animation R package

What is knitr?

knitr is an R package designed as a transparent engine for dynamic report generation. It is inspired by Sweave but modularized to be more extensible and feature-rich. It allows you to combine code (from R, Python, or shell scripts) with markup languages (LaTeX, HTML, Markdown, AsciiDoc, and reStructuredText) to create reproducible documents.

Key Concepts

• Transparency: Users have full control over how input code and R output are wrapped in the final document (e.g., choosing between LaTeX verbatim environments or HTML <div> tags).
• Faithful Output: Using the evaluate package as a backend, knitr captures everything seen in an R terminal, including printed results, plots, warnings, messages, and errors.
• Consistency: It runs R code as if it were pasted into a terminal. For example, calling a plot function like qplot() will automatically produce the plot in the output without needing an explicit print() call.
• Extensibility: Through the use of hooks, users can customize how code chunks are processed or how output is formatted (e.g., coloring errors or styling source code).

The knitr showcase provides a curated collection of real-world applications, blog posts, and tutorials demonstrating how to use knitr for reproducible research, scientific reporting, and automated blogging.

Key areas covered in the showcase include:

• Reproducible Scientific Reports: Using knitr and pandoc to create reports that integrate data and code.
• Workflow Integration: Converting Sweave LaTeX to knitr R Markdown and using knitr with tools like RStudio, MiKTeX, and Word.
• Advanced Reporting: Creating interactive 3D rgl graphics in HTML reports and automating blog posts.
• Specialized Use Cases: Using knitr for biostatistics, bioinformatics, and economic modeling.
• Literate Programming: Implementing easier literate programming workflows with R.

Users can find specific guides for converting legacy formats (Sweave) to modern R Markdown and leveraging knitr's spin function to turn R code into reports.

The knitr showcase provides a curated list of external websites, applications, and resources that utilize knitr for dynamic document generation and reproducible research. This includes:

• Websites: Examples of web publishing using RPubs, tutorials like 'knitr in a knutshell', and dynamically built resources like UCLA's R learning resources.
• Book Reviews: Reviews of Dynamic Documents with R and knitr published in various academic journals and platforms.
• Solutions: Practical implementations such as blogging with R Markdown and Jekyll, creating HTML5 slides, building business dashboards, and using knitr with Rakefiles or Pandoc.
• R Packages: Documentation and examples for packages like cda, ggbio, sampSurf, and ggmcmc that leverage knitr.
• Courses: Academic lectures and online courses (e.g., Coursera, University of Wisconsin-Madison) that teach reproducible research and data analysis using knitr.
• Workshops and Presentations: Tutorials and slide decks covering workflows for data analysis, geospatial data, and publication-quality graphics.

The core logic of lt does not reside in R, but in a JavaScript runtime (lt.js). The R package acts as a thin front-end that produces a compact JSON object. This object contains:

• data: The raw underlying data.
• ops: A list of operations (e.g., merge, label, align, width, fmt_number, fmt_date, sub, indent, style) to be applied to the data.
• header: Title and subtitle information.
• spanners: Column spanner definitions.
• footnotes: Footnote text and their locations.

Because the rendering is driven by JSON, any language (like Python) can drive the same lt.js engine by emitting the correct JSON schema. You can manually build a table in JavaScript using LT.build(spec).

// Example of driving the lt.js runtime directly with a JSON spec
LT.build({
  "data": { "x": [1, 2], "y": [3.1415, 2.7182] },
  "ops": [
    { "type": "fmt_number", "columns": ["y"], "decimals": 2 }
  ]
});