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RStudio is excited to announce the availability of its flagship enterprise-ready integrated development environment for R in AWS Marketplace.

RSP AWS

RStudio Server Pro AWS is identical to RStudio Server Pro, but with turnkey convenience. It comes pre-configured with multiple versions of R, common systems libraries, and the most popular R packages.

RStudio Server Pro AWS helps you adapt to your unique circumstances. It allows you to choose different AWS computing instances no matter how large, whenever a project requires it (flat hourly pricing). Or you can set up a persistent instance of RStudio Server Pro ready to be used anytime you need it (annual pricing), avoiding the sometimes complicated processes for procuring on-premises software.

If the enhanced security, elegant support for multiple R versions and multiple sessions, and commercially licensed and supported features of RStudio Server Pro appeal to you and your enterprise, consider RStudio Server Pro for AWS. It’s ready to go!

Read the FAQ         Try RStudio Server Pro AWS

We’re excited to announce a powerful new ability to organize content in RStudio Connect: version 1.5.0. Tags allow publishers to arrange what they’ve published and enable users to find and discover the content most relevant to them. The release also includes a newly designed (and customizable!) landing page and multiple important security enhancements.

New landing page in RStudio Connect v1.5.0

Tagging Content with a Custom Tag Schema

Tags can be used to manage and organize servers that have hundreds or even thousands of pieces of content published to them. Administrators can define a custom tag schema tailored to their organization. Publishers can then organize their content using tags, allowing all users to find the content they want by navigating through the tag schema.

See more details in the video below:

 

 

New Landing Page

The default landing page has been given a fresh look. Even better, administrators can now customize the landing page that logged out users will see when they visit the server. More details here.

Security Enhancements

This release includes multiple important security enhancements, so we recommend deploying this update as soon as possible. Specifically, this release adds protection for cross-site request forgery (CSRF) attacks and fixes two bugs around account management that could have been used to grant an account more permissions than it should have. These bugs were identified internally and we are not aware of any instances of these issues being exploited against a customer’s server.

Other notable changes this release:

  • [Authentication].Lifetime can be used to define the duration of a user’s session (the lifetime of their cookie) when they log in via web browser. It still defaults to 30 days.
  • Servers configured to use password authentication can now choose to disable user self-registration using the [Password].SelfRegistration setting. By default, this feature is still enabled.
  • Added experimental support for using PostgreSQL instead of SQLite as Connect’s database. If you’re interested in helping to test this feature, please contact support@rstudio.com.
  • Allow user and group names to contain periods.
  • Added support for the config package. More details here.
  • Formally documented the configuration settings that support being reloaded via a HUP signal. Settings now mention “Reloadable: true” in the documentation if they support reloading.
  • Renamed the “Performance” tab for Shiny applications to “Runtime.”
  • Further improve database performance in high-traffic environments.

If you haven’t yet had a chance to download and try RStudio Connect we encourage you to do so. RStudio Connect is the best way to share all the work that you do in R (Shiny apps, R Markdown documents, plots, dashboards, etc.) with collaborators, colleagues, or customers.

You can find more details or download a 45 day evaluation of the product at https://www.rstudio.com/products/connect/. Additional resources can be found below.

We love to engage with R and RStudio users online in webinars and communities because it is so efficient for everyone. But sometimes it’s great to meet in person, too!

Next week RStudio will be in Miami, Baltimore and Chicago. We wanted to let you know in case you’ll be there at the same time and want to “Connect” (yes, we said it :)) with us.

At each of these events we’ll have the latest books signed by RStudio authors, t-shirts to win, demonstrations of RStudio Connect and RStudio Server Pro and, of course, stickers and cheatsheets. Share with us what you’re doing with RStudio and get your product and company questions answered!

Apache Big Data – Miami
If big data is your thing, you use R, and you’re headed to Apache Big Data in Miami May 15th through the 18th, you can find out in person how easy and practical it is to analyze big data with R and Spark.

While you’re at the conference be sure to look us up at booth number 104 during the Expo Hall hours.

PharmaSUG – Baltimore
If you’re in the Pharma industry, you use R, and you’re headed to PharmaSUG in Baltimore May 14th through the 17th, we hope you’ll look us up. PharmaSUG is a not-to-be-missed event for programmers, statisticians, data managers, and others in the pharmaceutical, healthcare, and related industries.

Phil Bowsher from RStudio will be presenting An Introduction to Shiny, R Markdown, and HTML Widgets for R with Applications in Drug Development at 8am on Sunday, May 14th.

We will be in booth number 204 during the Expo Hall hours.

R/Finance – Chicago
Every year, new and interesting ways R is used in the financial industry surface at R/Finance. If you’re going to Chicago May 19th and the 20th, we hope you’ll come talk to us. You can’t miss us at R/Finance!

Jonathan Regenstein from RStudio will be presenting Reproducible Finance: A Global ETF Map and Shiny App at 2pm on Saturday, May 20th.

Otherwise, if those aren’t places you’ll be next week, look for us in London, San Francisco, Brussels, or one of the many other events coming soon!

We’re excited to announce the release of RStudio Connect: version 1.4.6. This is an incremental release which features significantly improved startup time and support for server-side Shiny bookmarks.

Creating a server-side Shiny bookmark in RStudio Connect

Improved Startup & Job Listing Time

We now track R process jobs in the database which allows us to list and query jobs much more quickly. This decreases the startup time of the RStudio Connect service — allowing even the busiest of servers to spin up in a matter of seconds. Additionally, operations that involve listing jobs such as viewing process logs for a particular application should be noticeably faster.

Server-Side Shiny Bookmarks

Shiny v0.14 introduced a feature by which users could bookmark the current state of the application by either encoding the state in the URL or saving the state to the server. As of this release, RStudio Connect now supports server-side bookmarking of Shiny applications.

Other notable changes this release:

  • BREAKING: Changed the default for Authorization.DefaultUserRole from publisher to viewer. New users will now be created with a viewer account until promoted. The user roles documentation explains the differences. To restore the previous behavior, set DefaultUserRole = publisher. Because viewer users cannot be added as collaborators on content, this means that in order to add a remote user as a collaborator on content you must first create their account, then promote them to a publisher account.
  • Fixed a bug in the previous release that had broken Applications.ViewerOnDemandReports and Applications.ViewerCustomizedReports. These settings are again functional and allow you to manage the capabilities of a viewer of a parameterized report on the server.
  • Tune the number of concurrent processes to use when building R packages. This is controlled with the Server.CompilationConcurrency setting and passed as the value to the make flag -jNUM. The default is to permit four concurrent processes. Decrease this setting in low memory environments.
  • The /etc/rstudio-connect/rstudio-connect.gcfg file is installed with more restrictive permissions.
  • Log file downloads include a more descriptive file name by default. Previously, we used the naming convention <jobId>.log, which resulted in file names like GBFCaiPE6tegbrEM.log. Now, we use the naming convention rstudio-connect.<appId>.<reportId>.<bundleId>.<jobType>.<jobId>.log, which results in file names like rstudio-connect.34.259.15.packrat_restore.GBFCaiPE6tegbrEM.log.
  • Bundle the admin guide and user guide in the product. You can access both from the Documentation tab.
  • Implemented improved, pop-out filtering panel when filtering content, which offers a better experience on small/mobile screens.
  • Improvements to the parameterized report pane when the viewer does not have the authority to render custom versions of the document.
  • Database performance improvements which should improve performance in high-traffic environments.

Upgrade Planning: The migration of jobs from disk to the database may take a few minutes. The server will be unavailable during this migration which will be performed the first time RStudio Connect v1.4.6 starts. Even on the busiest of servers we would expect this migration to complete in under 5 minutes.

If you haven’t yet had a chance to download and try RStudio Connect we encourage you to do so. RStudio Connect is the best way to share all the work that you do in R (Shiny apps, R Markdown documents, plots, dashboards, etc.) with collaborators, colleagues, or customers.

You can find more details or download a 45 day evaluation of the product at https://www.rstudio.com/products/connect/. Additional resources can be found below.

We’re excited to announce the release of RStudio Connect: version 1.4.4.1. This release includes the ability to manage different versions of your work on RStudio Connect.

Managing old versions of deployed content in RStudio Connect

Rollback / Roll Forward
The most notable feature of this release is the ability to “rollback” to a previously deployed version of your work or “roll forward” to a more recent version of your work.

You can also download a particular version, perhaps as a starting place for a new report or application, and delete old versions that you want to remove from the server.

Other important features allow you to:

  • Specify the number of versions to retain. You can alter the setting Applications.BundleRetentionLimit to specify how many versions of your applications you want to keep on disk. By default, we retain all bundles eternally.
  • Limit the number of scheduled reports that will be run concurrently using the Applications.ScheduleConcurrency setting. This setting will help ensure that your server isn’t overwhelmed by too many reports all scheduled to run at the same time of day. The default is set to 2.
  • Create a printable view of your content with a new “Print” menu option.
  • Notify users of unsaved changes before they take an action in parameterized reports.

The release also includes numerous security and stability improvements.

If you haven’t yet had a chance to download and try RStudio Connect we encourage you to do so. RStudio Connect is the best way to share all the work that you do in R (Shiny apps, R Markdown documents, plots, dashboards, etc.) with collaborators, colleagues, or customers.

You can find more details or download a 45 day evaluation of the product at https://www.rstudio.com/products/connect/. Additional resources can be found below.

If big data is your thing, you use R, and you’re headed to Strata + Hadoop World in San Jose March 13 & 14th, you can experience in person how easy and practical it is to analyze big data with R and Spark.

In a beginner level talk by RStudio’s Edgar Ruiz and an intermediate level  workshop by Win-Vector’s John Mount, we cover the spectrum: What R is, what Spark is, how Sparklyr works, and what is required to set up and tune a Spark cluster. You’ll also learn practical applications including: how to quickly set up a local Spark instance, store big data in Spark and then connect to the data with R, use R to apply machine-learning algorithms to big data stored in Spark, and filter and aggregate big data stored in Spark and then import the results into R for analysis and visualization.

2:40pm–3:20pm Wednesday, March 15, 2017
Sparklyr: An R interface for Apache Spark
Edgar Ruiz (RStudio)
Primary topic: Spark & beyond
Location: LL21 C/D
Level: Beginner
Secondary topics: R

1:30pm–5:00pm Tuesday, March 14, 2017
Modeling big data with R, sparklyr, and Apache Spark
John Mount (Win-Vector LLC)
Primary topic: Data science & advanced analytics
Location: LL21 C/D
Level: Intermediate
Secondary topics: R

While you’re  at the conference be sure to look us up in the Innovator’s Pavilion – booth number P8 during the Expo Hall hours. We’ll have the latest books from RStudio authors, t-shirts to win, demonstrations of RStudio Connect and RStudio Server Pro and, of course, stickers and cheatsheets. Share with us what you’re doing with RStudio and get your product and company questions answered by RStudio employees.

See you in San Jose! (https://conferences.oreilly.com/strata/strata-ca)

We’re excited to announce the latest release of RStudio Connect: version 1.4.2. This release includes a number of notable features including an overhauled interface for parameterized R Markdown reports.

Enhanced Parameterized R Markdown Reports

Enhanced Parameterized R Markdown Reports

The most notable feature in this release is the ability to publish parameterized R Markdown reports that are easier for anyone to customize. If you’re unfamiliar, parameterized R Markdown reports allow you to inject input parameters into your R Markdown document to alter what analysis the report performs. The parameters of your R Markdown report are now visible on the left-hand sidebar, allowing users to easily tweak the inputs to the document and quickly view the output in the browser.

Users even have the opportunity to create private versions of the report which they can schedule to run again, email, or save and revisit in the browser. Of course, you can continue to use the wide variety of output formats (notebooks, dashboards, books, and others) while using parameterized R Markdown.

In addition to the parameterized report overhaul, there are some other notable features included in this release.

  • Content private by default – Content is set to private (“Just Me”) by default. Users can still change the visibility of their content before publishing, as before.
  • Execute R as the authenticated viewer – You can now choose to have some applications execute their underlying R process as the authenticated viewer currently looking at the app. This allows applications to access any data or resource that the associated user has access to on the server. Requires PAM authentication. More details here.

Other important features include:

  • Show progress indicator when updating a report.
  • Users can now filter content to include only items that they can edit or view.
  • Users now only count against the named user license limit after they log in for the first time.
  • Added support for global “System Messages” that can display an HTML message to your users on the landing pages. Details here.
  • Updated packrat to gain more transparency on package build errors.
  • Updated the list of SSL ciphers to correspond with modern best-practices.

If you haven’t yet had a chance to download and try RStudio Connect we encourage you to do so. RStudio Connect is the best way to share all the work that you do in R (Shiny apps, R Markdown documents, plots, dashboards, etc.) with collaborators, colleagues, or customers.

You can find more details or download a 45 day evaluation of the product at https://www.rstudio.com/products/connect/. Additional resources can be found below.

We’re thrilled to officially introduce the newest product in RStudio’s product lineup: RStudio Connect.

You can download a free 45-day trial of it here.

RStudio Connect is a new publishing platform for all the work your teams do in R. It provides a single destination for your Shiny applications, R Markdown documents, interactive HTML widgets, static plots, and more.

RStudio Connect Settings

RStudio Connect isn’t just for R users. Now anyone can interact with custom built analytical data products developed by R users without having to program in R themselves. Team members can receive updated reports built on the same models/forecasts which can be configured to be rebuilt and distributed on a scheduled basis. RStudio Connect is designed to bring the power of data science to your entire enterprise.

RStudio Connect empowers analysts to share and manage the content they’ve created in R. Users of the RStudio IDE can publish content to RStudio Connect with the click of a button and immediately be able to manage that content from a user-friendly web application: setting access controls and performance settings and viewing the logs of the associated R processes on the server.

Deploying content from the RStudio IDE into RStudio Connect

RStudio Connect is on-premises software that you can install on a server behind your firewall ensuring that your data and R applications never have to leave your organization’s control. We integrate with many enterprise authentication platform including LDAP/Active Directory, Google OAuth, PAM, and proxied authentication. We also provide an option to use an internal username/password system complete with user self-sign-up.

RStudio Connect Admin Metrics

RStudio Connect has been in Beta for almost a year. We’ve had hundreds of customers validate and help us improve the software in that time. In November, we made RStudio Connect generally available without significant fanfare and began to work with Beta participants and existing RStudio customers eager to move it into their production environments. We are pleased that innovative early customers, like AdRoll, have already successfully introduced RStudio Connect into their data science process.

“At AdRoll, we have used the open source version of Shiny Server for years to great success but deploying apps always served as a barrier for new users. With RStudio Connect’s push button deployment from the RStudio IDE, the number of shiny devs has grown tremendously both in engineering and across teams completely new to shiny like finance and marketing. It’s been really powerful for those just getting started to be able to go from developing locally to sharing apps with others in just seconds.”

– Bryan Galvin, Senior Data Scientist, AdRoll

We invite you to take a look at RStudio Connect today, too!

You can find more details or download a 45 day evaluation of the product at https://www.rstudio.com/products/connect/. Additional resources can be found below.

On October 12, RStudio launched R Views with great enthusiasm. R Views is a new blog for R users about the R Community and the R Language. Under the care of editor-in-chief and new RStudio ambassador-at-large, Joseph Rickert, R Views provides a new perspective on R and RStudio that we like to think will become essential reading for you.

You may have read an R Views post already. In the first, widely syndicated, post, Joseph interviewed J.J. Allaire, RStudio’s founder, CEO and most prolific software developer. Later posts by Mine Cetinkaya-Rundel on Highcharts and thoughtful book reviews, new R package picks, and a primer on Naive Bayes from Joseph rounded out the first month. Each post was entirely different from anything you could have read here, on what we now call our Developer Blog at rstudio.org.

Fortunately, you don’t have to choose. Each has its purpose. Our Developer Blog is the place to go for RStudio news. You’ll find product announcements, events, and company happenings – like the announcement of a new blog – right here. R Views is about R in action. You’ll find stories and solutions and opinions that we hope will educate and challenge you.

Subscribe to each and stay up to date on all things R and RStudio!

Thanks for making R and RStudio part of your data science experience and for supporting our work.

Today we’re very pleased to announce the availability of RStudio Version 1.0! Version 1.0 is our 10th major release since the initial launch in February 2011 (see the full release history below), and our biggest ever! Highlights include:

  • Authoring tools for R Notebooks.
  • Integrated support for the sparklyr package (R interface to Spark).
  • Performance profiling via integration with the profvis package.
  • Enhanced data import tools based on the readr, readxl and haven packages.
  • Authoring tools for R Markdown websites and the bookdown package.
  • Many other miscellaneous enhancements and bug fixes.

We hope you download version 1.0 now and as always let us know what you think.

R Notebooks

R Notebooks 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.

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, compatibility with version control, and production-quality output you’ve come to rely on from R Markdown.

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.

Spark with sparklyr

The sparklyr package is a new R interface for Apache Spark. RStudio now includes integrated support for Spark and the sparklyr package, including tools for:

  • Creating and managing Spark connections
  • Browsing the tables and columns of Spark DataFrames
  • Previewing the first 1,000 rows of Spark DataFrames

Once you’ve installed the sparklyr package, you should find a new Spark pane within the IDE. This pane includes a New Connection dialog which can be used to make connections to local or remote Spark instances:

Once you’ve connected to Spark you’ll be able to browse the tables contained within the Spark cluster:

The Spark DataFrame preview uses the standard RStudio data viewer:

Profiling with profvis

“How can I make my code faster?”

If you write R code, then you’ve probably asked yourself this question. A profiler is an important tool for doing this: it records how the computer spends its time, and once you know that, you can focus on the slow parts to make them faster.

RStudio now includes integrated support for profiling R code and for visualizing profiling data. R itself has long had a built-in profiler, and now it’s easier than ever to use the profiler and interpret the results.

To profile code with RStudio, select it in the editor, and then click on Profile -> Profile Selected Line(s). R will run that code with the profiler turned on, and then open up an interactive visualization.

In the visualization, there are two main parts: on top, there is the code with information about the amount of time spent executing each line, and on the bottom there is a flame graph, which shows what R was doing over time. In the flame graph, the horizontal direction represents time, moving from left to right, and the vertical direction represents the call stack, which are the functions that are currently being called. (Each time a function calls another function, it goes on top of the stack, and when a function exits, it is removed from the stack.)

profile.png

The Data tab contains a call tree, showing which function calls are most expensive:

Profiling data pane

Armed with this information, you’ll know what parts of your code to focus on to speed things up!

Data Import

RStudio now integrates with the readr, readxl, and haven packages to provide comprehensive tools for importing data from many text file formats, Excel worksheets, as well as SAS, Stata, and SPSS data files. The tools are focused on interactively refining an import then providing the code required to reproduce the import on new datasets.

For example, here’s the workflow we would use to import the Excel worksheet at http://www.fns.usda.gov/sites/default/files/pd/slsummar.xls.

First provide the dataset URL and review the import in preview mode (notice that this file contains two tables and as a result requires the first few rows to be removed):

We can clean this up by skipping 6 rows from this file and unchecking the “First Row as Names” checkbox:

The file is looking better but some columns are being displayed as strings when they are clearly numerical data. We can fix this by selecting “numeric” from the column drop-down:

The final step is to click “Import” to run the code displayed under “Code Preview” and import the data into R. The code is executed within the console and imported dataset is displayed automatically:

Note that rather than executing the import we could have just copied and pasted the import code and included it within any R script.

RStudio Release History

We started working on RStudio in November of 2008 (8 years ago!) and had our first public release in February of 2011. Here are highlights of the various releases through the years:

Version Date Highlights
0.92 Feb 2011
  • Initial public release
0.93 Apr 2011
  • Interactive plotting with manipulate
  • Source editor themes
  • Configurable workspace layout
0.94 Jun 2011
  • Enhanced plot export
  • Enhanced package installation and management
  • Enhanced history management
0.95 Jan 2012
  • RStudio project system
  • Code navigation (typeahead search, go to definition)
  • Version control integration (Git and Subversion)
0.96 May 2012
  • Enhanced authoring for Sweave
  • Web publishing with R Markdown
  • Code folding and many other editing enhancements
0.97 Oct 2012
  • Package development tools
  • Vim editing mode
  • More intelligent R auto-indentation
0.98 Dec 2013
  • Interactive debugging tools
  • Enhanced environment pane
  • Viewer pane for web content / htmlwidgets
0.98b Jun 2014
  • R Markdown v2 (publish to PDF, Word, and more)
  • Integrated tools for Shiny application development
  • Editor support for XML, SQL, Python, and Bash
0.99 May 2015
  • Data viewer with support for large datasets, filtering, searching, and sorting
  • Major enhancements to R and C/C++ code completion and inline code diagnostics
  • Multiple cursors, tab re-ordering, enhanced Vim mode
0.99b Feb 2016
  • Emacs editing mode
  • Multi-window source editing
  • Customizable keyboard shortcuts
  • RStudio Addins
1.0 Nov 2016
  • Authoring tools for R Notebooks
  • Integrated support for sparklyr (R interface to Spark)
  • Enhanced data import tools
  • Performance profiling via integration with profvis

The RStudio Release History page on our support website provides a complete history of all major and minor point releases.