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Tools to facilitate logging in a clinical environment with the goal of making code easily traceable and reproducible.

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logrx

R build status CRAN status Lifecycle: experimental

The goal of {logrx} is to facilitate logging in a clinical environment with the goal of making code easily traceable and reproducible.

Installation

This package is available from CRAN and can be installed by running:

install.packages("logrx")

To get the latest development version use:

devtools::install_github("pharmaverse/logrx", ref = "dev")

What is {logrx} ?

Simple! The {logrx} package makes a log file for a R script!. Below we have a short gif of making a log file for a adsl.R script using the Addin. More details on the parts of the log script are found in Get Started and we have several vignettes that show specific use cases for using the package.

logrx Addin in Action

While many packages to facilitate the logging of code already exist in the R ecosystem, it is hard to find a solution that works well for clinical programming applications. Many logging implementations are more implicit and rely on user input to create the log for the execution of a script. While this is useful for logging specific events of an application, in clinical programming a log has a set purpose.

{logrx} is built around the concept of creating a log for the execution of an R script that provides an overview of what happened as well as the environment that it happened in. We set out to create a flexible logging utility that could provide the necessary information to anyone reviewing the code execution so they can recreate the execution environment and run the code for themselves. {logrx} is purpose-built for the needs of clinical programming to capture necessary elements of the environment and execution to be able to create a complete view of the program execution.

Using logrx

logrx can be used in a file or through scripting. More information on how to use {logrx} can be found in Get Started and in our Articles on Execution and Use Cases.

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Tools to facilitate logging in a clinical environment with the goal of making code easily traceable and reproducible.

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