Description Usage Arguments Value See Also Examples
Benchmark a list of quoted expressions. Each expression will always run at least twice, once to measure the memory allocation and store results and one or more times to measure timing.
1 2 3 4 
... 
Expressions to benchmark, if named the 
min_time 
The minimum number of seconds to run each expression, set to

iterations 
If not 
min_iterations 
Each expression will be evaluated a minimum of 
max_iterations 
Each expression will be evaluated a maximum of 
check 
Check if results are consistent. If 
filter_gc 
If 
relative 
If 
time_unit 
If 
exprs 
A list of quoted expressions. If supplied overrides expressions
defined in 
env 
The environment which to evaluate the expressions 
A tibble with the additional summary columns. The following summary columns are computed
min
 bench_time
The minimum execution time.
mean
 bench_time
The arithmetic mean of execution time
median
 bench_time
The sample median of execution time.
max
 bench_time
The maximum execution time.
mem_alloc
 bench_bytes
Total amount of memory allocated by running the expression.
itr/sec
 integer
The estimated number of executions performed per second.
n_itr
 integer
Total number of iterations after filtering
garbage collections (if filter_gc == TRUE
).
n_gc
 integer
Total number of garbage collections performed over all
iterations. This is a psudomeasure 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.
press()
to run benchmarks across a grid of parameters.
1 2 3 4 5 6 7  dat < data.frame(x = runif(100, 1, 1000), y=runif(10, 1, 1000))
mark(
min_time = .1,
dat[dat$x > 500, ],
dat[which(dat$x > 500), ],
subset(dat, x > 500))

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