Packaging Data Analytical Work Reproducibly Using R (and Friends)

Marwick, Ben; Boettiger, Carl; Mullen, Lincoln

doi:10.1080/00031305.2017.1375986

Cited by 74 publications

(39 citation statements)

References 52 publications

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“…Our contribution to improving the efficiency of using and sharing code is the R package rrtools ('reproducible research tools', https://github.com/benmarwick/rrtools). This package is the result of our analysis of existing practices among archaeologists and other researchers, and our study of best practices and current conventions in scientific computing, described in more detail in Marwick, Boettiger, & Mullen (2018). The goal of rrtools is to make it easier for archaeologists and other researchers to use R for research and publication.…”

Section: Making It Easiermentioning

confidence: 99%

Tool-Driven Revolutions in Archaeological Science

Schmidt

Marwick

2020

Journal of Computer Applications in Archaeology

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IntroductionIn this paper we investigate recent developments which are facilitating computational archaeology's return to fundamental principles of the scientific method. We ask whether this might be the beginning of a broader change in the field. Revolutions or paradigm-shifts in archaeology have been proposed to occur following major theoretical statements (Clark 1993;Härke 2002), but there is an argument among philosophers of science whether revolutions in science are idea-driven or tool-driven (Dyson 2000;Galison 1997). This argument motivated us to explore how the disciplines of ecology and archaeology have started to embrace tools that improve the reproducibility of research. We focus on one set of tools for this change, based on writing data analysis scripts in free and open source programming languages, exemplified by R, and the practice of sharing of these scripts. We focus on R because our prior observations indicate it is by far the most common scripting language used by ecologists and archaeologists. Bibliometric evidence shows a strong increase in the use of R among ecologists and the beginning of a similar development in archaeology. We evaluate how approaches to improving reproducibility and transparency in archaeology are mediated and transformed by digital approaches and propose these might reflect a tool-driven change in archaeology. Acknowledging that this is not a simple process, we offer an R-based tool to ease the task of creating a compen-dium which enables other researchers to reproduce the published results.

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Section: Making It Easiermentioning

confidence: 99%

Tool-Driven Revolutions in Archaeological Science

Schmidt

Marwick

2020

Journal of Computer Applications in Archaeology

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“…Following recent concerns on the reproducibility of archaeological analysis we include the entire R code used for all the analysis and visualizations contained in this paper in our supplemental online material (SOM) at https://dx.doi.org/10.17605/OSF.IO/J39SU. To produce those files we followed the procedures described by Marwick et al (2017)…”

Section: Case Studiesmentioning

confidence: 99%

Use of Lithic Assemblages for the Definition of Short-Term Occupations in Hunter-Gatherer Prehistory

Cascalheira

Bicho

2020

Short-Term Occupations in Paleolithic Archaeology

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One of the main elements in prehistoric research is the study of settlement patterns. In the last five decades, stemming partially from Binford's research on the topic, the idea of settlement patterns is based on site typology, including the traditional residential and logistic concepts. Both models of land use and exploitation are certainly marked by the notion of short-term occupation. This concept, used freely by many archaeologists, tends to rely on two main ideas: an occupation lasted a short span of time and resulted in a limited amount of material culture. Our aim, based on our results from various archaeological case studies dated to the Upper Paleolithic of Portugal, is to show that neither idea is necessarily correct: e.g. there may be short-term occupations with the production of large amounts of artifacts, such as lithic workshops; there might be very small collections, such as lithic caches, resulting from short occupations but with very long uses of the site; and most times, both are hardly differentiated within complex palimpsests. Our study shows that the common use of lithic volumetric density and retouch frequency is not always sufficient to differentiate between short and long-term occupations. Also, there are other variables that are more sensitive to indicate the duration of occupation of an archaeological context that should be used in the identification of time length. The archaeological use of 'Short-term occupation' Prehistoric archaeology has been fighting hard to understand the archaeological record and how it relates to past human evolution and anthropogenic adaptations to the changing environment. The development of actualistic studies (sensu Binford 1981) in the last half century, including such specialized disciplines as Ethnoarchaeology, Geoarchaeology or Zooarchaeology, have greatly helped us to understand, not only the archaeological material culture, but also site formation processes (e.g. Schiffer 1983, 1987) in a wide and very diversified manner. Nevertheless, archaeologists still endure complex problems for the definition of universally used concepts related to past human adaptations. In some cases, however, archaeologists just freely use those concepts without the necessary careful or proper consideration of their meaning and the impact that they might have in further interpretations of the distant past. The concept of 'shortterm occupation' seems to be such a case. The idea of short-term occupation has been frequently used to succinctly describe site´s or lithic assemblage's characteristics (e.g. Porraz 2009; Rios-Garaizar 2016; Picin 2017). Most times, it is, in one way or another, related to work on settlement patterns, starting with the classic Binford' studies on the Nunamiut settlement system and related discussion of site structuring and intra-site spatial organization (e.g. Binford 1978a,

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“…The folder where all the files reside that you need for analysis (code and data), is REPRODUCIBLE DATA ANALYSIS WORKFLOW 6 referred to as a "project" (or sometimes as a "research compendium"). Working with projects is particularly convenient with RStudio, an integrated development environment (IDE) for R. It is useful to organize a data analysis project in a way that strictly segregates (raw) data and code by placing them in directories called data and R (see Section 4 in Marwick, Boettiger, & Mullen, 2018); there are also tools that automatize the standardized creation of folder structures such as workflowr (Blischak, Carbonetto, & Stephens, 2019).…”

Section: # Bettermentioning

confidence: 99%

“…Another way to increase the chance of reproducibility is to develop an R package, which is a self-contained set of files with well-defined meta-data, for each analysis. The reasoning is that, abiding by the strict rules of package development and specifying all dependencies can archieve a high degree of stability can be achieved (e.g., Marwick et al, 2018).…”

Section: Related Approachesmentioning

confidence: 99%

A Reproducible Data Analysis Workflow with R Markdown, Git, Make, and Docker

Peikert¹,

Brandmaier²

2019

Preprint

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In this tutorial, we describe a workflow to ensure long-term reproducibility of R-based data analyses. The workflow leverages established tools and practices from software engineering. It combines the benefits of various open-source software tools including R Markdown, Git, Make, and Docker, whose interplay ensures seamless integration of version management, dynamic report generation conforming to various journal styles and full cross-platform and long-term computational reproducibility. The workflow ensures meeting the primary goals that 1) the reporting of statistical results is consistent with the actual statistical results (dynamic report generation), the analysis exactly reproduces at a later time even if the computing platform or software is changed (computational reproducibility), and 3) changes at any time (during development and post-publication) are tracked, tagged, and documented while earlier versions of both data and code remain accessible. While the research community increasingly recognizes dynamic document generation and version management as tools to ensure reproducibility, we demonstrate with practical examples that these alone are not sufficient to ensure long-term computational reproducibility. Leveraging containerization, dependence management, version management, and literate programming, the workflow increases scientific productivity by facilitating later reproducibility and reuse of code and data.

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Packaging Data Analytical Work Reproducibly Using R (and Friends)

Cited by 74 publications

References 52 publications

Tool-Driven Revolutions in Archaeological Science

Tool-Driven Revolutions in Archaeological Science

Use of Lithic Assemblages for the Definition of Short-Term Occupations in Hunter-Gatherer Prehistory

A Reproducible Data Analysis Workflow with R Markdown, Git, Make, and Docker

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