Aug 30
6 min

rstudio::glimpse() Newsletter

Thumbnail The rstudio glimpse logo, made up of the RStudio logo, two colons, and glimpse as an R function, on a background with small drawings of normal distributions, cats, pipes, the R symbol, and other R-related icons.
Tracy Teal is the Open Source Program Director at RStudio.

This is our second rstudio::glimpse() newsletter. If you're reading this on the blog, you can subscribe here to receive this newsletter in your inbox.

When I look back on rstudio::conf(2022), I wish that there were five of me that could have attended: One to watch and learn from all the talks on the incredible work people are doing and sharing to advance data science, education, and community; another for the ‘hallway track’ to meet and talk with people and hear what everyone is excited about, and the things we’re struggling with too; another to participate in the online community talking with and connecting with people all over the world; another to meet amazing co-workers, many for the first time; and finally one to just watch and listen and feel a part of this special community. Luckily the learning, sharing, and connecting don’t end with the conference, and I’m enjoying the opportunity to watch talk recordings.

Let me know if there are other things you’d like to see in the newsletter and follow along at @rstudio_glimpse too!

Roundup

  • RStudio is becoming Posit in October! In J.J.Allaire and Hadley Wickham’s blog post and rstudio::conf(2022) keynote, they share our mission and why Posit is the right name for this new phase in RStudio’s development. Our mission is the creation of free and open-source software for data science, scientific research, and technical communication. This mission intentionally goes beyond “R for Data Science”— with Posit, we hope to take the approach that’s succeeded with R and apply it more broadly. But no, Hadley will not be learning Python, and the hex stickers for packages aren’t going anywhere!
  • Announcing Quarto, a new open source scientific and technical publishing system). Quarto is the next iteration of R Markdown, and allows you can create dynamic content with Python, R, Julia, and Observable, author documents as plain text markdown or Jupyter notebooks, and output to multiple format types.Get started learning about Quarto through the tutorials, Tom Mock’s ‘Welcome to Quarto’ workshop, and Mine Çetinkaya-Rundel’s Quarto tip a day. See more in the Hello, Quarto keynote from Mine and Julia Stewart Lowndes and all the talks from rstudio::conf(2022) on Quarto!
  • New developments in Shiny. Shiny is a framework for building interactive web applications without knowing CSS, HTML, and Javascript. At rstudio::conf(2022) came the alpha release of a visual Shiny UI editor, Shiny for Python, Shiny without a server, and a new look for Shiny’s UI. These are early stages of development, so please try and give plenty of feedback! Joe Cheng shared in his keynote a personal perspective on the history and journey of Shiny. Happy 10-year birthday to Shiny, and it’s so exciting what’s next!
  • Learn about tidymodels 1.0.0. The tidymodels framework is a collection of R packages for modeling and machine learning using tidyverse principles that makes modeling ergonomic, effective, and safe. Learn more about ‘why tidymodels’ in Max Kuhn and Julia Silge’s rstudio::conf(2022) keynote, how to get started from the tidymodels workshop materials, and their 1.0 release updates in their roundup post.
  • Missed some of rstudio::conf(2022) or want to rewatch your favorite talks? Recordings of all the keynotes and talks are now available, along with workshop materials. Find them all through the talk and workshop wrap-up.

Learn. Teach. Share.

Selected new releases

gt v0.7.0
With the {gt} package, anyone can make wonderful-looking tables using the R programming language. There are lots of new features in this release, including the ability to export gt tables as Word documents, easier production of colorful and stylish tables, and many accessibility enhancements for HTML table outputs.

knitr 1.40
This release of {knitr} includes the addition of a new “graphics device” that allows for the export of grid graphics to SVG, which can make graphics more accessible and a new function convert_chunk_header() to help convert from .Rmd to .qmd.

luz 0.3.0
{luz} is a high-level API for torch for R, a machine learning framework based on PyTorch, that aims to encapsulate the training loop into a set of reusable pieces of code. It reduces the boilerplate required to train a model with torch, avoids the error-prone zero_grad() - backward() - step() sequence of calls, and also simplifies the process of moving data and models between CPUs and GPUs. This release brings a few improvements to the learning rate finder and, with the 0.2.0 release, supports R matrices/arrays as input data and adds in new built-in callbacks.

tidyverse

lintr 3.0.0
{lintr} provides both a framework for static analysis of R packages and scripts and a variety of linters, e.g., to enforce the tidyverse style guide. This is a major release, with new authors and many updates. See NEWS for full details, and thank you to the 97 authors who contributed to this release!

tidymodels

censored 0.1.0
We’re extremely pleased to announce the first release of {censored} on CRAN. The censored package is a parsnip extension package for survival models.

rsample 1.1.0
The {rsample} package makes it easy to create resamples for estimating distributions and assessing model performance. The biggest addition in this release is the set of new functions for grouped resampling.

Shiny

gridlayout 0.1.0
Alpha release of {gridlayout} that lets you build dashboard-style layouts for Shiny and R Markdown easily using CSS-Grid.

shinyuieditor 0.1.0
Alpha release of {shinyuieditor}, a visual tool for building the UI portion of a Shiny application that generates clean and human-readable code. The goal of the Shiny UI Editor is to allow people to build the broad-level UI for their Shiny app without writing code.

Shiny for Python
Alpha release of Shiny for Python. Shiny makes it easy to build interactive web applications, now also with the power of Python’s data and scientific stack.

Wrapping Up

Thank you for reading our second rstudio::glimpse() newsletter!

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I’d love to hear from you what you think and what you might like to see in this newsletter! Comment in RStudio Community, and follow along at our @rstudio_glimpse Twitter account too.

And finally:

What does a baby computer call its father?Data.

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