We previously discussed improved support in RStudio v1.2 for SQL, D3, Python, and C/C++. Today, we’re excited to announce improved support for the Stan programming language. The Stan programming language makes it possible for researchers and analysts to write high-performance and scalable statistical models. Stan® is a state-of-the-art platform for statistical modeling and high-performance statistical computation. Thousands of users rely on Stan for statistical modeling, data analysis, and prediction in the social, biological, and physical sciences, engineering, and business. Read more →

We’ve now discussed the improved support in RStudio v1.2 for SQL, D3, and Python. Today, we’ll talk about IDE support for C/C++ and Rcpp. The IDE has had excellent support for C/C++ since RStudio v0.99, including: Tight integration with the Rcpp package Code completion Source diagnostics as you edit Code snippets Auto-indentation Navigable list of compilation errors Code navigation (go to definition) The major new C/C++ feature in RStudio v1. Read more →

One of the primary focuses of RStudio v1.2 is improved support for other languages frequently used with R. Last week on the blog we talked about new features for working with SQL and D3. Today we’re taking a look at enhancements we’ve made around the reticulate package (an R interface to Python). The reticulate package makes it possible to embed a Python session within an R process, allowing you to import Python modules and call their functions directly from R. Read more →

As part our series on new features in the RStudio v1.2 Preview Release, we’re pleased to announce the r2d3 package, a suite of tools for using custom D3 visualizations with R. RStudio v1.2 includes several features to help optimize your development experience with r2d3. We’ll describe these features below, but first a bit more about the package. Features of r2d3 include: Translating R objects into D3 friendly data structures Read more →

The RStudio 1.2 Preview Release, available today, dramatically improves support and interoperability with many new programming languages and platforms, including SQL, D3, Python, Stan, and C++. Over the next few weeks on the blog, we’re going to be taking a look at improvements for each of these in turn. Today, we’re looking at SQL, and as a motivating example, we’re going to connect to a sample Chinook database to get a list of album titles. Read more →

Today we are excited to share that a new release of sparklyr is available on CRAN! This 0.9 release enables you to: Create Spark structured streams to process real time data from many data sources using dplyr, SQL, pipelines, and arbitrary R code. Monitor connection progress with upcoming RStudio Preview 1.2 features and support for properly interrupting Spark jobs from R. Use Kubernetes clusters with sparklyr to simplify deployment and maintenance. Read more →

RStudio Connect 1.6.8 includes additions to custom emails, new user endpoints in the RStudio Connect Server API, support for content descriptions and title changes, and important security and authentication improvements. Updates R Markdown Reports have access to environment variables containing metadata about the report on RStudio Connect. This addition is especially important for custom emails. In case you missed it, recent versions of RStudio Connect allow data scientists to distribute beautiful emails that can include plots, tables, and dynamically generated text. Read more →

Today we’re excited to announce Radix, a new R Markdown format optimized for scientific and technical communication. Features of Radix include: Reader-friendly typography that adapts well to mobile devices. Flexible figure layout options (e.g. displaying figures at a larger width than the article text). Tools for making articles easily citeable, as well as for generating Google Scholar compatible citation metadata. The ability to incorporate JavaScript and D3-based interactive visualizations. Read more →

rstudio::conf, the conference on all things R and RStudio, will take place January 17 and 18, 2019 in Austin, Texas, preceded by Training Days on January 15 and 16. We’ve received requests from a number of you for permission to submit talk/e-poster abstracts after the deadline (this Saturday, September 15). In response, we’re extending the deadline by a week for everyone; the new submission deadline is September 22, a week from Saturday. Read more →

There are good reasons to get into deep learning: Deep learning has been outperforming the respective “classical” techniques in areas like image recognition and natural language processing for a while now, and it has the potential to bring interesting insights even to the analysis of tabular data. For many R users interested in deep learning, the hurdle is not so much the mathematical prerequisites (as many have a background in statistics or empirical sciences), but rather how to get started in an efficient way. Read more →