knitr: A General-Purpose Package for Dynamic Report Generation in R. This package provides a general-purpose tool for dynamic report generation in R, which can be used to deal with any type of (plain text) files, including Sweave, HTML, Markdown, reStructuredText, AsciiDoc, and Textile. R code is evaluated as if it were copied and pasted in an R terminal thanks to the evaluate package (e.g., we do not need to explicitly print() plots from ggplot2 or lattice). R code can be reformatted by the formatR package so that long lines are automatically wrapped, with indent and spaces added, and comments preserved. A simple caching mechanism is provided to cache results from computations for the first time and the computations will be skipped the next time. Almost all common graphics devices, including those in base R and add-on packages like Cairo, cairoDevice and tikzDevice, are built-in with this package and it is straightforward to switch between devices without writing any special functions. The width and height as well as alignment of plots in the output document can be specified in chunk options (the size of plots for graphics devices is also supported). Multiple plots can be recorded in a single code chunk, and it is also allowed to rearrange plots to the end of a chunk or just keep the last plot. Warnings, messages and errors are written in the output document by default (can be turned off). The large collection of hooks in this package makes it possible for the user to control almost everything in the R code input and output. Hooks can be used either to format the output or to run R code fragments before or after a code chunk. The language in code chunks is not restricted to R (there is simple support to Python and shell scripts, etc). Many features are borrowed from or inspired by Sweave, cacheSweave, pgfSweave, brew and decumar.

References in zbMATH (referenced in 47 articles )

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  1. Chambaz, Antoine; Benkeser, David: A ride in targeted learning territory (2020)
  2. Irizarry, Rafael A.: Introduction to data science. Data analysis and prediction algorithms with R (2020)
  3. Lisa Amrhein, Christiane Fuchs: stochprofML: Stochastic Profiling Using Maximum Likelihood Estimation in R (2020) arXiv
  4. Daniel Sabanés Bové, Wai Yin Yeung, Giuseppe Palermo, Thomas Jaki: Model-Based Dose Escalation Designs in R with crmPack (2019) not zbMATH
  5. da Silva, Natalia; Alvarez-Castro, Ignacio: Clicks and cliques: exploring the soul of the community (2019)
  6. David Miller and Eric Rexstad and Len Thomas and Laura Marshall and Jeffrey Laake: Distance Sampling in R (2019) not zbMATH
  7. Hofmann, Heike (ed.); Wickham, Hadley (ed.); Cook, Dianne (ed.): The 2013 Data Expo of the American Statistical Association (2019)
  8. Mateusz Staniak, Przemyslaw Biecek: The Landscape of R Packages for Automated Exploratory Data Analysis (2019) arXiv
  9. Maurer, Karsten; Osthus, Dave; Loy, Adam: A tale of four cities: exploring the soul of State College, Detroit, Milledgeville and Biloxi (2019)
  10. McNamara, Amelia A.: Community engagement and subgroup meta-knowledge: some factors in the soul of a community (2019)
  11. Polzehl, Jörg; Tabelow, Karsten: Magnetic resonance brain imaging. Modeling and data analysis using R (2019)
  12. Shana Scogin; Johannes Karreth; Andreas Beger; Rob Williams: BayesPostEst: An R Package to Generate Postestimation Quantities for Bayesian MCMC Estimation (2019) not zbMATH
  13. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018) not zbMATH
  14. Bilgrau, Anders Ellern; Brøndum, Rasmus Froberg; Eriksen, Poul Svante; Dybkær, Karen; Bøgsted, Martin: Estimating a common covariance matrix for network meta-analysis of gene expression datasets in diffuse large B-cell lymphoma (2018)
  15. Carmichael, Iain; Marron, J. S.: Data science vs. statistics: two cultures? (2018)
  16. Marvá, M.; San Segundo, F.: Age-structure density-dependent fertility and individuals dispersal in a population model (2018)
  17. Natalia da Silva, Eun-Kyung Lee, Di Cook: A Projection Pursuit Forest Algorithm for Supervised Classification (2018) arXiv
  18. Pedro M. Valero Mora: bookdown: Authoring Books and Technical Documents with R Markdown (2018) not zbMATH
  19. William Michael Landau: The drake R package: a pipeline toolkit for reproducibility and high-performance computing (2018) not zbMATH
  20. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)

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