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Note that if you force a garbage collection each iteration the times are more stable. However, on the average it is faster to let the garbage collector decide when to leap into action. mb_gc <- microbenchmark::microbenchmark(gc(), { x <- as.list(sin(1:5e5)); x <- unlist(x) / cos(1:5e5) ; sum(x) }, times=1000, control=list(order="inorder")) with(mb_gc, plot(time[expr!="gc()"])) with(mb_gc, quantile(1e-6*time[expr!="gc()"], c(0, .5, .75, .9, .95, .9...

The large value for maximum time may be due to garbage collection, which happens periodically. E.g., try the following, where the unlist(as.list()) creates a lot of garbage. I get a very large time every 102 or 51 iterations and a moderately large time more often mb <- microbenchmark::microbenchmark({ x <- as.list(sin(1:5e5)); x <- unlist(x) / cos(1:5e5) ; sum(x) }, times=1000)

Dear all I was thinking about efficient reading data into R and tried several ways to test if load(file.Rdata) or readRDS(file.rds) is faster. The files file.Rdata and file.rds contain the same data, the first created with save(d, ' file.Rdata', compress=F) and the second with saveRDS(d, ' file.rds', compress=F). First I used the function microbenchmark() and was a astonished