pkgdown

Using R package structure for data science projects | Kylie Ainslie | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We’d love to have you!

The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow.

On this call, Libby Heeren is joined by Kylie Ainslie who walks through how structuring data science projects as R packages provides a consistent framework that integrates documentation for you and facilitates collaboration with others by organizing things really well. Kylie says, “I stumbled on using an R package structure to organize my projects a number of years ago and it has changed how I work in such a positive way that I want to share it with others! In a world where our attention is constantly being pulled in many directions, efficiency is crucial. Structuring projects as R packages is how I work more efficiently.”

Hosting crew from Posit: Libby Heeren, Isabella Velasquez

Company Branding Workflow Demo Live Q&A - February 26th

Company-branded reports, apps, and dashboards made easier with brand.yml & Posit

You will learn: How to apply consistent company branding across reports, dashboards, and apps

Key Links:

Hadley Wickham - R in Production

R in Production by Hadley Wickham

It’s a Great Time to be an R Package Developer! - posit::conf(2023)

Presented by Jenny Bryan and Hadley Wickham

(Due to unforeseen circumstances, Hadley Wickham presented this talk “slide karaoke” style, from materials prepared by Jenny Bryan.)

In R, the fundamental unit of shareable code is the package. As of March 2023, there were over 19,000 packages available on CRAN. Hadley Wickham and I recently updated the R Packages book for a second edition, which brought home just how much the package development landscape has changed in recent years (for the better!).

In this talk, I highlight recent-ish developments that I think have a great payoff for package maintainers. I’ll talk about the impact of new services like GitHub Actions, new tools like pkgdown, and emerging shared practices, such as principles that are helpful when testing a package.

Thanks, I Made It with Quartodoc - posit::conf(2023)

Presented by Isabel Zimmerman

When Python package developers create documentation, they typically must choose between mostly auto-generated docs or writing all the docs by hand. This is problematic since effective documentation has a mix of function references, high-level context, examples, and other content.

Quartodoc is a new documentation system that automatically generates Python function references within Quarto websites. This talk will discuss pkgdown’s success in the R ecosystem and how those wins can be replicated in Python with quartodoc examples. Listeners will walk away knowing more about what makes documentation delightful (or painful), when to use quartodoc, and how to use this tool to make docs for a Python package.

Kelly O’Briant | Building a business case for data science & advocating for analytic infrastructure

3:10 - Start of Kelly’s presentation 5:05 - Identifying where you are in your organizational culture when asking for change 9:30 - What is analytic infrastructure: the how, where, and with what that goes into your daily data science work 10:55 - Production looks different at different organizations 12:20 - Don’t get caught up in the hype - there is no perfect deployment pipeline 14:00 - Tactical metrics for communicating devops - code deployment lead time 15:55 - Your analytic infrastructure is what enables you or teams to deliver value through decreasing that code deployment lead time 20:49 - What is an analytic administrator / R Admin? (A data scientist who onboards new tools, deploys solutions, support existing standards. They work closely with IT to maintain and scale analytic environments. They influence others in the organization to be more effective, and are passionate about making data science a legitimate analytic standard) 24:04 - Building a business case (introducing rstudio.com/champion)

Join us for future champion chats the 4th Monday of every month (not the date I said in the recording) rstd.io/champion-chats

Let’s admit it. Getting to use the tools you want sometimes needs a little convincing.

We’ve had the opportunity to meet so many wonderful people from the community. People that: organize meetups in their own communities, spend time after-hours teaching their co-workers, solve business problems at work with Shiny, write blog posts to help others, and so much more..

People are doing amazing things with data science, yet so many are still in a position where they are not able to make the case for it at their own organizations.

Hear from Kelly O’Briant on how you can start to navigate the internal process and advocate for great analytic infrastructure as a data scientist.

Whether you’re getting pushback about using open-source, being told to use a BI tool instead, or just unable to find the other data scientists at your company - we want to help make this process less frustrating. Come hang out and chat with us!

*Please note, only the presentation portion of this meetup was recorded, the open discussion and Q&A was not.

Carson Sievert || Customizing Navigation Items in Shiny using {bslib} || RStudio

00:00 Introduction 00:15 Linking inside navbarPage 01:19 Replacing tabPanel with navbarPage, and navbarMenu 02:32 nav_spacer() 03:41 Adding header and//or footer content 04:07 Replacing tabsetPanel with navs_tab and navs_pill 04:32 navs_tab_card() and navs_pill_card() variants 04:40 Demo of all of the nav_*() functions

The bslib R package provides tools for customizing Bootstrap themes directly from R, making it much easier to customize the appearance of Shiny apps & R Markdown documents. bslib’s primary goals are:

• To make custom theming as easy as possible.
• Custom themes may even be created interactively in real-time.
• Also provide easy access to pre-packaged Bootswatch themes.
• Make upgrading from Bootstrap 3 to 4 (and beyond) as seamless as possible. (Shiny and R Markdown default to Bootstrap 3 and will continue to do so to avoid breaking legacy code.)
• Serve as a general foundation for Shiny and R Markdown extension packages. (Extensions such as flexdashboard, pkgdown, and bookdown already fully support bslib’s custom theming capabilities.)

Hadley Wickham | testthat 3.0.0 | RStudio (2020)

In this webinar, I’ll introduce some of the major changes coming in testthat 3.0.0. The biggest new idea in testthat 3.0.0 is the idea of an edition. You must deliberately choose to use the 3rd edition, which allows us to make breaking changes without breaking old packages. testthat 3e deprecates a number of older functions that we no longer believe are a good idea, and tweaks the behaviour of expect_equal() and expect_identical() to give considerably more informative output (using the new waldo package).

testthat 3e also introduces the idea of snapshot tests which record expected value in external files, rather than in code. This makes them particularly well suited to testing user output and complex objects. I’ll show off the main advantages of snapshot testing, and why it’s better than our previous approaches of verify_output() and expect_known_output().

Finally, I’ll go over a bunch of smaller quality-of-life improvements, including tweaks to test reporting and improvements to expect_error(), expect_warning() and expect_message().