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Using R Markdown – RStudio Support. R Markdown enables easy authoring of reproducible web reports from R.

Using R Markdown – RStudio Support

It offers: Easy creation of web reports from R that can be automatically regenerated whenever underlying code or data changes. A highly accessible syntax (markdown) which lower the barriers to entry for reproducible research. Output of a standalone HTML file (with images embedded directly in the file) that is easy to share using email, Dropbox, or by deploying to a web server. Embedding Markdown in Jekyll HTML - Stack Overflow. Document Templates. Overview If there is a particular form of R Markdown document that you or those you work with create frequently, it may make sense to create a re-usable document template for it.

Document Templates

R Markdown templates are typically re-distributed within R packages, and can be easily discovered from within RStudio using the New R Markdown dialog: Note that if you are not using RStudio you can also create new documents based on templates using the rmarkdown::draft function: rmarkdown::draft("my_article.Rmd", template = "jss_article", package = "rticles") Template Basics.

Jimhester/knitrBootstrap · GitHub. Pandoc - Pandoc User’s Guide. Pandoc [options] [input-file]… Pandoc is a Haskell library for converting from one markup format to another, and a command-line tool that uses this library.

Pandoc - Pandoc User’s Guide

It can read markdown and (subsets of) Textile, reStructuredText, HTML, LaTeX, MediaWiki markup, Haddock markup, OPML, Emacs Org-mode and DocBook; and it can write plain text, markdown, reStructuredText, XHTML, HTML 5, LaTeX (including beamer slide shows), ConTeXt, RTF, OPML, DocBook, OpenDocument, ODT, Word docx, GNU Texinfo, MediaWiki markup, EPUB (v2 or v3), FictionBook2, Textile, groff man pages, Emacs Org-Mode, AsciiDoc, InDesign ICML, and Slidy, Slideous, DZSlides, reveal.js or S5 HTML slide shows. It can also produce PDF output on systems where LaTeX is installed. Using pandoc. Pander: A Pandoc writer in R. Pander is an R package containing helpers to return Pandoc's markdown even automatically from several type of R objects with a general S3 method.

pander: A Pandoc writer in R

The package is also capable of exporting/converting complex Pandoc documents (reports) in three ways at the moment: create somehow a markdown text file (e.g. with brew, knitr or any scripts of yours, maybe with Pandoc.brew - see just below) and transform that to other formats (like HTML, odt, pdf, docx etc.) with Pandoc.convert -- just like pandoc function in knitr,users might write some reports in a forked version of brew syntax resulting in a pretty Pandoc document (where each R object are automatically transformed to nicely formatted table, list etc.) and also in a bunch of other formats (like HTML, odt, pdf, docx etc.) ,Example: this is cooked with Pandoc.brew based on inst/README.brew and also exported to HTML.

How does pander differ from Sweave, brew, knitr, R2HTML etc.? Rmarkdown: Alter Action Depending on Document. Can I see a show of hands for those who love rmarkdown?

rmarkdown: Alter Action Depending on Document

Yeah me too. One nifty feature is the ability to specify various document prettifications in the YAML of a .Rmd document and then use: The Problem Have you ever said, “I wish I could do X for document type A and Y for document type B”? Lot of reports with a single click! Suppose you want to create a huge number of pdf files through RMarkdown and pandoc, each of them including a statistical analysis on a part of your data, for example on each row of your data frame.

Lot of reports with a single click!

You need to write a .R file with cycles from 1 to the number of rows of your data set the instructions contained in a .Rmd file. Suppose that: your data set name is data.csv and its path is datapath/data.csv’’your .Rmd file name is report.Rmd and its path is basepath/report.Rmd’’ One weird trick to compile multipartite dynamic... < biochemistries. This afternoon I stumbled across this one weird trick an undocumented part of the YAML headers that get processed when you click the ‘knit’ button in RStudio.

One weird trick to compile multipartite dynamic... < biochemistries

Knitting turns an Rmarkdown document into a specified format, using the rmarkdown package’s render function to call pandoc (a universal document converter written in Haskell). If you specify a knit: field in an Rmarkdown YAML header you can replace the default function (rmarkdown::render) that the input file and encoding are passed to with any arbitrarily complex function. For example, the developer of slidify passed in a totally different function rather than render - slidify::knit2slides. I thought it’d be worthwhile to modify what was triggered upon clicking that button - as simply as using a specified output file name (see StackOverflow here), or essentially running a sort of make to compose a multi-partite document. Makefiles and RMarkdown. Quite some time ago (October 2013, according to Amazon), I bought a copy of “Reproducible Research with R and RStudio” by Christopher Gandrud.

Makefiles and RMarkdown

And it was awesome. Since then, I’ve been using knitr and RMarkdown quite a lot. However, until recently, I never bothered with a makefile. Make prettier documents by reusing chunks in RMarkdown. No revelations here, just a little R tip for generating more readable documents.

Make prettier documents by reusing chunks in RMarkdown

Original with lots of code at the top There are times when I want to show code in a document, but I don’t want it to be the first thing that people see. What I want to see first is the output from that code. Escape the Land of LaTeX/Word for Statistical Reporting: The Ecosystem of R Markdown Webinar. Embedding RData files in Rmarkdown files for more reproducible analyses. For those of us interested in reproducible analysis, Rmarkdown is a great way of communicating our code to other researchers.

Embedding RData files in Rmarkdown files for more reproducible analyses

Rstudio, in particular, makes it very easy to create attractive HTML document containing text, code, and figures, which can then be sent to colleagues or put on the internet for anyone to see. If you aren't using Rmarkdown for your statistical analyses, I recommend you start; you'll never go back to simple script files again (and your colleagues won't want you to). In this post, I describe how to improve your Rmarkdown by embedding data that can be downloaded by anyone viewing the document in a modern browser with javascript enabled.

For a quick look, see the example Rmd file and resulting HTML file. “Mail merge” with RMarkdown. The term “mail merge” might not be familiar to those who have not worked in an office setting, but here is the Wikipedia definition: Mail merge is a software operation describing the production of multiple (and potentially large numbers of) documents from a single template form and a structured data source. The letter may be sent out to many “recipients” with small changes, such as a change of address or a change in the greeting line.Source: Interactive documents: An incredibly easy way to use Shiny. R Markdown’s new interactive documents provide a quick, light-weight way to use Shiny. An interactive document embeds Shiny elements in an R Markdown report. The report becomes “live”, a choose your own adventure that readers can control and explore.

Interactive documents are easy to create and easy to share. Create an interactive document To create an interactive document use RStudio to create a new R Markdown file, choose the Shiny document template, then click “Run Document” to show a preview: How to set up your own R blog with Github pages and Jekyll Bootstrap. This post is in reply to a request: How did I set up this R blog? I have wanted to have my own R blog for a while before I actually went ahead and realised this page. I had seen all the cool things people do with R by following R-bloggers and reading their newsletter every day!

Jupyter And R Markdown: Notebooks With R. When working on data science problems, you might want to set up an interactive environment to work and share your code for a project with others. You can easily set this up with a notebook. In other cases, you’ll just want to communicate about the workflow and the results that you have gathered for the analysis of your data science problem. For a transparent and reproducible report, a notebook can also come in handy. R Markdown: How to number and reference tables. R Markdown is a great tool to make research results reproducible. However, in scientific research papers or reports, tables and figures usually need to be numbered and referenced.

Unfortunately, R Markdown has no “native” method to number and reference table and figure captions. The recently published bookdown package makes it very easy to number and reference tables and figures (Link). However, since bookdown uses LaTex functionality, R Markdown files created with bookdown cannot be converted into MS Word (.docx) files. Dynamic Documents with RMarkdown and Knitr. Getting started with R Markdown. Rmarkdown/pdf_document.R at master · rstudio/rmarkdown · GitHub. Fast-track publishing using the new R markdown – a tutorial and a quick look behind the scenes. Floating table of contents for your html reports using knitr.

R Markdown v2. Ly — Plot with ggplot2 and plotly within knitr reports. Interactive Documents. Shiny - Gallery. Announcing RPubs: A New Web Publishing Service for R.