The American Statistician · 2017 · 59 citations · 4 references
ComputingComputer ScienceData ScienceDistributed SystemsSoftware EngineeringStatistical ComputingDocument ManagementResearch Project ManagementSoftware Project ManagementStatistical Software DevelopmentVersion ControlVersion Control SystemsSource Code EvolutionVersion Control ProcessesVersioningVersioning Workflows
Data analysis, statistics, and teaching generate many evolving files that, without structured management, can lead to chaotic organization and reduced product quality. The article proposes using distributed version control, specifically Git and GitHub, to prevent this chaos. It explains how to apply Git and GitHub to statistical and data‑scientific projects, especially those using R and R Markdown, and provides tutorials and real‑world examples. Supplementary materials for the article are available online.
Data analysis, statistical research, and teaching statistics have at least one thing in common: these activities all produce many files! There are data files, source code, figures, tables, prepared reports, and much more. Most of these files evolve over the course of a project and often need to be shared with others, for reading or edits, as a project unfolds. Without explicit and structured management, project organization can easily descend into chaos, taking time away from the primary work and reducing the quality of the final product. This unhappy result can be avoided by repurposing tools and workflows from the software development world, namely, distributed version control. This article describes the use of the version control system Git and the hosting site GitHub for statistical and data scientific workflows. Special attention is given to projects that use the statistical language R and, optionally, R Markdown documents. Supplementary materials include an annotated set of links to step-by-step tutorials, real world examples, and other useful learning resources. Supplementary materials for this article are available online.
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R: A Language and Environment for Statistical Computing
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Natural Sciences, Computer Science, Statistical Computing +10
THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS
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