Keras for R

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. Read more →

Packages

Shiny 1.0.4

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. Read more →

Packages

Building tidy tools workshop

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. Read more →

Packages

sparklyr 0.6: Distributed R and external sources

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. Read more →

tidyverse

haven 1.1.0

2017-07-13 Hadley Wickham

I’m pleased to announce the release of haven 1.1.0. Haven is designed to faciliate the transfer of data between R and SAS, SPSS, and Stata. It makes it easy to read SAS, SPSS, and Stata file formats in to R data frames, and makes it easy to save your R data frames in to SPSS and Stata if you need to collaborate with others using closed source statistical software. Install the latest version by running: Read more →

Packages

Introducing learnr

2017-07-11 Garrett Grolemund

We’re pleased to introduce the learnr package, now available on CRAN. The learnr package makes it easy to turn any R Markdown document into an interactive tutorial. Tutorials consist of content along with interactive components for checking and reinforcing understanding. Tutorials can include any or all of the following: Narrative, figures, illustrations, and equations. Code exercises (R code chunks that users can edit and execute directly). Multiple choice quizzes. Read more →

Packages

dbplyr 1.1.0

2017-06-27 Hadley Wickham

I’m pleased to announce the release of the dbplyr package, which now contains all dplyr code related to connecting to databases. This shouldn’t affect you-as-a-user much, but it makes dplyr simpler, and makes it easier to release improvements just for database related code. You can install the latest version of dbplyr with: install.packages("dbplyr") DBI and dplyr alignment The biggest change in this release is that dplyr/dbplyr works much more directly with DBI database connections. Read more →

Packages

bigrquery 0.4.0

2017-06-26 Hadley Wickham

I’m pleased to announce that bigrquery 0.4.0 is now on CRAN. bigrquery makes it possible to talk to Google’s BigQuery cloud database. It provides both DBI and dplyr backends so you can interact with BigQuery using either low-level SQL or high-level dplyr verbs. Install the latest version of bigrquery with: install.packages("bigrquery") Basic usage Connect to a bigquery database using DBI: library(dplyr) con <- DBI::dbConnect(dbi_driver(), project = "publicdata", dataset = "samples", billing = "887175176791" ) DBI::dbListTables(con) #> [1] "github_nested" "github_timeline" "gsod" "natality" #> [5] "shakespeare" "trigrams" "wikipedia" (You’ll be prompted to authenticate interactively, or you can use a service token with set_service_token(). Read more →

Packages

dplyr 0.7.0

2017-06-13 Hadley Wickham

I’m pleased to announce that dplyr 0.7.0 is now on CRAN! (This was dplyr 0.6.0 previously; more on that below.) dplyr provides a “grammar” of data transformation, making it easy and elegant to solve the most common data manipulation challenges. dplyr supports multiple backends: as well as in-memory data frames, you can also use it with remote SQL databases. If you haven’t heard of dplyr before, the best place to start is the Data transformation chapter in R for Data Science. Read more →

Packages

shinydashboard 0.6.0

2017-05-18 Bárbara Borges

Shinydashboard 0.6.0 is now on CRAN! This release of shinydashboard was aimed at both fixing bugs and also bringing the package up to speed with users’ requests and Shiny itself (especially fully bringing bookmarkable state to shinydashboard’s sidebar). In addition to bug fixes and new features, we also added a new “Behavior” section to the shinydashboard website to explain this release’s two biggest new features, and also to provide users with more material about shinydashboard-specific behavior. Read more →