packages

plumber 1.1.0

2021-03-29 Barret Schloerke and Carson Sievert
Thumbnail hex sticker
{plumber} v1.1.0 is now on CRAN! In this post, we'll highlight some of the most exciting new features in both the v1.1.0 and v1.0.0 releases, including: parallel endpoint execution, a tidy interface, and request body parsing Read more →

Painful Package Management

2021-02-11 Alex K Gold
Thumbnail thumbnail
Data science teams are often frustrated by poorly-designed or nonexistent approaches to R and Python package management. In this post, you'll learn specifically how that pain shows up for data scientists and how to identify your organization's requirements for a better package management plan. Read more →

The Package Management Prime Directive

2021-02-05 Alex K Gold
Thumbnail thumbnail
Data Scientists and the IT Admins or DevOps engineers who support these platforms often face "package management" problems. Understanding and following the package management prime directive can drastically ease your organization's headaches. Read more →

Shiny 1.6

2021-02-01 Carson Sievert, Winston Chang, and Barret Schloerke
Thumbnail thumbnail.jpg
Shiny 1.6 is now on CRAN! This release includes significant improvements to theming, caching, accessibility, and more. Read more →

Announcing blogdown v1.0

2021-01-18 Alison Hill, Christophe Dervieux, Yihui Xie
Thumbnail thumbnail.jpg
The blogdown package is now on CRAN. Read on for highlights from the version 1.0 release, including smoother workflows, new checking functions to guide you into the pit of success, the ability to pin Hugo versions, better organization of content files via page bundles, and the new Markdown mode for R Markdown posts. Read more →

Latest News from the R Markdown Family

2020-12-21 Alison Hill, Christophe Dervieux, Yihui Xie
Thumbnail thumbnail.jpg
An end-of-2020 round-up of all the latest news from the R Markdown family of packages so that you know all you need to know to take advantage of the newest features. Read more →

Introducing torch for R

2020-09-29 The RStudio Multiverse Team
Thumbnail
As of this writing, two deep learning frameworks are widely used in the Python community: TensorFlow and PyTorch. TensorFlow, together with its high-level API Keras, has been usable from R since 2017, via the tensorflow and keras packages. Today, we are thrilled to announce that now, you can use Torch natively from R! This post addresses three questions: What is deep learning, and why might I care? What’s the difference between torch and tensorflow? Read more →

sparklyr 1.3: Higher-order Functions, Avro and Custom Serializers

2020-07-16 Yitao Li
Thumbnail
Sparklyr 1.3 is now available, featuring integration of Spark higher-order functions, and data import/export in Avro and in user-defined serialization formats. Read more →

sparklyr 1.2: Foreach, Spark 3.0 and Databricks Connect

2020-05-06 Yitao Li
Thumbnail
A new version of sparklyr is now available on CRAN! In this sparklyr 1.2 release, the following new improvements have emerged into spotlight: A registerDoSpark() method to create a foreach parallel backend powered by Spark that enables hundreds of existing R packages to run in Spark. Support for Databricks Connect, allowing sparklyr to connect to remote Databricks clusters. Improved support for Spark structures when collecting and querying their nested attributes with dplyr. Read more →

Wrangling Unruly Data: The Bane of Every Data Science Team

2020-05-05 Carl Howe
Thumbnail data-wrangling
There’s an old saying (at least old in data scientist years) that goes, “90% of data science is data wrangling.” This rings particularly true for data science leaders, who watch their data scientists spend days painstakingly picking apart ossified corporate datasets or arcane Excel spreadsheets. Does data science really have to be this hard? And why can’t they just delegate the job to someone else? Data Is More Than Just Numbers The reason that data wrangling is so difficult is that data is more than text and numbers. Read more →