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 
 #> 0.002 0.000 0.107
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: 0.109 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).