Pkgndep: a tool for analyzing dependency heaviness of R packages

Author:

Gu Zuguang1ORCID,Hübschmann Daniel1234

Affiliation:

1. Molecular Precision Oncology Program, National Center for Tumor Diseases (NCT)

2. Heidelberg Institute of Stem Cell Technology and Experimental Medicine (HI-STEM)

3. German Cancer Consortium (DKTK)

4. Department of Pediatric Immunology, Hematology and Oncology, University Hospital Heidelberg , 69120 Heidelberg, Germany

Abstract

Abstract Summary Numerous R packages have been developed for bioinformatics analysis in the last decade and dependencies among packages have become critical issues to consider. In this work, we proposed a new metric named dependency heaviness that measures the number of dependencies that a parent uniquely brings to a package and we proposed possible solutions for reducing the complexity of dependencies by optimizing the use of heavy parents. We implemented the metric in a new R package pkgndep which provides an intuitive way for dependency heaviness analysis. Based on pkgndep, we additionally performed a global analysis of dependency heaviness on CRAN and Bioconductor ecosystems and we revealed top packages that have significant contributions of high dependency heaviness to their child packages. Availability and implementation The package pkgndep and documentations are freely available from the Comprehensive R Archive Network https://cran.r-project.org/package=pkgndep. The dependency heaviness analysis for all 22 076 CRAN and Bioconductor packages retrieved on June 8, 2022 are available at https://pkgndep.github.io/. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

National Center for Tumor Diseases

Molecular Precision Oncology Program

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference1 articles.

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