Excuse Me, Do You Have a Moment to Talk About Version Control?
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 s...
| Publicado en: | American Statistician Vol. 72; no. 1; pp. 20 - 28 |
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| Formato: | Artículo |
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Taylor & Francis Ltd
Feb2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=129302223&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 129302223 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00031305 STT jtl: American Statistician issn: 00031305 maglogo: Y pubinfo: dt: Feb2018 vid: 72 iid: 1 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 129302223 10.1080/00031305.2017.1399928 ppf: 20 ppct: 8 formats: tig: atl: Excuse Me, Do You Have a Moment to Talk About Version Control? aug: au: Bryan, Jennifer affil: RStudio and the Department of Statistics, University of British Columbia, Vancouver, Canada su: Data science Data analysis Statistical research Statistics education Reproducible research sug: subj: Data science Data analysis Statistical research Statistics education Reproducible research keyword: Git GitHub R language R Markdown Reproducibility Workflow Git GitHub R language R Markdown Reproducibility Workflow ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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