c(results, vector("list", n))         n <- n * 2l       }     }     = + 1l   }   unlist(results) }  system.time(test(1000)) #    user  system elapsed  #   0.008   0.000   0.008  system.time(test(10000)) #    user  system elapsed  #   0.090   0.002   0.093  system.time(test(100000)) #    user  system elapsed  #   0.885   0.051   0.936  system.time(test(1000000)) #    user  system elapsed  #   9.428   0.339   9.776