Sweave: Dynamic Generation of Statistical Reports Using Literate Data Analysis

By Friedrich Leisch. International Conference on Computational Statistics, 2002

521 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/5cfac1dd6110c94dfe009fcd787b57c4655b4474

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

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Citations in the corpus

Generalized Linear Models With Examples in R (2018, methodology): “The complete quote from Box and Draper clariﬁes [2, p. 424], “. . . Essentially, all models are wrong, but some are useful.”
Ten Simple Rules for Reproducible Computational Research (2013, background): “rectly into textual documents, such as Sweave [19], the GenePattern Word”
An introduction to Docker for reproducible research (2014, background): “dynamic documents [17, 22, 26] which embed the code required to re-generate the results within the manuscript.”
Advancing our understanding of the human microbiome using QIIME (2013, unknown): “lends itself well to reproducible research and is enhanced substantially by R’s integration with tools for literate programming such as Sweave (Leisch, 2002), knitr (Xie, 2013), and R markdown (Allaire, Horner, Marti, & Porte, 2013), as well as data graphics.”
Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences (2010, methodology): “Leisch F: Sweave: dynamic generation of statistical reports using literate data analysis.”
Bioconductor: open software development for computational biology and bioinformatics (2004, methodology): “The Sweave system [26] was adopted for creating and processing vignettes.”
Dynamic Documents with R and knitr (2015, background): “Sweave (Leisch, 2002) was among the first implementations for dealing with dynamic documents in R (Ihaka and Gentleman, 1996; R Core Team, 2013).”
archivist: An R Package for Managing, Recording and Restoring Data Analysis Results (2017, methodology): “The R ecosystem of packages is equipped with wonderful tools such as knitr (see Xie 2013, 2015) or Sweave (see Leisch 2002; Rossini and Leisch 2003) which allow to create reproducible reports or articles.”
Deriving chemosensitivity from cell lines: Forensic bioinformatics and reproducible research in high-throughput biology (2009, methodology): “We now require most of our reports to be written using Sweave [Leisch (2002)], a literate programming combination of I^TjhX source and R [R Develop ment Core Team (2008)] code (SASweave and odfWeave are also available) so that we can rerun the reports as needed and get the same results.”
Statistical Analyses and Reproducible Research (2007, unknown): “Sweave (Leisch, 2002) provides an environment within the R system and more traditional tools (e.”
MetaboAnalystR: an R package for flexible and reproducible analysis of metabolomics data (2018, methodology): “Both the R package and the web server generate analysis reports for each module using Sweave (Leisch, 2002).”
Dynamic, Interactive Documents for Teaching Statistical Practice (2007, unknown): “The combination of Sxslt and these XSL stylesheets provide equivalent facilities as Sweave (Leisch, 2002) but is more flexible as it uses an XML format that can accommodate new markup with more than two types of content (text and code) and which can support LATEX and other text formatting systems.”
Analysis of cell-based RNAi screens (2006, methodology): “It uses the vignette and packaging technology available from the R and Bioconductor projects [31,34,35].”