2019-04-26
Joe Cheng
We’re excited to announce the release of Shiny v1.3.2. This release has two main features: a new reactivity debugging tool we call reactlog, and much faster serving of static file assets.
Introducing reactlog: Visually debug your reactivity issues Debugging faulty reactive logic can be challenging, as we’ve written and talked about in the past. In particular, some of the most difficult Shiny app bugs to track down are when reactive expressions and observers re-execute either too often (i.
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2019-03-15
Javier Luraschi
With much excitement built over the past three years, we are thrilled to share that sparklyr 1.0 is now available on CRAN!
The sparklyr package provides an R interface to Apache Spark. It supports dplyr, MLlib, streaming, extensions and many other features; however, this particular release enables the following new features:
Arrow enables faster and larger data transfers between Spark and R. XGBoost enables training gradient boosting models over distributed datasets.
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2019-03-08
Roger Oberg
Join RStudio Chief Data Scientist Hadley Wickham for his popular “Building tidy tools” workshop in Sydney, Australia! If you’d missed the sold out course at rstudio::conf 2019 now is your chance.
Register here: https://www.rstudio.com/workshops/building-tidy-tools/
You should take this class if you have some experience programming in R and you want to learn how to tackle larger-scale problems. You’ll get the most if you’re already familiar with the basics of functions (i.
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2018-11-13
Joe Cheng
We’re pleased to announce the CRAN release of Shiny v1.2.0! This release features Plot Caching, an important new tool for improving performance and scalability in Shiny apps.
If you’re not familiar with the term “caching”, it just means that when we perform a time-consuming operation, we save (cache) the results so that the next time that operation is requested, we can skip the actual operation and instantly fetch the previously cached results.
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2018-10-18
RStudio Team
Continuing our series on new features in the RStudio v1.2 preview release, we would like to introduce shinytest. shinytest is a package to perform automated testing for Shiny apps, which allows us to:
Record Shiny tests with ease.
Run and troubleshoot Shiny tests.
shinytest is available on CRAN, supported in RStudio v1.2 preview and can be installed as follows:
install.packages("shinytest") Recording Tests This is the general procedure for recording tests:
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2018-10-09
RStudio Team
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.
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2018-10-05
RStudio Team
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
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2018-10-01
Javier Luraschi
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.
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2018-09-12
Sigrid Keydana
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.
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2018-06-26
Joe Cheng
This is a significant release for Shiny, with a major new feature that was nearly a year in the making: support for asynchronous operations!
Without this capability, when Shiny performs long-running calculations or tasks on behalf of one user, it stalls progress for all other Shiny users that are connected to the same process. Therefore, Shiny apps that feature long-running calculations or tasks have generally been deployed using many R processes, each serving a small number of users; this works, but is not the most efficient approach.
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