On 2014-03-16 23:56, Duncan Murdoch wrote:
On 14-03-16 2:57 PM, Göran Broström wrote:I have always known that "matrices are faster than data frames", for instance this function: dumkoll <- function(n = 1000, df = TRUE){ dfr <- data.frame(x = rnorm(n), y = rnorm(n)) if (df){ for (i in 2:NROW(dfr)){ if (!(i %% 100)) cat("i = ", i, "\n") dfr$x[i] <- dfr$x[i-1] } }else{ dm <- as.matrix(dfr) for (i in 2:NROW(dm)){ if (!(i %% 100)) cat("i = ", i, "\n") dm[i, 1] <- dm[i-1, 1] } dfr$x <- dm[, 1] } } -------------------- > system.time(dumkoll()) user system elapsed 0.046 0.000 0.045 > system.time(dumkoll(df = FALSE)) user system elapsed 0.007 0.000 0.008 ---------------------- OK, no big deal, but I stumbled over a data frame with one million records. Then, with df = TRUE, ---------------------------- user system elapsed 44677.141 1271.544 46016.754 ---------------------------- This is around 12 hours. With df = FALSE, it took only six seconds! About 7500 time faster. I was really surprised by the huge difference, and I wonder if this is to be expected, or if it is some peculiarity with my installation: I'm running Ubuntu 13.10 on a MacBook Pro with 8 Gb memory, R-3.0.3.I don't find it surprising. The line dfr$x[i] <- dfr$x[i-1] will be executed about a million times. It does the following:
Thanks for the explanation; I got the idea that dfr[1, i] <- might be faster than dfr$x[i] <- , but it is in fact significantly slower.
Helpful experience. Göran
1. Get a pointer to the x element of dfr. This requires R to look through all the names of dfr to figure out which one is "x". 2. Extract the i-1 element from it. Not particularly slow. 3. Get a pointer to the x element of dfr again. (R doesn't cache these things.) 4. Set the i element of it to a new value. This could require the entire column or even the entire dataframe to be copied, if R hasn't kept track of the fact that it is really being changed in place. In a complex assignment like that, I wouldn't be surprised if that took place. (In the matrix equivalent, it would be easier to recognize that it is safe to change the existing value.) Luke Tierney is making some changes in R-devel that might help a lot in cases like this, but I expect the matrix code will always be faster. Duncan Murdoch
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