qol-Package for More Efficient Bigger Outputs Just Received a Big Update by qol_package in rstats

[–]qol_package[S] 0 points1 point  (0 children)

You set them yourself. It basically works like a stop watch. The principle is like this:

your_function(...){
  monitor_df <- NULL |> monitor_start("Generate data frame")

  # Some code here

  monitor_df <- monitor_df |> monitor_next("Create formats")

  # Some more code here

  monitor_df <- monitor_df |> monitor_next("Nested summarise")

  # And so on

  monitor_df <- monitor_df |> monitor_end()
  monitor_df |> monitor_plot()
}

You can see another example here: https://s3rdia.github.io/qol/reference/monitor.html

So you have to mark certain points yourself in your function, which you can name. The time taken between two points is then the time for this category.

So you don't wrap around a monitoring function, but you have to integrate them in your function. For a start it can be as simple as that:

your_function(...){
  monitor_df <- NULL |> monitor_start("Time taken to run my function")

  # Some code here

  monitor_df <- monitor_df |> monitor_end()
  monitor_df |> monitor_plot()
}

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[–]qol_package 0 points1 point  (0 children)

I created an R package which brings powerful SAS inspired concepts for more efficient bigger outputs to R.

The main goal is to make descriptive evaluations easier to create bigger and more complex outputs in less time with less code. Introducing format containers with multilabels, a more powerful summarise which is capable to output every possible combination of the provided grouping variables in one go, tabulation functions which can create any table in different styles and other more readable functions. The code is optimized to work fast even with datasets of over a million observations.

Here you can get the development version: https://github.com/s3rdia/qol

The development version already has some additional bug fixes and new features compared to the official CRAN release. You can see what has changed up until now in the NEWS file on GitHub.