You are currently browsing the category archive for the ‘R Markdown’ category.

We have released the R package bookdown (v0.3) to CRAN. It may be old news to some users, but we are happy to make an official announcement today. To install the package from CRAN, you can

install.packages("bookdown")

The bookdown package provides an easier way to write books and technical publications than traditional tools such as LaTeX and Word. It inherits the simplicity of syntax and flexibility for data analysis from R Markdown, and extends R Markdown for technical writing, so that you can make better use of document elements such as figures, tables, equations, theorems, citations, and references, etc. Similar to LaTeX, you can number and cross-reference these elements with bookdown. Read the rest of this entry »

rstudio::conf 2017, the conference on all things R and RStudio, is only 90 days away. Now is the time to claim your spot or grab one of the few remaining seats at Training Days – including the new Tidyverse workshop.

REGISTER NOW

Whether you’re already registered or still working on it, we’re delighted today to announce the full conference schedule, so that you can plan your days in Florida.

rstudio::conf 2017 takes place January 12-14 at the Gaylord Resorts in Kissimmee, Florida. There are over 30 talks and tutorials to choose from that are sure to accelerate your productivity in R and RStudio. In addition to the highlights below, topics include the latest news on R notebooks, sparklyr, profiling, the tidyverse, shiny, r markdown, html widgets, data access and the new enterprise-scale publishing capabilities of RStudio Connect.

Schedule Highlights

Keynotes
– Hadley Wickham, Chief Scientist, RStudio: Data Science in the Tidyverse
– Andrew Flowers, Economics Writer, FiveThirtyEight: Finding and Telling Stories with R
– J.J. Allaire, Software Engineer, CEO & Founder: RStudio Past, Present and Future

Tutorials
– Winston Chang, Software Engineer, RStudio: Building Dashboards with Shiny
– Charlotte Wickham, Oregon State University: Happy R Users Purrr
– Yihui Xie, Software Engineer, RStudio: Advanced R Markdown
– Jenny Bryan, University of British Columbia: Happy Git and GitHub for the UseR

Featured Speakers
– Max Kuhn, Senior Director Non-Clinical Statistics, Pfizer
– Dirk Eddelbuettel, Ketchum Trading: Extending R with C++: A Brief Introduction to Rcpp
– Hilary Parker, Stitch Fix: Opinionated Analysis Development“
Bryan Lewis, Paradigm4: “Fun with htmlwidgets”
Ryan Hafen, Hafen Consulting: “Interactive plotting with rbokeh and crosstalk”
Julia Silge, Datassist: “Text mining, the tidy way”
Bob Rudis, Rapid7: “Writing readable code with pipes”

Featured Talk
– Joseph Rickert, R Ambassador, RStudio: R’s Role in Data Science

Be sure to visit https://www.rstudio.com/conference/ for the full schedule and latest updates and don’t forget to download the RStudio conference app to help you plan your days in detail.

Special Reminder: When you register, make sure you purchase your ticket for Friday evening at Universal’s Wizarding World of Harry Potter. The park is reserved exclusively for rstudio::conf attendees. It’s an extraordinary experience we’re sure you’ll enjoy!

We appreciate our sponsors and exhibitors!

Today we’re excited to announce R Notebooks, which add a powerful notebook authoring engine to R Markdown. Notebook interfaces for data analysis have compelling advantages including the close association of code and output and the ability to intersperse narrative with computation. Notebooks are also an excellent tool for teaching and a convenient way to share analyses.

screen-shot-2016-09-21-at-3-42-44-pm

You can try out R Notebooks today in the RStudio Preview Release.

Interactive R Markdown

As an authoring format, R Markdown bears many similarities to traditional notebooks like Jupyter and Beaker. However, code in notebooks is typically executed interactively, one cell at a time, whereas code in R Markdown documents is typically executed in batch.

R Notebooks bring the interactive model of execution to your R Markdown documents, giving you the capability to work quickly and iteratively in a notebook interface without leaving behind the plain-text tools and production-quality output you’ve come to rely on from R Markdown.

R Markdown Notebooks Traditional Notebooks
Plain text representation
Same editor/tools used for R scripts
Works well with version control
Focus on production output
Output inline with code
Output cached across sessions
Share code and output in a single file
Emphasized execution model Interactive & Batch Interactive

This video provides a bit more background and a demonstration of notebooks in action:

Iterate Quickly

In a typical R Markdown document, you must re-knit the document to see your changes, which can take some time if it contains non-trivial computations. R Notebooks, however, let you run code and see the results in the document immediately. They can include just about any kind of content R produces, including console output, plots, data frames, and interactive HTML widgets.

screen-shot-2016-09-20-at-4-16-47-pm

You can see the progress of the code as it runs:

screen-shot-2016-09-21-at-10-52-02-am

You can preview the results of individual inline expressions, too:

notebook-inline-output

Even your LaTeX equations render in real-time as you type:

notebook-mathjax

This focused mode of interaction doesn’t require you to keep the console, viewer, or output panes open. Everything you need is at your fingertips in the editor, reducing distractions and helping you concentrate on your analysis. When you’re done, you’ll have a formatted, reproducible record of what you’ve accomplished, with plenty of context, perfect for your own records or sharing with others.

Batteries Included

R Notebooks can run more than just R code. You can run chunks written in other languages, like Python, Bash, or C++ (Rcpp).

screen-shot-2016-09-20-at-4-25-48-pm

It’s even possible to run SQL directly:

notebook-sql

This makes an R Notebook an excellent tool for orchestrating a reproducible, end-to-end data analysis workflow; you can easily ingest data using your tool of choice, and share data among languages by using packages like feather, or ordinary CSV files.

Reproducible Notebooks

While you can run chunks (and even individual lines of R code!) in any order you like, a fully reproducible document must be able to be re-executed start-to-finish in a clean environment. There’s a built-in command to do this, too, so it’s easy to test your notebooks for reproducibility.

screen-shot-2016-09-21-at-3-52-34-pm

Rich Output Formats

Since they’re built on R Markdown, R Notebooks work seamlessly with other R Markdown output types. You can use any existing R Markdown document as a notebook, or render (knit) a notebook to any R Markdown output type.

notebook-yaml

The same document can be used as a notebook when you’re quickly iterating on ideas and later rendered to a wholly different format for publication – no duplication of code, data, or output required.

Share and Publish

R Notebooks are easy to share with collaborators. Because they’re plain-text files, they work well with version control systems like Git. Your collaborators don’t even need RStudio to edit them, since notebooks can be rendered in the R console using the open source rmarkdown package.

Rendered notebooks can be previewed right inside RStudio:

notebook-preview

While the notebook preview looks similar to a rendered R Markdown document, the notebook preview does not execute any of your R code chunks; it simply shows you a rendered copy of the markdown in your document along with the most recent chunk output. Because it’s very fast to generate this preview (again, no R code is executed), it’s generated every time you save the R Markdown document.

The generated HTML file has the special extension .nb.html. It is self-contained, free of dependencies, and can be viewed locally or published to any static web hosting service.

screen-shot-2016-09-14-at-12-12-35-pm

It also includes a bundled copy of the R Markdown source file, so it can be seamlessly opened in RStudio to resume work on the notebook with all output intact.

Try It Out

To try out R Notebooks, you’ll need to download the latest RStudio Preview Release.

You can find documentation on notebook features on the R Notebooks page on the R Markdown website, and we’ve also published a video tutorial in our R Notebooks Webinar.

We believe the R Notebook will become a powerful new addition to your toolkit. Give it a spin and let us know what you think!

The JSM conference in Chicago, July 31 thru August 4, 2016, is one of the largest to be found on statistics, with many terrific talks for R users. We’ve listed some of the sessions that we’re particularly excited about below. These include talks from RStudio employees, like Hadley Wickham, Yihui Xie, Mine Cetinkaya-Rundel, Garrett Grolemund, and Joe Cheng, but also include a bunch of other talks about R that we think look interesting.

When you’re not in one of the sessions below, please visit us in the exhibition area, booth #126-128. We’ll have copies of all our cheat sheets and stickers, and it’s a great place to learn about the other stuff we’ve been working on lately:  from Sparklyr and R Markdown Notebooks to the latest in RStudio Server Pro, Shiny Server Pro, shinyapps.io, RStudio Connect (beta) and more!

Another great place to chat with people interested in R is the Statistical Computing and Graphics Mixer at 6pm on Monday in the Hilton Stevens Salon A4. It’s advertised as a business meeting in the program, but don’t let that put you off – it’s open to all.

SUNDAY

Session 21: Statistical Computing and Graphics Student Awards
Sunday, July 31, 2016 : 2:00 PM to 3:50 PM, CC-W175b

Session 47 Making the Most of R Tools
Hadley Wickham, RStudio (Discussant)
Sunday, July 31, 2016: 4:00 PM to 4:50 PM, CC-W183b

Thinking with Data Using R and RStudio: Powerful Idioms for Analysts
Nicholas Jon Horton, Amherst College; Randall Pruim, Calvin College ; Daniel Kaplan, Macalester College
Transform Your Workflow and Deliverables with Shiny and R Markdown
Garrett Grolemund, RStudio

Session 54 Recent Advances in Information Visualization
Yihui Xie, RStudio (organizer)
Sunday, July 31, 2016: 4:00 PM to 4:50 PM, CC-W183c

Session 85 Reproducibility Promotes Transparency, Efficiency, and Aesthetics
Richard Schwinn
Sunday, July 31, 2016 : 5:35 PM to 5:50 PM, CC-W176a

Session 88 Communicate Better with R, R Markdown, and Shiny
Garrett Grolemund, RStudio (Poster Session)
Sunday, July 31, 2016: 6:00 PM to 8:00 PM, CC-Hall F1 West

MONDAY

Session 106  Linked Brushing in R
Hadley Wickham, RStudio
Monday, August 1, 2016 : 8:35 AM to 8:55 AM, CC-W196b

Session 127 R Tools for Statistical Computing
Monday, August 1, 2016 : 8:30 AM to 10:20 AM, CC-W196c

8:35 AM The Biglasso Package: Extending Lasso Model Fitting to Big Data in R — Yaohui Zeng, University of Iowa ; Patrick Breheny, University of Iowa
8:50 AM Independent Sampling for a Spatial Model with Incomplete Data — Harsimran Somal, University of Iowa ; Mary Kathryn Cowles, University of Iowa
9:05 AM Introduction to the TextmineR Package for R — Thomas Jones, Impact Research
9:20 AM Vector-Generalized Time Series Models — Victor Miranda Soberanis, University of Auckland ; Thomas Yee, University of Auckland
9:35 AM New Computational Approaches to Large/Complex Mixed Effects Models — Norman Matloff, University of California at Davis
9:50 AM Broom: An R Package for Converting Statistical Modeling Objects Into Tidy Data Frames — David G. Robinson, Stack Overflow
10:05 AM Exact Parametric and Nonparametric Likelihood-Ratio Tests for Two-Sample Comparisons — Yang Zhao, SUNY Buffalo ; Albert Vexler, SUNY Buffalo ; Alan Hutson, SUNY Buffalo ; Xiwei Chen, SUNY Buffalo

Session 270 Automated Analytics and Data Dashboards for Evaluating the Impacts of Educational Technologies
Daniel Stanhope and Joyce Yu and Karly Rectanus
Monday, August 1, 2016 : 3:05 PM to 3:50 PM, CC-Hall F1 West

TUESDAY

Session 276 Statistical Tools for Clinical Neuroimaging
Ciprian Crainiceanu
Tuesday, August 2, 2016 : 7:00 AM to 8:15 AM, CC-W375a

Session 332 Doing More with Data in and Outside the Undergraduate Classroom
Mine Cetinkaya-Rundel, Duke University (organizer)
Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM, CC-W184bc

Session 407 Interactive Visualizations and Web Applications for Analytics
Tuesday, August 2, 2016 : 2:00 PM to 3:50 PM, CC-W179a

2:05 PM Radiant: A Platform-Independent Browser-Based Interface for Business Analytics in R — Vincent Nijs, Rady School of Management
2:20 PM Rbokeh: An R Interface to the Bokeh Plotting Library — Ryan Hafen, Hafen Consulting
2:35 PM Composable Linked Interactive Visualizations in R with Htmlwidgets and Shiny — Joseph Cheng, RStudio
2:50 PM Papayar: A Better Interactive Neuroimage Plotter in R — John Muschelli, The Johns Hopkins University
3:05 PM Interactive and Dynamic Web-Based Graphics for Data Analysis — Carson Sievert, Iowa State University
3:20 PM HTML Widgets: Interactive Visualizations from R Made Easy! — Yihui Xie, RStudio ; Ramnath Vaidyanathan, Alteryx

WEDNESDAY

Session 475  Steps Toward Reproducible Research
Yihui Xie, RStudio  (Discussant)
Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM, CC-W196c

8:35 AM Reproducibility for All and Our Love/Hate Relationship with Spreadsheets — Jennifer Bryan, University of British Columbia
8:55 AM Steps Toward Reproducible Research — Karl W. Broman, University of Wisconsin – Madison
9:15 AM Enough with Trickle-Down Reproducibility: Scientists, Open This Gate! Scientists, Tear Down This Wall! — Karthik Ram, University of California at Berkeley
9:35 AM Integrating Reproducibility into the Undergraduate Statistics Curriculum — Mine Cetinkaya-Rundel, Duke University

Session 581 Mining Text in R
David Marchette, Naval Surface Warfare Center
Wednesday, August 3, 2016 : 2:05 PM to 2:40 PM, CC-W180

THURSDAY

Session 696 Statistics for Social Good
Hadley Wickham, RStudio (Chair)
Thursday, August 4, 2016 : 10:30 AM to 12:20 PM, CC-W179a

Session 694 Web Application Teaching Tools for Statistics Using R and Shiny
Jimmy Doi and Gail Potter and Jimmy Wong and Irvin Alcaraz and Peter Chi
Thursday, August 4, 2016 : 11:05 AM to 11:20 AM, CC-W192a

Following our initial and very gratifying Shiny Developer Conference this past January, which sold out in a few days, RStudio is very excited to announce a new and bigger conference today!

rstudio::conf, the conference about all things R and RStudio, will take place January 13 and 14, 2017 in Orlando, Florida. The conference will feature talks and tutorials from popular RStudio data scientists and developers like Hadley Wickham, Yihui Xie, Joe Cheng, Winston Chang, Garrett Grolemund, and J.J. Allaire, along with lightning talks from RStudio partners and customers.

Preceding the conference, on January 11 and 12, RStudio will offer two days of optional training. Training attendees can choose from Hadley Wickham’s Master R training, a new Intermediate Shiny workshop from Shiny creator Joe Cheng or a new workshop from Garrett Grolemund that is based on his soon-to-be-published book with Hadley: Introduction to Data Science with R.

rstudio::conf is for R and RStudio users who want to learn how to write better shiny applications in a better way, explore all the new capabilities of the R Markdown authoring framework, apply R to big data and work effectively with Spark, understand the RStudio toolchain for data science with R, discover best practices and tips for coding with RStudio, and investigate enterprise scale development and deployment practices and tools, including the new RStudio Connect.

Not to be missed, RStudio has also reserved Universal Studio’s The Wizarding World of Harry Potter on Friday night, January 13, for the exclusive use of conference attendees!

Conference attendance is limited to 400. Training is limited to 70 students for each of the three 2-day workshops. All seats are are available on a first-come, first-serve basis.

Please go to http://www.rstudio.com/conference to purchase.

We hope to see you in Florida at rstudio::conf 2017!

For questions or issues registering, please email conf@rstudio.com. To ask about sponsorship opportunities contact anne@rstudio.com.

UseR! 2016 has arrived and the RStudio team is at Stanford to share our newest products and latest enhancements to Shiny, R Markdown, dplyr, and more. Here’s a quick snapshot of RStudio related sessions. We hope to see you in as many of them as you can attend!

Monday June 27

Morning Tutorials

Afternoon Tutorials

Afternoon short talks moderated by Hadley Wickham

Tuesday June 28

Wednesday June 29

Thursday June 30

Stop by the booth!
Don’t miss our table in the exhibition area during the conference. Come talk to us about your plans for R and learn how RStudio Server Pro and Shiny Server Pro can provide enterprise-ready support and scalability for your RStudio IDE and Shiny deployments.

Note: Although UseR! is sold out, arrangements have been made to stream the keynote talks from https://aka.ms/user2016conference. Video recordings of the other sessions (where permitted by speakers) will be made available by UseR! organizers after the conference.

 

Today we’re excited to announce flexdashboard, a new package that enables you to easily create flexible, attractive, interactive dashboards with R. Authoring and customization of dashboards is done using R Markdown and you can optionally include Shiny components for additional interactivity.

neighborhood-diversity-flexdashboard

Highlights of the flexdashboard package include:

  • Support for a wide variety of components including interactive htmlwidgets; base, lattice, and grid graphics; tabular data; gauges; and value boxes.
  • Flexible and easy to specify row and column-based layouts. Components are intelligently re-sized to fill the browser and adapted for display on mobile devices.
  • Extensive support for text annotations to include assumptions, contextual narrative, and analysis within dashboards.
  • Storyboard layouts for presenting sequences of visualizations and related commentary.
  • By default dashboards are standard HTML documents that can be deployed on any web server or even attached to an email message. You can optionally add Shiny components for additional interactivity and then deploy on Shiny Server or shinyapps.io.

Getting Started

The flexdashboard package is available on CRAN; you can install it as follows:

install.packages("flexdashboard", type = "source")

To author a flexdashboard you create an R Markdown document with the flexdashboard::flex_dashboard output format. You can do this from within RStudio using the New R Markdown dialog:

Dashboards are simple R Markdown documents where each level 3 header (###) defines a section of the dashboard. For example, here’s a simple dashboard layout with 3 charts arranged top to bottom:

---
title: "My Dashboard"
output: flexdashboard::flex_dashboard
---

### Chart 1
 
```{r}

```
 
### Chart 2

```{r}

```

### Chart 3

```{r}

```

You can use level 2 headers (-----------) to introduce rows and columns into your dashboard and section attributes to control their relative size:

---
title: "My Dashboard"
output: flexdashboard::flex_dashboard
---

Column {data-width=600}
-------------------------------------
 
### Chart 1
 
```{r}

```
 
Column {data-width=400}
-------------------------------------
 
### Chart 2

```{r}

``` 
 
### Chart 3
 
```{r}

```

Learning More

The flexdashboard website includes extensive documentation on building your own dashboards, including:

  • A user guide for all of the features and options of flexdashboard, including layout orientations (row vs. column based), chart sizing, the various supported components, theming, and creating dashboards with multiple pages.
  • Details on using Shiny to create dashboards that enable viewers to change underlying parameters and see the results immediately, or that update themselves incrementally as their underlying data changes.
  • A variety of sample layouts which you can use as a starting point for your own dashboards.
  • Many examples of flexdashboard in action (including links to source code if you want to dig into how each example was created).

The examples below illustrate the use of flexdashboard with various packages and layouts (click the thumbnail to view a running version of each dashboard):

htmlwidgets-d3heatmap

d3heatmap: NBA scoring

ggplotly: ggplot2 geoms

ggplotly: ggplot2 geoms

Shiny: biclust example

Shiny: biclust example

dygraphs: Linked time series

dygraphs: linked time series

highcharter: sales report

highcharter: sales report

Storyboard: htmlwidgets showcase

Storyboard: htmlwidgets showcase

rbokeh: iris dataset

rbokeh: iris dataset

Shiny: diamonds explorer

Shiny: diamonds explorer

 

Try It Out

The flexdashboard package provides a simple yet powerful framework for creating dashboards from R. If you know R Markdown you already know enough to begin creating dashboards right now! We hope you’ll try it out and let us know how it’s working and what else we can do to make it better.

 

If you’re a data wrangler or data scientist, ODSC East in Boston from May 20-22 is a wonderful opportunity to get up-to-date on the latest open source tools and trends. R and RStudio will have a significant presence.

J.J. Allaire, RStudio founder and CEO, will talk about recent and upcoming improvements in R Markdown.

The creator of Shiny and CTO of RStudio, Joe Cheng, will review the progress made bridging modern web browsers and R, along with the newest updates to htmlwidgets and Shiny frameworks. In addition, Joe will join Zev Ross Spatial Analysis to offer a Shiny developer workshop for those interested in a deeper dive.

Other notable R speakers include Max Kuhn, the author of the Caret package for machine learning and Jared Lander, R contributor and author of R for Everyone.

For RStudio and R enthusiasts, ODSC has graciously offered discounted tickets.

We hope to see you there!

The R Markdown package ships with a raft of output formats including HTML, PDF, MS Word, R package vignettes, as well as Beamer and HTML5 presentations. This isn’t the entire universe of available formats though (far from it!). R Markdown formats are fully extensible and as a result there are several R packages that provide additional formats. In this post we wanted to highlight a few of these packages, including:

  • tufte — Documents in the style of Edward Tufte
  • rticles — Formats for creating LaTeX based journal articles
  • rmdformats — Formats for creating HTML documents

We’ll also discuss how to create your own custom formats as well as re-usable document templates for existing formats.

Using Custom Formats

Custom R Markdown formats are just R functions which return a definition of the format’s behavior. For example, here’s the metadata for a document that uses the html_document format:

---
title: "My Document"
output: html_document
---

When rendering, R Markdown calls the rmarkdown::html_document function to get the definition of the output format. A custom format works just the same way but is also qualified with the name of the package that contains it. For example, here’s the metadata for a document that uses the tufte_handout format:

---
title: "My Document"
output: tufte::tufte_handout
---

Custom formats also typically register a template that helps you get started with using them. If you are using RStudio you can easily create a new document based on a custom format via the New R Markdown dialog:

Screen Shot 2016-03-21 at 11.16.04 AM

Tufte Handouts

The tufte package includes custom formats for creating documents in the style that Edward Tufte uses in his books and handouts. Tufte’s style is known for its extensive use of sidenotes, tight integration of graphics with text, and well-set typography. Formats for both LaTeX and HTML/CSS output are provided (these are in turn based on the work in tufte-latex and tufte-css). Here’s some example output from the LaTeX format:

If you want LaTeX/PDF output, you can use the tufte_handout format for handouts and tufte_book for books. For HTML output, you use the tufte_html format. For example:

---
title: "An Example Using the Tufte Style"
author: "John Smith"
output:
  tufte::tufte_handout: default
  tufte::tufte_html: default
---

You can install the tufte package from CRAN as follows:

install.packages("tufte")

See the tufte package website for additional documentation on using the Tufte custom formats.

Journal Articles

The rticles package provides a suite of custom R Markdown LaTeX formats and templates for various journal article formats, including:

Screen Shot 2016-03-21 at 11.48.40 AM

You can install the rticles package from CRAN as follows:

install.packages("rticles")

See the rticles repository for more details on using the formats included with the package. The source code of the rticles package is an excellent resource for learning how to create LaTeX based custom formats.

rmdformats Package

The rmdformats package from Julien Barnier includes three HTML based document formats that provide nice alternatives to the default html_document format that is included in the rmarkdown package. The readthedown format is inspired by the Read the docs Sphinx theme and is fully responsive, with collapsible navigation:

readthedown

The html_docco and html_clean formats both provide provide automatic thumbnails for figures with lightbox display, and html_clean provides an automatic and dynamic table of contents:

html_docco html_clean

You can install the rmdformats package from CRAN as follows:

install.packages("rmdformats")

See the rmdformats repository for documentation on using the readthedown, html_docco, and html_clean formats.

Creating New Formats

Hopefully checking out some of the custom formats described above has you inspired to create your very own new formats. The R Markdown website includes documentation on how to create a custom format. In addition, the source code of the tufte, rticles, and rmdformats packages provide good examples to work from.

Short of creating a brand new format, it’s also possible to create a re-usable document template that shows up within the RStudio New R Markdown dialog box. This would be appropriate if an existing template met your needs but you wanted to have an easy way to create documents with a pre-set list of options and skeletal content. See the article on document templates for additional details on how to do this.

A new release of the rmarkdown package is now available on CRAN. This release features some long-requested enhancements to the HTML document format, including:

  1. The ability to have a floating (i.e. always visible) table of contents.
  2. Folding and unfolding for R code (to easily show and hide code for either an entire document or for individual chunks).
  3. Support for presenting content within tabbed sections (e.g. several plots could each have their own tab).
  4. Five new themes including “lumen”, “paper”, “sandstone”, “simplex”, & “yeti”.

There are also three new formats for creating GitHub, OpenDocument, and RTF documents as well as a number of smaller enhancements and bug fixes (see the package NEWS for all of the details).

Floating TOC

You can specify the toc_float option to float the table of contents to the left of the main document content. The floating table of contents will always be visible even when the document is scrolled. For example:

---
title: "Habits"
output:
  html_document:
    toc: true
    toc_float: true
---

Here’s what the floating table of contents looks like on one of the R Markdown website’s pages:

FloatingTOC

Code Folding

When the knitr chunk option echo = TRUE is specified (the default behavior) the R source code within chunks is included within the rendered document. In some cases it may be appropriate to exclude code entirely (echo = FALSE) but in other cases you might want the code available but not visible by default.

The code_folding: hide option enables you to include R code but have it hidden by default. Users can then choose to show hidden R code chunks either indvidually or document wide. For example:

---
title: "Habits"
output:
  html_document:
    code_folding: hide
---

Here’s the default HTML document template with code folding enabled. Note that each chunk has it’s own toggle for showing or hiding code and there is also a global menu for operating on all chunks at once.

Screen Shot 2016-03-21 at 7.27.40 AM

Note that you can specify code_folding: show to still show all R code by default but then allow users to hide the code if they wish.

Tabbed Sections

You can organize content using tabs by applying the .tabset class attribute to headers within a document. This will cause all sub-headers of the header with the .tabset attribute to appear within tabs rather than as standalone sections. For example:

## Sales Report {.tabset}

### By Product

(tab content)

### By Region

(tab content)

Here’s what tabbed sections look like within a rendered document:

Screen Shot 2016-03-21 at 7.43.38 AM

Authoring Enhancements

We also shouldn’t fail to mention that the most recent release of RStudio included several enhancements to R Markdown document editing. There’s now an optional outline view that enables quick navigation across larger documents:

Screen Shot 2015-12-22 at 9.27.34 AM

We also also added inline UI to code chunks for running individual chunks, running all previous chunks, and specifying various commonly used knit options:

Screen Shot 2015-12-22 at 9.30.11 AM

What’s Next

We’ve got lots of additional work planned for R Markdown including new document formats, additional authoring enhancements in RStudio, and some new tools to make it easier to publish and manage documents created with R Markdown. More details to follow soon!