knitr

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 65 articles )

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  1. da Silva, Natalia; Cook, Dianne; Lee, Eun-Kyung: A projection pursuit forest algorithm for supervised classification (2021)
  2. Gregor Zens, Sylvia Frühwirth-Schnatter, Helga Wagner: Efficient Bayesian Modeling of Binary and Categorical Data in R: The UPG Package (2021) arXiv
  3. Mickael Binois, Robert B. Gramacy: hetGP: Heteroskedastic Gaussian Process Modeling and Sequential Design in R (2021) not zbMATH
  4. Peder Bacher, Hjörleifur G. Bergsteinsson, Linde Frölke, Mikkel L. Sørensen, Julian Lemos-Vinasco, Jon Liisberg, Jan Kloppenborg Møller, Henrik Aalborg Nielsen, Henrik Madsen: onlineforecast: An R package for adaptive and recursive forecasting (2021) arXiv
  5. Pratheesh P. Gopinath, Rajender Parsad, Brigit Joseph, Adarsh V. S: grapesAgri1: Collection of Shiny Apps for Data Analysis in Agriculture (2021) not zbMATH
  6. Wollschläger, Daniel: R compact. The fast introduction into data analysis (2021)
  7. Arsalane Chouaib Guidoum, Kamal Boukhetala: Performing Parallel Monte Carlo and Moment Equations Methods for Ito and Stratonovich Stochastic Differential Systems: R Package Sim.DiffProc (2020) not zbMATH
  8. Chambaz, Antoine; Benkeser, David: A ride in targeted learning territory (2020)
  9. Haim Bar, HaiYing Wang: Reproducible Science with LaTeX (2020) arXiv
  10. Homrighausen, Darren; McDonald, Daniel J.: Compressed and penalized linear regression (2020)
  11. Irizarry, Rafael A.: Introduction to data science. Data analysis and prediction algorithms with R (2020)
  12. Laa, Ursula; Cook, Dianne: Using tours to visually investigate properties of new projection pursuit indexes with application to problems in physics (2020)
  13. Laa, Ursula; Cook, Dianne; Valencia, German: A slice tour for finding hollowness in high-dimensional data (2020)
  14. Lisa Amrhein, Christiane Fuchs: stochprofML: Stochastic Profiling Using Maximum Likelihood Estimation in R (2020) arXiv
  15. Sayani Gupta, Rob J Hyndman, Dianne Cook, Antony Unwin: Visualizing probability distributions across bivariate cyclic temporal granularities (2020) arXiv
  16. Wang, Earo; Cook, Dianne; Hyndman, Rob J.: A new tidy data structure to support exploration and modeling of temporal data (2020)
  17. Becker, Gabriel; Moore, Sara E.; Lawrence, Michael: trackr: a framework for enhancing discoverability and reproducibility of data visualizations and other artifacts in R (2019)
  18. Daniel Sabanés Bové, Wai Yin Yeung, Giuseppe Palermo, Thomas Jaki: Model-Based Dose Escalation Designs in R with crmPack (2019) not zbMATH
  19. da Silva, Natalia; Alvarez-Castro, Ignacio: Clicks and cliques: exploring the soul of the community (2019)
  20. David Miller and Eric Rexstad and Len Thomas and Laura Marshall and Jeffrey Laake: Distance Sampling in R (2019) not zbMATH

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