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RStudio has teamed up with O’Reilly media to create a new way to learn R!

The Introduction to Data Science with R video course is a comprehensive introduction to the R language. It’s ideal for non-programmers with no data science experience or for data scientists switching to R from Excel, SAS or other software.

Join RStudio Master Instructor Garrett Grolemund as he covers the three skill sets of data science: computer programming (with R), manipulating data sets (including loading, cleaning, and visualizing data), and modeling data with statistical methods. You’ll learn R’s syntax and grammar as well as how to load, save, and transform data, generate beautiful graphs, and fit statistical models to the data.

All of the techniques introduced in this video are motivated by real problems that involve real datasets. You’ll get plenty of hands-on experience with R (and not just hear about it!), and lots of help if you get stuck.

You’ll also learn how to use the ggplot2, reshape2, and dplyr packages.

The course contains over eight hours of instruction. You can access the first hour free from O’Reilly’s website. The course covers the same content as our two day Introduction to Data Science with R workshop, right down to the same exercises. But unlike our workshops, the videos are self-paced, which can help you learn R in a more relaxed way.

To learn more, visit Introduction to Data Science with R.

Are you headed to Strata? It’s just around the corner!

We particularly hope to see you at R Day on October 15, where we will cover a raft of current topics that analysts and R users need to pay attention to. The R Day tutorials come from Hadley Wickham, Winston Chang, Garrett Grolemund, J.J. Allaire, and Yihui Xie who are all working on fascinating new ways to keep the R ecosystem apace of the challenges facing those who work with data.

If you plan to stay for the full Strata Conference+Hadoop World be sure to look us up in the Innovator Pavilion booth P14 during the Expo Hall hours. We’ll have the latest books from RStudio authors and “shiny” t-shirts to win. Share with us what you’re doing with RStudio and get your product and company questions answered by RStudio employees.

See you in New York City!

The Joint Statistical Meetings (JSM) start this weekend! We wanted to let you know we’ll be there. Be sure to check out these sessions from RStudio and friends:

Sunday, August 3

  • 4:00 PM: A Web Application for Efficient Analysis of Peptide Libraries: Eric Hare*+ and Timo Sieber and Heike Hofmann
  • 4:00 PM: Gravicom: A Web-Based Tool for Community Detection in Networks: Andrea Kaplan*+ and Heike Hofmann and Daniel Nordman

Monday, August 4

  • 8:35 AM: Preparing Students for Big Data Using R and Rstudio: Randall Pruim
  • 8:55 AM: Thinking with Data in the Second Course: Nicholas J. Horton and Ben S. Baumer and Hadley Wickham
  • 8:55 AM: Doing Reproducible Research Unconsciously: Higher Standard, but Less Work: Yihui Xie
  • 10:30 AM: Interactive Web Application with Shiny: Bharat Bahadur
  • 2:00 PM: Interactive Web Application with Shiny: Bharat Bahadur

Tuesday, August 5

  • 2:00PM: Give Me an Old Computer, a Blank DVD, and an Internet Connection and I’ll Give You World-Class Analytics: Ty Henkaline

Wednesday, August 6

  • 10:35 Shiny: Easy Web Applications in R:Joseph Cheng
  • 11:00 AM: ggvis: Moving Toward a Grammar of Interactive Graphics: Hadley Wickham

For even more talks on R we thought Joseph Rickert’s “Data Scientists and R Users Guide to the JSM” was excellent. Click here to see it. http://blog.revolutionanalytics.com/2014/07/a-data-scientists-and-r-users-guide-to-the-jsm.html

While you’re at the conference, please come by our exhibition area (Booth #112) to say hello. J.J., Hadley and other members of the team will be there. We’ve got enough space to talk about your plans for R and how RStudio Server Pro and Shiny Server Pro can provide enterprise-ready support and scalability for your RStudio IDE and Shiny Server deployments.

We hope to see you there!

RStudio is very pleased to announce the general availability of Shiny Server Pro 1.2.

Download a free 45 day evaluation of Shiny Server Pro 1.2

Shiny Server Pro 1.2 adds support for R Markdown Interactive Documents in addition to Shiny applications. Learn more about Interactive Documents by registering for the Reproducible Reporting webinar August 13 and Interactive Reporting webinar September 3.

We are excited about the new ways in which you can now share your data analysis in Shiny Server Pro along with the security, management and performance tuning capabilities you and your IT teams need to scale.

Uncover all the features of Shiny Server Pro 1.2 in the updated Shiny Server admin guide…then give it a try!

The RStudio team recently rolled out new capabilities in RStudio, shiny, ggvis, dplyr, knitr, R Markdown, and packrat. The “Essential Tools for Data Science with R” free webinar series is the perfect place to learn more about the power of these R packages from the authors themselves.

Click to learn more and register for one or more webinar sessions. You must register for each separately. If you miss a live webinar or want to review them, recorded versions will be available to registrants within 30 days.

The Grammar and Graphics of Data Science
Live! Wednesday, July 30 at 11am Eastern Time US  Click to register

  • dplyr: a grammar of data manipulation – Hadley Wickham
  • ggvis: Interactive graphics in R – Winston Chang

Reproducible Reporting 
Live! Wednesday, August 13 at 11am Eastern Time US  Click to register

  • The Next Generation of R Markdown – Jeff Allen
  • Knitr Ninja – Yihui Xie
  • Packrat – A Dependency Management System for R – J.J. Allaire & Kevin Ushey

Interactive Reporting
Live! Wednesday, September 3 at 11am Eastern Time US  Click to register

  • Embedding Shiny Apps in R Markdown documents – Garrett Grolemund
  • Shiny: R made interactive – Joe Cheng

 

The R User Conference 2014 is coming up fast in Los Angeles!

RStudio will be there in force to share the latest enhancements to shiny, ggvis, knitr, dplyr. R markdown, packrat and more.  Here’s a quick snapshot of our scheduled sessions. We hope to see you in as many of them as you can attend!

Monday, June 30

Morning Tutorials

  • Interactive graphics with ggvis - Winston Chang
  • Dynamic Documents with R and knitr – Yihui Xie

Afternoon Tutorials

  • Data manipulation with dplyr – Hadley Wickham
  • Interactive data display with Shiny and R – Garrett Grolemund

Tuesday, July 1

Session 1 10:30 Room – Palisades
ggvis: Interactive graphics in R – Winston Chang

Session 2 13:00 Room – Palisades
Shiny: R made interactive – Joe Cheng

Session 3 16:00 Room – Palisades
dplyr: a grammar of data manipulation – Hadley Wickham

Wednesday, July 2

Session 5 16.00 Room – Palisades
Packrat – A Dependency Management System for R – J.J. Allaire

Thursday, July 3

Session 6 10:00 Room – Palisades
The Next Generation of R Markdown – J.J. Allaire
Knitr Ninja – Yihui Xie
Embedding Shiny Apps in R Markdown documents – Garrett Grolemund

Every Day

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.

People rarely agree on a best authoring tool or language. Some people cannot live without \LaTeX{} because of the beauty and quality of its PDF output. Some \feel{} \uncomfortable{} \with{} \backslashes{}, and would rather live in another World Word. We have also witnessed the popularity of Markdown, an incredibly simple language (seriously? a LANGUAGE?) that has made reproducible research much easier.

Thinking of all these tools and languages, every developer will dream about “One ring to rule them all“. \section{}, <h1></h1>, ===, #, … Why cannot we write the first-level section header in a single way? Yes, we are aware of the danger of “adding yet another so-called universal standard that covers all the previous standards”. However, we believe Pandoc has done a fairly good job in terms of “yet another Markdown standard”. Standing on the shoulders of Pandoc, today we are excited to announce the second episode of our journey into the development of the tools for authoring dynamic documents:

The Return of R Markdown!

The R package markdown (plus knitr) was our first version of R Markdown. The primary output format was HTML, which certainly could not satisfy all users in the World Word. It did not have features like citations, footnotes, or metadata (title, author, and date, etc), either. When we were asked how one could convert Markdown to PDF/Word, we used to tell users to try Pandoc. The problem is that Pandoc’s great power comes with a lot of command line options (more than 70), and knitr has the same problem of too many options. That is why we created the second generation of R Markdown, represented by the rmarkdown package, to provide reasonably good defaults and an R-friendly interface to customize Pandoc options.

The new version of RStudio (v0.98.932) includes everything you need to use R Markdown v2 (including pandoc and the rmarkdown package). If you are not using RStudio you can install rmarkdown and pandoc separately as described here. To get started with a “Hello Word” example, simply click the menu File -> New File -> R Markdown in RStudio IDE. You can choose the output format from the drop-down menu on the toolbar.

R Markdown Formats

The built-in output formats include HTML, LaTeX/PDF, Word, Beamer slides, HTML5 presentations, and so on. Pandoc’s Markdown allows us to write richer content such as tables, citations, and footnotes. For power users who understand LaTeX/HTML, you can even embed raw LaTeX/HTML code in Markdown, and Pandoc is smart enough to process these raw fragments. If you cannot remember the possible options for a certain output format in the YAML metadata (data between --- and --- in the beginning of a document), you can use the Settings button on the toolbar.

Extensive documentation for R Markdown v2 and all of it’s supported output formats are available on the new R Markdown website at http://rmarkdown.rstudio.com.

We understand users will never be satisfied by our default templates, regardless of how hard we try to make them appealing. The rmarkdown package is fully customizable and extensible in the sense that you can define your custom templates and output formats. You want to contribute an article to The R Journal, or JSS (Journal of Statistical Software), but prefer writing in Markdown instead of LaTeX? No problem! Pandoc also supports many other output formats, and you want EPUB books, or a different type of HTML5 slides? No problem! Not satisfied with one single static output document? You can embed interactive widgets into R Markdown documents as well! Let there be Shiny! The more you learn about rmarkdown and Pandoc, the more freedom you will get.

For a brief video introduction, you may watch the talk below (jump to 18:30 if you only want to see the demos):

The rmarkdown package is open-source (GPL-3) and is both included in the RStudio IDE and available on GitHub. The package is not on CRAN yet, but will be there as soon as we make all the improvements requested by early users.

To clarify the relationship between rmarkdown and RStudio IDE, our IDE is absolutely not the only way to compile R Markdown documents. You are free to call functions in rmarkdown in any environment. Please check out the R package documentation, in particular, the render() function in rmarkdown.

Please let us know if you have any questions or comments, and your feedback is greatly appreciated. We hope you will enjoy R Markdown v2.

Keep Calm and Markdown

Today we’re very pleased to announce a new version of RStudio (v0.98.932) which is available for download now. New features in this release include:

  • A next generation implementation of R Markdown with a raft of new features including support for HTML, PDF, and Word output, many new options for customizing document appearance, and the ability to create presentations (Beamer or HTML5).
  • Interactive Documents (Shiny meets R Markdown). Readers can now change the parameters underlying your analysis and see the results immediately. Interactive Documents make it easier than ever to use Shiny!
  • Shareable notebooks from R scripts. Notebooks include all R code and generated output, and can be rendered in HTML, PDF, and Word formats.
  • Enhanced debugging including support for the new R 3.1 debugging commands to step into function calls and finish the current loop or function.
  • Various source editor enhancements including new syntax highlighting modes for XML, YAML, SQL, Python, and shell scripts. You can also execute Python and shell scripts directly from the editor using Ctrl+Shift+Enter.
  • Integrated tools for Shiny development including the ability to run applications within an IDE pane as well as Run/Reload applications with a keyboard shortcut (Ctrl+Shift+Enter).
  • A new devtools mode for package development (uses devtools for check, document, test, build, etc.)
  • Contextual Git/SVN menu that enables quick access to per-file revision history and selection-aware View/Blame for projects hosted on GitHub.
  • Fast lookup of shortcuts using the new keyboard shortcut quick-reference card (Alt+Shift+K)

See the release notes for a full list of what’s changed and see Yihui Xie’s post on R Markdown v2 for more on what’s new there.

We’ll be posting additional articles over the next few days that describe the new features in more depth. In the meantime we hope you download the new version and as always let us know how it’s working and what else you’d like to see.

reshape2 1.4 is now available on CRAN. This version adds a number of useful arguments and messages, but mostly importantly it gains a C++ implementation of melt.data.frame(). This new method should be much much faster (>10x) and does a better job of preserving existing attributes. For full details, see the release notes on github.

The C++ implementation of melt was contributed by Kevin Ushey, who we’re very pleased to announce has joined RStudio. You may be familiar with Kevin from his contributions to Rcpp, or his CRAN packages Kmisc and timeit.

We are happy to announce the availability of v1.1 of RStudio Shiny Server Pro, our commercial server for deploying Shiny applications.  In this release we took your feedback and made it easier for you to integrate Shiny Server Pro into your production environments.  With Shiny Server Pro v1.1 you now can:

  • Control access to your applications with Google Authentication (OAuth2).
  • Create sessions and authenticate with PAM (auth_pam and pam_sessions_profile).
  • Set the version of R that is used per application and/or per user
  • Customize page templates for directory listings and error pages.
  • Monitor service health and get additional metrics with a new health check endpoint.
  • Provide custom environment variables to a Shiny process using Bash profiles
  • Configure apps to run using the authenticated user’s account with custom environment variables from Bash or PAM
  • Launch Shiny apps with a prefix command such as ‘nice’ allowing you to prioritize compute resources per application or  per user

If you haven’t tried Shiny Server Pro yet, download a copy here.

 

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