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Why pipetime?

Source: vignettes/why_pipetime.Rmd
why_pipetime.Rmd

R pipelines (|>) allow chaining operations in a readable, sequential way. Existing timing tools (e.g. system.time(), tictoc) do not integrate naturally with pipelines and tidy workflows. pipetime solves this by letting you measure time inline, without interrupting the pipeline.

Examples 

slow_op <- function(x) {
  Sys.sleep(0.1)  # Simulate a time-consuming operation
  x^2
}

system.time() 

# Must wrap the entire pipeline, breaking the flow
the_time <- system.time({
  df <- data.frame(x = 1:3) |>
    mutate(y = slow_op(x)) |>
    summarise(mean_y = mean(y))
})
the_time
#>    user  system elapsed 
#>   0.009   0.000   0.110
df
#>     mean_y
#> 1 4.666667

# system.time() cannot be inserted inline in a pipeline:
data.frame(x = 1:3) |>
  mutate(y = slow_op(x)) |>
  # system.time() would break the pipeline here
  summarise(mean_y = mean(y))
#>     mean_y
#> 1 4.666667

tictoc 

library(tictoc)

# Requires manual start/stop
tic("total pipeline")
df <- data.frame(x = 1:3) |>
  mutate(y = slow_op(x)) |>
  summarise(mean_y = mean(y))
toc()
#> total pipeline: 0.104 sec elapsed
df
#>     mean_y
#> 1 4.666667

time_pipe 

# Inline timing checkpoints, pipeline stays intact
data.frame(x = 1:3) |>
  mutate(y = slow_op(x)) |>
  time_pipe("after mutate") |>
  summarise(mean_y = mean(y)) |>
  time_pipe("total pipeline")
#> [2026-03-18 14:34:46.331] after mutate: +0.1018 secs
#> [2026-03-18 14:34:46.331] total pipeline: +0.1082 secs
#>     mean_y
#> 1 4.666667

Why pipetime?

  • Works directly inside pipelines.

  • Supports multiple checkpoints.

  • Prints or logs timings in .pipetime_env (see ?get_log).