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We’re very pleased to announce Shiny 0.8.0 (which actually went up on CRAN about two weeks ago). This release features a vastly better way to display tabular data, and new debugging tools that make it much easier to fix errors in your app.
We now support much more attractive and powerful displays of tabular data using the popular DataTables library. Our DataTables integration features pagination, searching/filtering, sorting, and more. Check out this demo to see it in action, and learn more about how to use it in your own apps by visiting the tutorial’s chapter on DataTables.
In version 0.8.0 of the Shiny package, we’ve greatly improved the set of debugging tools you can use with your Shiny apps. It’s now much easier to figure out what’s happening when things go wrong, thanks to two new features:
- Integration with the new visual debugger that’s available with RStudio v0.98. You can set breakpoints and step through your code much more easily than before.
- A new option ‘shiny.error’ which can take a function as an error handler. It is called when an error occurs in a reactive observer (e.g. when running an output rendering function). You can use options(shiny.error=traceback) to simply print a traceback, options(shiny.error=recover) for debugging from a regular R console, or options(shiny.error=browser) to jump into the RStudio visual debugger.
There have also been a few smaller tweaks and bug fixes. For the full list, you can take a look at our NEWS file.
Welcome, Yihui Xie!
If you’re reading this, there’s a good chance you have heard of Yihui Xie or have used his software; during his time as a PhD student at Iowa State University, he created the knitr, cranvas, and animation packages, among others.
We’re thrilled to announce that Yihui has joined the RStudio team! He will be one of the primary maintainers of the Shiny package and has already contributed some great improvements in the short time he has been with us.
RStudio maintains its own CRAN mirror, http://cran.rstudio.com. The server itself is a virtual machine run by Amazon’s EC2 service, and it syncs with the main CRAN mirror in Austria once per day. When you contact http://cran.rstudio.com, however, you’re probably not talking to our CRAN mirror directly. That’s because we use Amazon CloudFront, a content delivery network, which automatically distributes the content to locations all over the world. When you try to download a package from the Rstudio cloud mirror, it’ll be retrieved from a local CloudFront cache instead of the CRAN mirror itself. That means that, no matter where you are in the world, the data doesn’t need to travel very far, and so is fast to download.
To back this up with some data, we asked some friends to time downloads from all the CRAN mirrors. The RStudio mirror was not always the fastest (especially if you have a mirror nearby), but it was consistently fast around the world. (If you think you could improve on our testing methodology, the scripts and raw data are available at https://gist.github.com/hadley/5420147 – let us know what you come up with!)
You can use our mirror, even if you don’t use RStudio. (If you haven’t deliberately chosen a CRAN mirror in RStudio, we’ll use ours by default). It’s the first one in the list of mirrors (“0-Cloud”), or if you don’t want to select it every time you install a package, you can it as the default in your .Rprofile:
options(repos = c(CRAN = "http://cran.rstudio.com"))
Of course, speed isn’t the only factor you want to consider when choosing a mirror. Another important factor is reliability: is the mirror always available, and how often is it updated? CRAN provides the useful mirror monitoring report. Running a mirror is easy (it’s just a simple script run every few hours), so it’s a warning flag if a mirror has any non-green squares. We care about the availability of our mirror, and if it ever does go down, we’ll endeavour to fix it as quickly as possible.
Finally, because every download from a CRAN mirror is logged, CRAN mirrors provide a rich source of data about R and package usage. To date, it’s been hard to get access to this data. We wanted to change that, so you can now download our anonymised log data from cran-logs.rstudio.com. We’ve tried to strike a balance between utility and privacy. We’ve parsed the raw log data into fields that mean something to R users (like r version, architecture and os). The IP address is potentially revealing, so we’ve replaced it with a combination of country and a unique id within each day. This should make it possible to explore download patterns without undermining the privacy of the mirror users.
date time size r_version r_arch r_os date time size r_version r_arch r_os 1 2013-01-01 00:18:22 551371 2.15.2 x86_64 darwin9.8.0 2 2013-01-01 00:43:47 220277 2.15.2 x86_64 mingw32 3 2013-01-01 00:43:51 3505851 2.15.2 x86_64 mingw32 4 2013-01-01 00:43:53 761107 2.15.2 x86_64 mingw32 5 2013-01-01 00:31:15 187381 2.15.2 i686 linux-gnu 6 2013-01-01 00:59:46 2388932 2.15.2 x86_64 mingw32 package version country ip_id 1 knitr 0.9 RU 1 2 R.devices 2.1.3 US 2 3 PSCBS 0.30.0 US 2 4 R.oo 1.11.4 US 2 5 akima 0.5-8 US 3 6 spacetime 1.0-3 VN 4
Altogether, there’s currently around 150 megs of gzipped log files, representing over 7,000,000 package downloads. We’re looking forward to seeing what the R community does with this data, and we’ll highlight particularly interesting analyses in a future blog post. If you have any problems using the data, or you’d like to highlight a particularly interesting result, please feel free to email me.
Using the web logs collected when users download RStudio, we’ve prepared the following two maps showing where RStudio is being used, over the whole globe and just within the continental USA. Obviously this data is somewhat biased, as it reflects the number of downloads of RStudio, rather than the number of users of R (which we’d really love to know!). However, based on a month’s worth of data, we think the broad patterns are pretty interesting.
We made the maps by translating IP addresses to latitude and longitude with the free GeoIP databases provided by MaxMind. To make it easier to see the main patterns for each map, we used k-means clustering to group the original locations into 300 clusters for the world and 100 clusters for the US, then used ggplot2 to display the number of users in each cluster with the area of each bubble.
RStudio’s mission from the beginning has been to create powerful tools that support the practices and techniques required for creating trustworthy, high quality analysis. For many years Hadley Wickham has been teaching and working on his own set of tools for R with many of the same core goals. We’ve been collaborating quite a bit with Hadley over the past couple of years and today we’re excited to announce that Hadley, Winston Chang, and Garrett Grolemund are joining RStudio so we can continue to work together much more closely.
You probably know Hadley from his work on ggplot2, plyr, and many other packages. Garrett was a PhD student of Hadley’s at Rice, and you might also know him from the lubridate package, which makes dealing with dates and time easier; he’s also been working on new tools for visualisation and new ways of thinking about the process of data analysis. Winston has been working full-time on ggplot2 for the last couple of months, squashing many bugs and repaying a lot of the technical debt that’s accumulated over the years. Winston’s also writing an R Graphics Cookbook for O’Reilly that should be available in the near future.
What does this mean for RStudio? We’ll of course continue developing open-source software like the RStudio IDE, ggplot2, and plyr (among many other projects). One of Hadley’s core focuses at RStudio will also be expanding our mission to include education, which we plan to offer in a variety of formats ranging from in-person training to some innovative new online courses. We’ll also be working on hosted services (like RPubs) as well as some new products that address the challenges of deploying R within larger organizations.
We’re all excited to begin this next phase of work together and will have lots more details to announce later this fall!
Today we’re very excited to announce RPubs, a free service that makes it easy to publish documents to the web from R. RPubs is a quick and easy way to disseminate data analysis and R code and do ad-hoc collaboration with peers.
RPubs documents are based on R Markdown, a new feature of knitr 0.5 and RStudio 0.96. To publish to RPubs within RStudio, you simply create an R Markdown document then click the Publish button within the HTML Preview window:
RPubs documents include a moderated comment stream for feedback and dialog with readers, and can be updated with changes by publishing again from within RStudio.
Note that you’ll only see the Publish button if you update to the latest version of RStudio (v0.96.230, available for download today).
The markdown package
RStudio has integrated support for working with R Markdown and publishing to RPubs, but we also want to make sure that no matter what tools you use it’s still possible to get the same results. To that end we’ve also been working on a new version of the markdown package (v0.5, available now on CRAN).
The markdown package provides a standalone implementation of R Markdown rendering that can be integrated with other editors and IDEs. The package includes a function to upload to RPubs, but is also flexible enough to support lots of other web publishing scenarios. We’ve been working with Jeff Horner on this and he has a more detailed write-up on the capabilities of the markdown package on his blog.
Gallery of examples
We’ve also published a gallery of example documents on RPubs—the gallery illustrates some of the most useful techniques for getting the most out of R Markdown, and includes the following articles:
- MathJax and Writing Equations
- Dynamic Graphics with the googleVis Package
- Customizing Chunk Options
- Caching Code Chunks
Let us know what additional examples you’d like to see—we’ll be adding more in the weeks ahead.
We’ve just a made a change to the syntax for embedding MathJax equations in R Markdown documents. The change was made to eliminate some parsing ambiguities and to support future extensibility to additional formats.
The revised syntax adds a “latex” qualifier to the $ or $$ equation begin delimiter. It looks like this:
This change was the result of a few considerations:
- Some users encountered situations where the $<equation>$ syntax recognized standard text as an equation. There was an escape sequence (\$) to avoid this but for users not explicitly aware of MathJax semantics this was too hard to discover.
- The requirement to have no space between equation delimiters ($) and the equation body (intended to reduce parsing ambiguity) was also confusing for users.
- We want to eventually support ASCIIMath and for this will require an additional qualifier to indicate the equation format.
RStudio v0.96.227 implements the new MathJax syntax and is available for download now.
There’s been lots of excitement about the new R Markdown feature introduced as part of knitr 0.5 and RStudio 0.96. People see R Markdown as both a simpler way to do reproducible research and as a great way to publish to the web from R. Jeromy Anglim has a nice write up on getting started with R Markdown and Marcus Gesmann describes how to embed Google Visualizations using his googleVis package.
We are just as excited about R Markdown and think there is lots more that can be done with it. We’ll be talking about this along with Yihui Xie (author of knitr) and Jeff Horner (author of R/Apache and Rook) on Tuesday June 5th in New York:
At the meetup we’ll be showing the latest versions of knitr and RStudio and will be announcing some new R Markdown stuff—if you are in New York we’d love to see you there!
We started the RStudio project because we were excited and inspired by R. The creators of R provided a flexible and powerful foundation for statistical computing; then made it free and open so that it could be improved collaboratively and its benefits could be shared by the widest possible audience.
It’s better for everyone if the tools used for research and science are free and open. Reproducibility, widespread sharing of knowledge and techniques, and the leveling of the playing field by eliminating cost barriers are but a few of the shared benefits of free software in science.
RStudio is an integrated development environment (IDE) for R which works with the standard version of R available from CRAN. Like R, RStudio is available under a free software license. Our goal is to develop a powerful tool that supports the practices and techniques required for creating trustworthy, high quality analysis. At the same time, we want RStudio to be as straightforward and intuitive as possible to provide a friendly environment for new and experienced R users alike. RStudio is also a company, and we plan to sell services (support, training, consulting, hosting) related to the open-source software we distribute.
We’re looking forward to joining the R community, learning from users, growing the product, and hopefully making a meaningful contribution to the practice of research and science.
Welcome to the RStudio weblog! We’ll use the weblog to talk about both the product and its features as well as broader issues that concern the R community.