You are currently browsing the monthly archive for February 2014.

We are excited to announce the general availability of RStudio Shiny Server Pro.

Shiny Server Pro is the simplest way for data scientists and R users in the enterprise to share their work with colleagues. With Shiny Server Pro you can:

  • Secure access to Shiny applications with authentication systems such as LDAP and Active Directory
  • Configure a Shiny application to use more than one process
  • Control the number of concurrent users per application
  • Gain insight into your applications’ CPU and memory use
  • Get help directly from our team at RStudio

If you’re interested in finding out more, download a free 45 day evaluation here.

We’re pleased to announce a new major version of testthat. Version 0.8 comes with a new recommended structure for storing your tests. To better meet CRAN recommended practices, we now recommend that tests live in tests/testthat, instead of inst/tests. This makes it possible for users to choose whether or not to install tests. With this new structure, you’ll need to use test_check() instead of test_packages() in the test file (usually tests/testthat.R) that runs all testthat unit tests.

Another big improvement comes from Karl Forner. He contributed code which provides line numbers in test errors so you can see exactly where the problems are. There are also four new expectations (expect_null(), expected_named(), expect_more_than(), expect_less_than()) and many other minor improvements and bug fixes. For a complete list of changes, please see the github release. After release of 0.8 to CRAN, we discovered two small bugs. These were fixed in 0.8.1.

As always, you can install the latest version with install.packages("testthat").

We’re pleased to announce a new minor version of dplyr. This fixes a number of bugs that crashed R, and considerably improves the functionality of select(). You can now use named arguments to rename existing variables, and use new functions starts_with(), ends_with()contains(),  matches() and num_range() to select variables based on their names. Finally, select() now makes a shallow copy, substantially reducing its memory impact. I’ve also added the summarize() alias for people from countries who don’t spell correctly 😉

For a complete list of changes, please see the github release, and as always, you can install the latest version with install.packages("dplyr").