r - Why is mean() so slow? -


everything in question! tried bit of optimization, , nailing down bottle necks, out of curiosity, tried that:

t1 <- rnorm(10) microbenchmark(   mean(t1),   sum(t1)/length(t1),   times = 10000)

and result mean() 6+ times slower computation "by hand"!

does stem overhead in code of mean() before call internal(mean) or c code slower? why? there reason , use case?

it due s3 method, , necessary parsing of arguments in mean.default. (and other code in mean)

sum , length both primitive functions. fast (but how handling na values?)

t1 <- rnorm(10) microbenchmark(   mean(t1),   sum(t1)/length(t1),   mean.default(t1),   .internal(mean(t1)),   times = 10000)  unit: nanoseconds                 expr   min    lq median    uq     max neval             mean(t1) 10266 10951  11293 11635 1470714 10000   sum(t1)/length(t1)   684  1027   1369  1711  104367 10000     mean.default(t1)  2053  2396   2738  2739 1167195 10000  .internal(mean(t1))   342   343    685   685   86574 10000

the internal bit of mean faster sum/length.

see http://rwiki.sciviews.org/doku.php?id=packages:cran:data.table#method_dispatch_takes_time more details (and data.table solution avoids .internal

note if increase length of vector, primitive approach fastest

t1 <- rnorm(1e7) microbenchmark(      mean(t1),      sum(t1)/length(t1),      mean.default(t1),      .internal(mean(t1)), +     times = 100)  unit: milliseconds                 expr      min       lq   median       uq      max neval             mean(t1) 25.79873 26.39242 26.56608 26.85523 33.36137   100   sum(t1)/length(t1) 15.02399 15.22948 15.31383 15.43239 19.20824   100     mean.default(t1) 25.69402 26.21466 26.44683 26.84257 33.62896   100  .internal(mean(t1)) 25.70497 26.16247 26.39396 26.63982 35.21054   100

now method dispatch fraction of overall "time" required.


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