We’ve redesigned our training pages to make it even easier for you to learn R or Shiny. Visit our new training web page, www.rstudio.com/training, to see:

  • A curated list of free materials for learning R. We think that these are some of the most helpful resources on the web. They would make an effective starting place if you want to improve your R skills.
  • Announcements for upcoming RStudio public workshops, like the Introduction to R course that we’re holding on April 28 & 29 in San Francisco.
  • A database of well known R instructors, who can provide on-site — as well as online — R training.
  • Links to the new Shiny Dev Center, which includes articles, examples, and a tutorial, all designed to help you master Shiny.
  • Links to the preview sites for R Markdown, an easy option for writing reproducible reports with R, and ggvis, an R package that creates interactive plots with the grammar of graphics.
  • Links to books that we have written (or are writing) about R and its tools.

Why are we so excited about training? We think that learning R and Shiny is the best investment that a data user can make. These two free tools can streamline how you analyze data and deliver results. Browse through the links at www.rstudio.com/training and see for yourself.

 

 

devtools 1.5 is now available on CRAN. It includes four new functions to make it easier to add useful infrastructure to packages:

  • add_test_infrastructure() will create testthat infrastructure when needed.

  • add_rstudio_project() adds an Rstudio project file to your package.

  • add_travis() adds a basic template for travis-ci.

  • add_build_ignore() makes it easy to add files to .Rbuildignore,
    escaping special characters as needed.

We’ve also bumped two dependencies: devtools now requires R 3.0.2 and roxygen2 3.0.0. We’ve also included many minor improvements and bug fixes, particularly for package installation. For example install_github() now prefers the safer github personal access token, and does a better job of installing the dependencies that you actually need. We also provide versions of help(), ? and system.file() that work with all packages, regardless of how they’re loaded. See a complete list of changes in the full release notes.

Please join us for our popular Introduction to R course for data scientists and data analysts in San Francisco on April 28 and 29.  This is a two-day workshop, designed to provide a comprehensive introduction to R that will have you analyzing and modeling data with R in no time. We will cover practical skills for visualizing, transforming, and modeling data in R. You will learn how to explore and understand data as well as how to build linear and non-linear models in R.

The course will be led by RStudio Master Instructor and author Dr. Garrett Grolemund.

We offer introductory R training only a few times a year. The Boston course in January sold out quickly. Space is limited. We encourage you to register (rstudio-sfbay.eventbrite.com) as soon as you can.

“The instructor was amazing. He knew so much and could answer any questions. His expertise was obvious and he was also very clear about how to explain it to a varied audience.” – Workshop Student, January 2014

“Very well organized and at a good pace. The example datasets were very helpful. Excellent teachers!” – Workshop Student, January 2014

We’re excited to introduce to you our new website for Shiny: shiny.rstudio.com!

shiny-rstudio-com

We’ve included articles on many Shiny-related topics, dozens of example applications, and an all-new tutorial for getting started.

Whether you’re a beginner or expert at Shiny, we hope that having these resources available in one place will help you find the information you need.

We’d also like to announce Shiny 0.9, now available on CRAN. This release includes many bug fixes and new features, including:

New application layout options

Until now, the vast majority of Shiny apps have used a sidebar-style layout. Shiny 0.9 introduces new layout features to:

  1. Make it easy to create custom page layouts using the Bootstrap grid system. See our new application layout guide or a live example.
  2. Provide navigation bars and lists for separating your application into different pages. See navbarPage and navlistPanel, and this example.
  3. Enhance tabsetPanel to allow pill-style tabs, and to let tabs be placed above, below, or to either side of tab content.
  4. Create floating panels and place them relative to the sides of the page, optionally making them draggable. See absolutePanel or this example.
  5. Use Bootstrap themes to easily modify the fonts and colors of your application. Example

You can see many of these features in action together in our reimplementation of the Washington Post’s interactive article on Super Zips.

Selectize.js integration

The JavaScript library selectize.js provides a much more flexible interface compared to the basic select input. It allows you to type and search in the options, use placeholders, control the number of options/items to show/select, and so on.

selectize

We have integrated selectize.js in shiny 0.9, and selectInput now creates selectize inputs by default. (You can revert back to plain select inputs by passing selectize=FALSE to selectInput.) For more advanced uses, we have included a new selectizeInput function that lets you pass options to selectize.

Please check out this example to see a subset of features of the selectize input. There is also an example comparing the select and selectize input.

Showcase mode

Shiny apps can now (optionally) run in a “showcase” mode in which the app’s R code can be automatically displayed within the app. Most of the Shiny example apps in our new gallery use showcase mode.

Showcase example

As you interact with the application, reactive expressions and outputs in server.R will light up as they execute. This can be helpful in visualizing the reactivity in your app.

See this article to learn more.

As always, you can install the latest release of Shiny by running this command at the R console:

install.packages("shiny")

The complete list of bug fixes and features is available in the NEWS file.

We hope you’ll find these new features helpful in exploring and understanding your data!

We’re very pleased to announce the release of httr 0.3. httr makes it
easy to work with modern web apis so that you can work with web data
almost as easily as local data. For example, this code shows how might
find the most recently asked question about R on stackoverflow:

# install.packages("httr")
library(httr)

# Find the most recent R questions on stackoverflow
r <- GET(
  "http://api.stackexchange.com",
  path = "questions",
  query = list(
    site = "stackoverflow.com",
    tagged = "r"
  )
)

# Check the request succeeded
stop_for_status(r)

# Automatically parse the json output
questions <- content(r)
questions$items[[1]]$title
#> [1] "Remove NAs from data frame without deleting entire rows/columns"

httr 0.3 recieved a major overhaul to OAuth support. OAuth is a modern
standard for authentication used when you want to allow a service (i.e R
package) access to your account on a website. This version of httr
provides an improved initial authentication experience and supports
caching so that you only need to authenticate once per project. A big
thanks goes to Craig Citro (Google) who contributed a lot of code and
ideas to make this possible.

httr 0.3 also includes many other bug fixes and minor improvements. You
can read about these in the github release notes.

dplyr 0.1.3 is now on CRAN. It fixes an incompatibility with the latest version of Rcpp, and a number of other bugs that were causing dplyr to crash R. See the full details in the release notes.

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").

We’re pleased to announce a new minor version of dplyr. This fixes a few bugs that crashed R, adds a few minor new features (like a sort argument to tally()), and uses shallow copying in a few more places. There is one backward incompatible change: explain_tbl() has been renamed to explain. For a complete list of changes, please see the github release notice.

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

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