It’s been an exciting week for me! On Monday, we finally took the wraps off the product that I’ve been working on since leaving Microsoft 18 months ago.
First, a little background: There’s a programming language called R that is taking the world of statistical computing and data analysis by storm. If you’ve never heard of R, you can read more about it in this New York Times article. But the gist of it is:
“R is really important to the point that it’s hard to overvalue it,” said Daryl Pregibon, a research scientist at Google, which uses the software widely. “It allows statisticians to do very intricate and complicated analyses without knowing the blood and guts of computing systems.”
We designed RStudio from the ground up to make working with R easier and more productive. It brings your R console, source code, plots, help, history, and workspace browser into one cohesive package. We’ve added some neat productivity features like a searchable endless command history, function/symbol completion, data import dialog with preview, one-click Sweave compile, and more. Many experienced R users have told us that even at this early stage, RStudio is the R IDE that works best for them.
What’s really unique about RStudio, though, is that it can run equally well as a desktop program or as a web application.
What does it mean to run an IDE as a web application? Take a look: Here’s a screenshot of RStudio running in Google Chrome on Windows 7. As you can see, it’s a full IDE—it just happens to be in a browser.
The server I’m using is an 8-proc Ubuntu server with 68.4GB of RAM (it happens to be on EC2). When you run RStudio this way, it’s always the server’s computing resources—CPU, RAM, hard drive—that are being used by R.
Why is this interesting? It turns out that a lot of serious R users share a big, powerful Linux box or cluster with their colleagues. They write their R code on their own desktop or laptop, and execute it on the big server. There’s definitely a lot of friction in this workflow. With RStudio, a lot of the friction goes away because you can do your exploring and iterating directly on the big iron.
If you’re an R user, try it out and let us know what you think!
Filed under: Career, RStudio | 12 Comments