Reproducible science or research provide the ability to replicate research results from a published study. This idea ultimately also includes the full computational environment comprising code and data to replicate the analysis (Peng, 2011).
Over recent years our research group has settled on using the R statistical and computing environment together with R studio, R markdown and latex with some or our own scripts and packages to conduct reproducible science.
R provides a toolbox with its packages that allows analysis of most data conveniently without tedious reformatting on all major computing platforms including Microsoft Windows, Linux, and Apple’s OS X. R is an open source statistical programming and graphing language that includes tools for statistical, epidemiological, population genetic, genomic, phylogenetic, and comparative genomic analyses.
Note that the R user community is very active and that both R and its packages are regularly updated, critically modified, and noted as deprecated (no longer updated) as appropriate.
Any R user needs to make sure all components are up-to-date and that versions are compatible.
[1] R. D. Peng. “Reproducible Research in Computational Science”. In: Science 334.6060 (Dec. 2011), pp. 1226–1227. DOI: 10.1126/science.1213847. URL: http://dx.doi.org/10.1126/science.1213847.