Summarize bench::mark results.

Source: R/mark.R

Summarize bench::mark results.

# S3 method for bench_mark
summary(object, filter_gc = TRUE, relative = FALSE, time_unit = NULL, ...)

Arguments

object

bench_mark object to summarize.
filter_gc

If TRUE remove iterations that contained at least one garbage collection before summarizing. If TRUE but an expression had a garbage collection in every iteration, filtering is disabled, with a warning.
relative

If TRUE all summaries are computed relative to the minimum execution time rather than absolute time.
time_unit

If NULL the times are reported in a human readable fashion depending on each value. If one of 'ns', 'us', 'ms', 's', 'm', 'h', 'd', 'w' the time units are instead expressed as nanoseconds, microseconds, milliseconds, seconds, hours, minutes, days or weeks respectively.
...

Additional arguments ignored.

Value

A tibble with the additional summary columns. The following summary columns are computed

  • expression - bench_expr The deparsed expression that was evaluated (or its name if one was provided).

  • min - bench_time The minimum execution time.

  • median - bench_time The sample median of execution time.

  • itr/sec - double The estimated number of executions performed per second.

  • mem_alloc - bench_bytes Total amount of memory allocated by R while running the expression. Memory allocated outside the R heap, e.g. by malloc() or new directly is not tracked, take care to avoid misinterpreting the results if running code that may do this.

  • gc/sec - double The number of garbage collections per second.

  • n_itr - integer Total number of iterations after filtering garbage collections (if filter_gc == TRUE).

  • n_gc - double Total number of garbage collections performed over all iterations. This is a psudo-measure of the pressure on the garbage collector, if it varies greatly between to alternatives generally the one with fewer collections will cause fewer allocation in real usage.

  • total_time - bench_time The total time to perform the benchmarks.

  • result - list A list column of the object(s) returned by the evaluated expression(s).

  • memory - list A list column with results from Rprofmem().

  • time - list A list column of bench_time vectors for each evaluated expression.

  • gc - list A list column with tibbles containing the level of garbage collection (0-2, columns) for each iteration (rows).

Details

If filter_gc == TRUE (the default) runs that contain a garbage collection will be removed before summarizing. This is most useful for fast expressions when the majority of runs do not contain a gc. Call summary(filter_gc = FALSE) if you would like to compute summaries with these times, such as expressions with lots of allocations when all or most runs contain a gc.

Examples

dat <- data.frame(x = runif(10000, 1, 1000), y=runif(10000, 1, 1000))

# `bench::mark()` implicitly calls summary() automatically
results <- bench::mark(
  dat[dat$x > 500, ],
  dat[which(dat$x > 500), ],
  subset(dat, x > 500))

# However you can also do so explicitly to filter gc differently.
summary(results, filter_gc = FALSE)
#> # A tibble: 3 x 13
#>   expression                  min median `itr/sec` mem_alloc `gc/sec` n_itr
#>   <bch:expr>                <bch> <bch:>     <dbl> <bch:byt>    <dbl> <int>
#> 1 dat[dat$x > 500, ]        394µs  405µs     2248.        NA     18.0  1123
#> 2 dat[which(dat$x > 500), ] 310µs  319µs     2834.        NA     16.0  1415
#> 3 subset(dat, x > 500)      522µs  535µs     1733.        NA     14.0   866
#> # … with 6 more variables: n_gc <dbl>, total_time <bch:tm>, result <list>,
#> #   memory <list>, time <list>, gc <list>

# Or output relative times
summary(results, relative = TRUE)
#> # A tibble: 3 x 13
#>   expression                  min median `itr/sec` mem_alloc `gc/sec` n_itr
#>   <bch:expr>                <dbl>  <dbl>     <dbl>     <dbl>    <dbl> <int>
#> 1 dat[dat$x > 500, ]         1.27   1.27      1.32        NA     1.31  1114
#> 2 dat[which(dat$x > 500), ]  1      1         1.67        NA     1.17  1407
#> 3 subset(dat, x > 500)       1.68   1.68      1           NA     1      859
#> # … with 6 more variables: n_gc <dbl>, total_time <bch:tm>, result <list>,
#> #   memory <list>, time <list>, gc <list>