profvis: Profile an R expression and visualize profiling data

In profvis: Interactive Visualizations for Profiling R Code

Profile an R expression and visualize profiling data

Description

This function will run an R expression with profiling, and then return an htmlwidget for interactively exploring the profiling data.

Usage

profvis(
  expr = NULL,
  interval = 0.01,
  prof_output = NULL,
  prof_input = NULL,
  timing = NULL,
  width = NULL,
  height = NULL,
  split = c("h", "v"),
  torture = 0,
  simplify = TRUE,
  rerun = FALSE
)

Arguments

expr	Expression to profile. The expression will be turned into the body of a zero-argument anonymous function which is then called repeatedly as needed. This means that if you create variables inside of expr they will not be available outside of it. The expression is repeatedly evaluated until Rprof() produces an output. It can be a quosure injected with rlang::inject() but it cannot contain injected quosures. Not compatible with prof_input.
interval	Interval for profiling samples, in seconds. Values less than 0.005 (5 ms) will probably not result in accurate timings
prof_output	Name of an Rprof output file or directory in which to save profiling data. If NULL (the default), a temporary file will be used and automatically removed when the function exits. For a directory, a random filename is used.
prof_input	The path to an Rprof() data file. Not compatible with expr or prof_output.
timing	The type of timing to use. Either "elapsed" (the default) for wall clock time, or "cpu" for CPU time. Wall clock time includes time spent waiting for other processes (e.g. waiting for a web page to download) so is generally more useful. If NULL, the default, will use elapsed time where possible, i.e. on Windows or on R 4.4.0 or greater.
width	Width of the htmlwidget.
height	Height of the htmlwidget
split	Orientation of the split bar: either "h" (the default) for horizontal or "v" for vertical.
torture	Triggers garbage collection after every torture memory allocation call. Note that memory allocation is only approximate due to the nature of the sampling profiler and garbage collection: when garbage collection triggers, memory allocations will be attributed to different lines of code. Using torture = steps helps prevent this, by making R trigger garbage collection after every torture memory allocation step.
simplify	Whether to simplify the profiles by removing intervening frames caused by lazy evaluation. Equivalent to the filter.callframes argument to Rprof().
rerun	If TRUE, Rprof() is run again with expr until a profile is actually produced. This is useful for the cases where expr returns too quickly, before R had time to sample a profile. Can also be a string containing a regexp to match profiles. In this case, profvis() reruns expr until the regexp matches the modal value of the profile stacks.

Details

An alternate way to use profvis is to separately capture the profiling data to a file using Rprof(), and then pass the path to the corresponding data file as the prof_input argument to profvis().

See Also

print.profvis() for printing options.

Rprof() for more information about how the profiling data is collected.

Examples

# Only run these examples in interactive R sessions
if (interactive()) {

# Profile some code
profvis({
  dat <- data.frame(
    x = rnorm(5e4),
    y = rnorm(5e4)
  )

  plot(x ~ y, data = dat)
  m <- lm(x ~ y, data = dat)
  abline(m, col = "red")
})


# Save a profile to an HTML file
p <- profvis({
  dat <- data.frame(
    x = rnorm(5e4),
    y = rnorm(5e4)
  )

  plot(x ~ y, data = dat)
  m <- lm(x ~ y, data = dat)
  abline(m, col = "red")
})
htmlwidgets::saveWidget(p, "profile.html")

# Can open in browser from R
browseURL("profile.html")

}

profvis documentation built on Sept. 20, 2024, 5:10 p.m.