Packages

Getting started with deep learning in R

2018-09-12 Sigrid Keydana
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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 →

Shiny 1.1.0: Scaling Shiny with async

2018-06-26 Joe Cheng
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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. Read more →

Applied Machine Learning Workshop

2018-05-15 Roger Oberg
Join Max Kuhn of RStudio for his popular Applied Machine Learning Workshop in Washington D.C.! If you’d missed his sold out course at rstudio::conf 2018 now is your chance. Register here: https://www.rstudio.com/workshops/applied-machine-learning/ This two-day course will provide an overview of using R for supervised learning. The session will step through the process of building, visualizing, testing, and comparing models that are focused on prediction. The goal of the course is to provide a thorough workflow in R that can be used with many different regression or classification techniques. Read more →

sparklyr 0.8: Production pipelines and graphs

2018-05-14 Kevin Kuo
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We’re pleased to announce that sparklyr 0.8 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 ML. You can also learn more about Apache Spark and sparklyr at spark.rstudio.com and the sparklyr webinar series. In this version, we added support for Spark 2.3, Livy 0. Read more →

leaflet 2.0.0

2018-05-10 Barret Schloerke
leaflet 2.0.0 is now on CRAN! The leaflet R package wraps the Leaflet.js JavaScript library, and this release of the R package marks a major upgrade from the outdated Leaflet.js 0.7.x to the current Leaflet.js 1.x (specifically, 1.3.1). Leaflet.js 1.x includes some non-backward-compatible API changes versus 0.7.x. If you’re using only R code to create your Leaflet maps, these changes should not affect you. If you are using custom JavaScript, some changes may be required to your code. Read more →

Building tidy tools workshop

2018-04-09 Roger Oberg
Join RStudio Chief Data Scientist Hadley Wickham for his popular Building tidy tools workshop in San Francisco! If you’d missed the sold out course at rstudio::conf 2018 now is your chance. Register here: https://www.rstudio.com/workshops/extending-the-tidyverse/ 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. Read more →

DT 0.4: Editing Tables, Smart Filtering, and More

2018-03-29 Yihui Xie
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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. Read more →

reticulate: R interface to Python

2018-03-26 JJ Allaire
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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). Read more →

TensorFlow for R

2018-02-06 Tareef Kawaf
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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. Read more →

sparklyr 0.7: Spark Pipelines and Machine Learning

2018-01-29 Kevin Kuo
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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. Read more →