Biostatistics Experts Develop R Package for Automated Flowchart Generation to Enhance Scientific Research Reproducibility

The Germans Trias i Pujol Research Institute has launched an R package to generate automated, publication-ready flowcharts for clinical and scientific studies.

By: AXL Media

Published: Apr 30, 2026, 6:10 AM EDT

Source: Information for this report was sourced from EurekAlert!

Automating the Documentation of Research Pathways

A specialized team at the Germans Trias i Pujol Research Institute has introduced a significant advancement in clinical and epidemiological reporting through the development of a new R package. This software tool, appropriately named flowchart, is designed to simplify the visualization of participant pathways in complex scientific studies. Traditionally, creating these diagrams has been a manual and error prone task, often requiring researchers to transcribe data points into graphic design software. By automating this process, the new package ensures that the visual representation of a study matches the underlying data with absolute precision.

Adhering to Global Standards for Scientific Reporting

The creation of participant flow diagrams is a critical requirement for adhering to international research guidelines such as CONSORT and STROBE. These standards demand clear visualization of every stage of a research process, from the initial selection of candidates to the final analysis of data. According to the development team, the flowchart package addresses the challenges of labor intensive manual entry by allowing diagrams to be produced directly from the research dataset. This ensures that the documentation of participant attrition or cohort splitting is transparent and verifiable by peer reviewers.

Integration with the Tidyverse Ecosystem

From a technical standpoint, the package is built to function seamlessly within the tidyverse framework, which is a widely used collection of R programming tools. This integration allows users to utilize the pipe operator to combine functions for filtering data, initializing diagrams, and splitting cohorts in a few simple steps. The architecture of the package enables researchers to produce clear diagrams within a fully reproducible workflow, meaning that as the dataset changes, the diagram can be updated automatically without the need for manual redesign or reformatting.