Dynamic Documents with R and knitr

By Yihui Xie. 2015

745 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/95a2b1b7093cf4d5910ad322bb5d1d4f7c5611a2

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In Artificial Intelligence

Citations in the corpus

Equivalence Testing for Psychological Research: A Tutorial (2018, neutral): “…R packages bookdown (Version 0.5; Xie, 2016), ggplot2 (Version 2.2.1; Wickham, 2009), gridExtra (Version 2.3; Auguie, 2017), knitr (Version 1.17; Xie, 2015), lattice (Version 0.20.35; Sarkar, 2008), papaja (Version 0.1.0.9492; Aust & Barth, 2017), purrr (Version 0.2.4; Henry & Wickham, 2017),…”
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, positive): “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.”
50 Years of Data Science (2017, methodology): “…2017a), and rmarkdown are iterations of previous packages and software ( reshape and reshape2 by Wickham 2007, plyr by Wickham 2011, knitr by Xie 2015, 2017, and Sweave by Leisch 2002), and all were developed to solve practical problems that became obvious through teaching or data analysis.”
A parametric texture model based on deep convolutional features closely matches texture appearance for humans (2017, methodology): “This document was prepared using the knitr package (Xie, 2013, 2015) in the R statistical environment (Arnold, 2016; Auguie, 2016; {R Core Development Team}, 2016; Wickham, 2009, 2011; Wickham & Francois, 2016) to improve its reproducibility.”
rotl: an R package to interact with the Open Tree of Life data (2016, background): “These packages, combined with the language’s support for literate programming (Knuth 1984; Xie 2015), make R a comprehensive platform for reproducible research in phylogenetics and comparative biology, as they allow a complete record of the steps taken in gathering, processing and analysing a given…”
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.”
limma powers differential expression analyses for RNA-sequencing and microarray studies (2015, methodology): “BeingR-based, reports of limma analyses can be compiled using Sweave (77) or knitr (78) and provided along with the raw data in a compendium to promote reproducible research in genomics (79).”
Predictability of monthly temperature and precipitation using automatic time series forecasting methods (2018, neutral): “…Chang 2017), ‘forecast’ (Hyndman et al. 2017), ‘fracdiff’ (Fraley et al. 2012), ‘gdata’ (Warnes et al. 2017), ‘ggplot2’ (Wickham 2016), ‘HKprocess’ (Tyralis 2016), ‘knitr’ (Xie 2014, 2015, 2017), ‘maps’ (Brownrigg et al. 2017), ‘prophet’ (Taylor and Letham 2017b) and ‘readr’ (Wickham et al. 2017).”
How to explain and predict the shape parameter of the generalized extreme value distribution of streamflow extremes using a big dataset (2018, neutral): “2018), gdata (Warnes et al. 2017), ggplot2 (Wickham 2016; Wickham et al. 2018), knitr (Xie 2014, 2015, 2018), olsrr (Hebbali 2018), randomForest (Liaw and Wiener 2002; Breiman et al. 2018), readr (Wickham et al. 2017), relaimpo (Grömping 2007b, 2018), reshape2 (Wickham 2007, 2017), rmarkdown…”
msa: an R package for multiple sequence alignment (2015, neutral): “Let us first load amino acid sequences from one of the example files that are supplied with the msa package: mySequenceFile <- system.file("examples", "exampleAA.fasta", package="msa") mySequences <- readAAStringSet(mySequenceFile) mySequences ## AAStringSet object of length 9: ## width seq names ##”
False discovery rates: a new deal (2016, positive): “Development of the methods in this paper was greatly enhanced by the use of the knitr package (Xie, 2013) within the RStudio GUI, and git and github.”