Automation and Standardization of Cleaning Clinical Laboratory Data R package lab2clean version 2.0.0 Navigating the shift of clinical laboratory data from primary everyday clinical use to secondary research purposes presents a significant challenge. Given the substantial time and expertise required for lab data pre-processing and cleaning and the lack of all-in-one tools tailored for this need, we developed our algorithm 'lab2clean' as an open-source R-package. 'lab2clean' package is set to automate and standardize the intricate process of cleaning clinical laboratory results. With a keen focus on improving the data quality of laboratory result values and units, our goal is to equip researchers with a straightforward, plug-and-play tool, making it smoother for them to unlock the true potential of clinical laboratory data in clinical research and clinical machine learning (ML) model development. Functions to clean & validate result values (Version 1.0) are described in detail in 'Zayed et al. (2024)' <doi:10.1186/s12911-024-02652-7>. Functions to standardize & harmonize result units (added in Version 2.0) are described in detail in 'Zayed et al. (2025)' <doi:10.1016/j.ijmedinf.2025.106131>. Software Depends: R (>= 3.5) Imports: data.table, stats, utils Suggests: knitr, rmarkdown, fansi, kableExtra, printr Ahmed Zayed <ahmed.zayed@kuleuven.be> Comprehensive R Archive Network (CRAN) Ahmed Zayed [aut, cre] (ORCID: <https://orcid.org/0000-0001-7797-1655>), Ilias Sarikakis [aut, ctb], Arne Janssens [aut, ctb], Pavlos Mamouris [ctb] GPL (>= 3) 2025-10-04 application/tgz https://CRAN.R-project.org/package=lab2clean doi:10.32614/CRAN.package.lab2clean