Why pipetime?

library(pipetime)
library(dplyr)

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 
#>   0.003   0.000   0.104
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)
#> 
#> Attaching package: 'tictoc'
#> The following object is masked from 'package:data.table':
#> 
#>     shift

# 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: 0.103 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")
#>     mean_y
#> 1 4.666667

Why pipetime?

  • Works directly inside pipelines.

  • Supports multiple checkpoints.

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