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Related Concept Videos

Introduction to R01:11

Introduction to R

R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's functionality,...

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A User-friendly and Powerful R Analysis of Large-scale Datasets
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Published on: November 4, 2025

Reproducible Data Analysis With R in Laboratory Hematology.

Amrom E Obstfeld1,2

  • 1Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.

International Journal of Laboratory Hematology
|May 15, 2026
PubMed
Summary
This summary is machine-generated.

Spreadsheets hinder laboratory hematology data analysis reproducibility. Script-based analysis using R offers a transparent, auditable, and reproducible framework for complex hematology datasets, improving data integrity.

Keywords:
R programmingdata analysislaboratory hematologyquality assurancereproducibilityspreadsheets

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Area of Science:

  • Laboratory Medicine
  • Bioinformatics
  • Computational Biology

Background:

  • Traditional spreadsheet workflows in laboratory hematology are insufficient for large, complex datasets.
  • Spreadsheets lack transparency, auditability, and reproducibility due to manual data manipulation and obscured analytical logic.
  • These limitations pose significant challenges in regulated clinical laboratory environments.

Purpose of the Study:

  • To outline the limitations of spreadsheet-based analysis in laboratory hematology.
  • To introduce script-based data analysis as a reproducible alternative.
  • To highlight practical applications of script-based analysis in hematology.

Main Methods:

  • Review of structural limitations of spreadsheet-centered analysis.
  • Introduction of script-based analysis using the R programming language.
  • Discussion of complementary technologies: IDEs, version control, and LLMs.

Main Results:

  • Spreadsheets obscure analytical logic, leading to potential errors and limiting reproducibility.
  • Script-based analysis provides an explicit, ordered, and reusable framework for hematology workflows.
  • Applications demonstrated in method comparison, reference interval estimation, quality control, and flow cytometry.

Conclusions:

  • Script-based analysis, particularly with R, enhances data integrity and analytical rigor in laboratory hematology.
  • Adoption of reproducible tools is crucial for advancing analytical techniques and regulatory compliance.
  • This approach prepares laboratories for future data-intensive challenges and advanced analytics.