2018-03-29
Yihui Xie
It has been more than two years since we announced the initial version of the DT package. Today we want to highlight a few significant changes and new features in the recent releases v0.3 and v0.4. The full changes can be found in the release notes.
Editable tables Now you can make a table editable through the new argument datatable(..., editable = TRUE). Then you will be able to edit a cell by double-clicking on it.
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2018-03-26
JJ Allaire
We are pleased to announce the reticulate package, a comprehensive set of tools for interoperability between Python and R. The package includes facilities for:
Calling Python from R in a variety of ways including R Markdown, sourcing Python scripts, importing Python modules, and using Python interactively within an R session.
Translation between R and Python objects (for example, between R and Pandas data frames, or between R matrices and NumPy arrays).
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2018-02-06
Tareef Kawaf
Over the past year we’ve been hard at work on creating R interfaces to TensorFlow, an open-source machine learning framework from Google. We are excited about TensorFlow for many reasons, not the least of which is its state-of-the-art infrastructure for deep learning applications.
In the 2 years since it was initially open-sourced by Google, TensorFlow has rapidly become the framework of choice for both machine learning practitioners and researchers. On Saturday, we formally announced our work on TensorFlow during J.
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2018-01-29
Kevin Kuo
We are excited to share that sparklyr 0.7 is now available on CRAN! Sparklyr provides an R interface to Apache Spark. It supports dplyr syntax for working with Spark DataFrames and exposes the full range of machine learning algorithms available in Spark. You can also learn more about Apache Spark and sparklyr in spark.rstudio.com and our new webinar series on Apache Spark. Features in this release:
Adds support for ML Pipelines which provide a uniform set of high-level APIs to help create, tune, and deploy machine learning pipelines at scale.
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2017-11-17
Bárbara Borges
The pool package makes it easier for Shiny developers to connect to databases. Up until now, there wasn’t a clearly good way to do this. As a Shiny app author, if you connect to a database globally (outside of the server function), your connection won’t be robust because all sessions would share that connection (which could leave most users hanging when one of them is using it, or even all of them if the connection breaks).
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2017-09-11
Yihui Xie
Today I’m excited to announce a new R package, blogdown, to help you create general-purpose (static) websites with R Markdown. The first version of blogdown is available on CRAN now, and you can install it with:
install.packages("blogdown") The source package is hosted on Github in the repository rstudio/blogdown. Since blogdown is a new package, you may install and test the development version using devtools::install_github("rstudio/blogdown") if you run into problems with the CRAN version.
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2017-09-05
JJ Allaire
We are excited to announce that the keras package is now available on CRAN. The package provides an R interface to Keras, a high-level neural networks API developed with a focus on enabling fast experimentation. Keras has the following key features:
Allows the same code to run on CPU or on GPU, seamlessly.
User-friendly API which makes it easy to quickly prototype deep learning models.
Built-in support for convolutional networks (for computer vision), recurrent networks (for sequence processing), and any combination of both.
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2017-08-15
Winston Chang
Shiny 1.0.4 is now available on CRAN. To install it, run:
install.packages("shiny") For most Shiny users, the most exciting news is that file inputs now support dragging and dropping:
It is now possible to add and remove tabs from a tabPanel, with the new functions insertTab(), appendTab(), prependTab(), and removeTab(). It is also possible to hide and show tabs with hideTab() and showTab().
Shiny also has a new a function, onStop(), which registers a callback function that will execute when the application exits.
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2017-08-10
Roger Oberg
Have you embraced the tidyverse? Do you now want to expand it to meet your needs? Then this is a NEW two-day hands on workshop designed for you! The goal of this workshop is to take you from someone who uses tidyverse functions to someone who can extend the tidyverse by:
Writing expressive code using advanced functional programming techniques Designs consistent APIs using analogies to existing tools Uses the S3 object system to make user friendly values Can bundle functions with documentation and tests into a package to share with others.
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2017-07-31
Javier Luraschi
We’re excited to announce a new release of the sparklyr package, available in CRAN today! sparklyr 0.6 introduces new features to:
Distribute R computations using spark_apply() to execute arbitrary R code across your Spark cluster. You can now use all of your favorite R packages and functions in a distributed context. Connect to External Data Sources using spark_read_source(), spark_write_source(), spark_read_jdbc() and spark_write_jdbc(). Use the Latest Frameworks including dplyr 0.
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