flexdashboard

Carson Sievert: Towards the next generation of Shiny UI

About the talk: Shiny, the R package for creating interactive web graphics, recently celebrated its 10th birthday. Since then, Shiny has grown tremendously in many areas (e.g., performance, functionality, extensions, etc); however, a “hello world” Shiny app still looks like it did 10 years ago. This is mostly because Shiny goes to great lengths to ensure backwards compatibility; and as a result, default Shiny UI will likely continue to be based on Bootstrap 3 (a CSS styling framework released in 2010). However, thanks to the new bslib R package, it is now easy to opt-into a modern Bootstrap 5 foundation that “just works” with Shiny, R Markdown, flexdashboard, pkgdown, bookdown, and more. In addition to upgrading Shiny’s Bootstrap dependency, bslib also makes it much easier to do custom theming, leverage modern layout techniques, and create custom components (all from R without any CSS/HTML/JS required). At this point, bslib is still maturing, and does not yet provide what we’d consider a “complete UI toolkit”, but it should eventually replace and/or improve upon all of Shiny UI. In this talk, I’ll highlight bslib features that we’re most excited about (e.g., expandable cards, accordions, (sidebar) layouts, input controls, etc.), discuss some best design practices for improving user experience with these tools, and present some real world examples of these tools in action.

Speakers’ bio: Carson is a software engineer on the Shiny team at Posit. He joined Posit in 2018, and in recent years, has focused primarily on Shiny for Python and improving Shiny UI. The Shiny UI work has manifested in the creation and development of many R packages such as bslib, thematic, htmltools, htmlwidgets, sass, shiny, rmarkdown, flexdashboard, and more. Carson also has a PhD in statistics, is a recipient of the ASA’s Chambers Statistical Software Award, has maintained the R package plotly since 2015, authored the book “Interactive data visualization with R, plotly, and shiny”, and ran a successful freelance consulting service for numerous years

Carson Sievert, Shiny Team

Quarto with the Quarto Team | An Open-Source Chat

Join Al Manning, Carlos SchIidegger, & Charles Teague, members of the Quarto Team, as they take our questions.

Quarto is an open-source tool for scientific and technical publishing. Create dynamic content with Python, R, Julia, and Observable. Author documents as plain text markdown or Jupyter notebooks. Publish high-quality articles, reports, presentations, websites, blogs, and books in HTML, PDF, MS Word, ePub, and more. Author with scientific markdown, including equations, citations, crossrefs, figure panels, callouts, advanced layout, and more.

Key Resources: ⬡ Learn more and get started with Quarto at quarto.org

Contact ⬡ Bug reports and feature requests - https://github.com/quarto-dev/quarto-cli/issues ⬡ Need help? Github discussions - https://github.com/quarto-dev/quarto-cli/discussions

Introduction Videos for Quarto ⬡ Mine and Julia talk, https://www.youtube.com/watch?v=p7Hxu4coDl8 ⬡ Quarto Series, 1️⃣ Welcome to Quarto Workshop led by Tom Mock: https://www.youtube.com/watch?v=yvi5uXQMvu4 2️⃣ Building a Blog with Quarto led by Isabella Velásquez: https://www.youtube.com/watch?v=CVcvXfRyfE0&feature=youtu.be 3️⃣ Beautiful reports and presentations with Quarto led by Tom Mock: https://www.youtube.com/watch?v=hbf7Ai3jnxY&feature=youtu.be

Timestamps 00:00:00 Introductions 2:55 Why open source? 6:20 Can we expect to see Quarto available to R-users via CRAN any time soon? 9:10 Quarto and Google Docs? 9:49 Lua filters/shortcodes. Advice for a good development environment for prototyping and debugging? 14:59 Is there a single documentation page for ALL the quarto-specific YAML options? https://quarto.org/docs/reference
16:15 Navigating Quarto’s documentation. 18:00 Is there something like Observable SQL cells on the roadmap? 20:10 Is there something closer to {bookdown} for Quarto? What is the best way to retain data and environment objects in a quarto book? Is there any path to enabling this? See Includes, https://quarto.org/docs/authoring/includes.html 24:20 Flexdashboard? Coming soon. 26:30 A big challenge in the adoption is that Quarto is competing with ipython notebooks for mindspace, what does the Quarto team think about that? Quarto and Jupyter Notebooks will hopefully be thought of as complementary to one another, with Quarto helping a lot with narrative, layout, and appearance for publication and sharing. 30:10 Where should I go to contact you about an issue? What if the issue isn’t just Quarto, say, Quarto + Jupyter? 31:50: What is the Quarto team hoping to see the community produce? Feedback, reporting in github issues. Quarto Extensions. 34:05 Custom styling, configuring grid options. Any tips or anything in the roadmap that will help users finetune the look and feel of their output? 38:40 Terminology question; what do we call a published Quarto doc? (or webpage, blog, etc.?) 40:00 How do I stay up to date with Quarto? Getting the latest release and learning about what is new?

See what’s up on quarto.org. Look under get-started and under downloads for pre-releases

Isabella Velásquez

bookdown cli finetune flexdashboard Quarto Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny RMarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source OSS Reticulate

Jorge Valente Hernández Castelán | Algunas formas de ver a Uber en datos: Shiny en acción

*Please note, this meetup was hosted in Spanish.

Algunas formas de ver a Uber en datos: Shiny en acción Jorge Valente Hernández Castelán

App: https://lnkd.in/evpSjden

¿Cuál es el límite verdadero de la programación en R? Esta pregunta me rondó la cabeza cuando conocí este lenguaje por primera vez hace unos dos años. En aquel momento imaginé que era un lenguaje algo corto de posibilidades, que estaba orientado a estadística y, únicamente, obtener resultados de algunos cálculos. No pude haber estado más equivocado en aquel momento.

Cuando comencé a trabajar con Dashboards, probando primero cosas simples como Flexdashboard, me di cuenta de que el presentar información que se encuentra dentro de una base de datos, archivo de excel, etc, no solo se limita a crear estadísticos y un simple gráfico. Las aplicaciones web hechas con shiny no solo presentan información estadística, te cuentan una historia mediante los datos, la cuál puede ser presentada de muchísimas maneras.

La aplicación “Uber App” de hecho tiene ese objetivo: Contar mediante estadística y geografía una historia, conocer la lógica y la tendencia de los viajes en Uber dentro de la ciudad de Nueva York, pero ¿Cómo surgió la idea de hacer esta app? En realidad fue una experiencia interesante, ya que un día me encontraba visualizando ejemplos de apps hechas dentro de Python, por lo que una en específico me pareció bastante interesante y en ese momento me hice la pregunta: ¿Es posible realizar algo similar en R? La respuesta a la pregunta anterior fue la siguiente: No solo es posible, también es posible hacerla mejor, mucho mejor.

Abstract in English: What is the true limit of programming in R? This question was on my mind when I first came across this language about two years ago. At that time I imagined that it was a language somewhat short of possibilities, that it was oriented to statistics and, only, to obtain results of some calculations. I couldn’t have been more wrong at the time.

When I started working with Dashboards, first trying simple things like Flexdashboard, I realized that presenting information found in a database, excel file, etc, is not just limited to creating statistics and a simple graph. Web applications made with shiny not only present statistical information, they tell you a story through data, which can be presented in many ways.

The application “Uber App” in fact has that objective: To tell a story through statistics and geography, to know the logic and the trend of Uber trips within the city of New York, but how did the idea of making this app come about? It was actually an interesting experience, since one day I was viewing examples of apps made within Python, so one in particular seemed quite interesting to me and at that moment I asked myself the question: Is it possible to do something similar in R? The answer to the previous question was the following: Not only is it possible, it is also possible to do it better

R Markdown Advanced Tips to Become a Better Data Scientist & RStudio Connect | With Tom Mock

R Markdown is an incredible tool for being a more effective data scientist. It lets you share insights in ways that delight end users.

In this presentation, Tom Mock will teach you some advanced tips that will let you get the most out of R Markdown. Additionally, RStudio Connect will be highlighted, specifically how it works wonderfully with tools like R Markdown.

Please provide feedback: https://docs.google.com/forms/d/e/1FAIpQLSdOwz3yJluPR2fEqE0hBt92NtKZzzNACR8KJhHUt9rhFj3HqA/viewform?usp=sf_link

More resources if you’re interested: https://docs.google.com/document/d/1VKGs1G9GcQcv4pCYFbK68_LDh72ODiZsIxXLN0z-zD4/edit

04:15 Literate Programming 09:00 - Rstudio Visual Editor Demo 15:44 - R and python in same document via {reticulate} 18:10 - Q&A: Options for collaborative editing (version control, shared drive etc.) 19:30 - Q&A: Multi-pane support in Rstudio 20:46 Data Product (reports, presentations, dashboards, websites etc.) 24:15 - Distill article 26:27 - Xaringan presentation (add three dashes — for new slide) 28:58 - Flexdashboard (with shiny) 30:30 - Crosstalk (talk between different html widgets instead of {shiny} server) 35:03 - Q&A: Jobs panel – parallelise render jobs in background 36:50 - Q&A: various data product packages, formats 39:35 Control Document (modularise data science tasks, control code flow) 39:58 - Knit with Parameters (YAML params: option) 41:20 - Reference named chunks from .R files (knitr::read_chunk()) 43:00 - Child Documents (reuse content, conditional inclusion, {blastula} email) 47:07 Templating (don’t repeat yourself) 47:38 - rmarkdown::render() with params, looping through different param combinations 49:30 - Loop templates within a single document 50:40 - 04-templating/ live code demo 54:37 - {whisker} vs {glue} – {{logic-less}} vs {logic templating} 55:30 - {whisker} for generating markdown files that you can continue editing 57:49 RMarkdown + Rstudio Connect 1:00:41 Follow-up Reading and resources 1:04:49 Q&A - {shiny} apps, {webshot2} for screenshots of html, reading in multiple .R files, best practice for producing MSoffice files, {blastula}

blastula crosstalk flexdashboard reticulate rmarkdown rstudio Shiny webshot2 Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny RMarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source OSS Reticulate

Mike Garcia | R in Pharma: Intro to Shiny | Posit

Slides: https://garciamikep.github.io/rstudioglobal-2021-shiny-slides/slides.html#1

From rstudio::global(2021) Pharma X-Sessions, sponsored by ProCogia: in this introduction to Shiny app development, we begin with a quick review of visualization with {ggplot2} and then cover core concepts in app structure and reactive programming. After building several Shiny apps of increasing complexity, we wrap up with a demonstration of how to include your Shiny app in a dashboard using the {flexdashboard} package.

About Mike Garcia: Mike is a Data Science Consultant with ProCogia, with a background in Biostatistics and experience in clinical trial design and public health research. If not geeking out on data with a cup of coffee and spreading his passion for R, he’s probably out enjoying the outdoors.

Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/

To hear more about how other major pharmaceutical companies are transitioning to open source data science you can watch talks from this year’s R in Pharma conference: https://www.youtube.com/@RinPharma/playlists

At Posit, we have a dedicated Pharma team to help organizations migrate and utilize open source for drug development. To learn more about our support for life sciences, please see our dedicated Pharma page where you can book a call with our team. (https://posit.co/solutions/pharma )

flexdashboard ggplot2 rstudio Shiny Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny RMarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source OSS Reticulate Mike Garcia Pharma How To

Brendan Graham | Using R Bridge the Gap in Electronic Medical Record Reporting | Posit (2020)

Electronic Medical Records (EMRs) are a treasure trove of information, but tend to fall disappointingly short when it comes to visualizing and reporting data in a user friendly and intuitive manner. Building reports in an EMR can be a frustrating experience; the developer is at the mercy of how the data is stored within the EMR and the available EMR reporting tools can be bland and uninspiring. But reporting on data in the EMR doesn’t have to be this way! Combining the data-rich EMR with R’s robust reporting capabilities benefits both developers and consumers of data. This talk will describe how a cross-departmental project team uses an internal R package, RMarkdown reports scheduled via R Studio Connect, and an interactive flexdashboard app to quickly implement solutions to gaps in the reporting capabilities of the EMR. The flexibility of R relative to EMR reporting tools facilitates a design thinking approach to reporting allowing for more user input, customization and quick iteration. Furthermore, the web-based app we developed is able to be embedded within the EMR itself allowing for a more streamlined workflow

flexdashboard rmarkdown Rstudio::conf(2020) Brendan Graham Rstudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny RMarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source OSS Reticulate

Advanced R Markdown 2-day Workshop - rstudio::conf(2019L)

What is the 2-day Advanced R Markdown Workshop? That’s a great question, I’m glad you asked.

This is a two-day hands-on workshop based on the book “R Markdown: The Definitive Guide”.

This workshop is designed for those who want to take their R Markdown skills to the next level. We’ll talk about many low-level details in the rmarkdown package and the whole R Markdown ecosystem. The two goals of this workshop are: 1) learn how to fully customize R Markdown output (HTML, LaTeX/PDF, Word, and PowerPoint); and 2) learn more about existing R Markdown extensions in the ecosystem, such as flexdashboard, bookdown, blogdown, pkgdown, xaringan, rticles, and learnr. We will also talk about how to use or develop new language engines (languages that are not R), how to develop HTML widgets, and integrate Shiny with R Markdown.

You should take this workshop if you have experience programming in R and want to learn how to take advantage of the amazing breadth and depth of R Markdown. You’ll get the most from it if you enjoy learning how R Markdown works under the hood (which will involve reading some source code), and are seriously interested in hacking (playing) with HTML, JavaScript, CSS, LaTeX, and command-line tools. We will give minimal tutorials on these languages and tools in the workshop, but it may be easier for you to keep pace with the instructor if you already know them before.

This workshop is led by Yihui Xie, Software Engineer and Data Scientist at RStudio. Yihui is the main author of the open-source knitr package for reproducible research and dynamic report generation. He has also created and co-authored several other R packages, including rmarkdown, bookdown, blogdown, xaringan, tinytex, DT, shiny, and leaflet. He has authored and co-authored four books: Dynamic Documents with R and knitr, bookdown: Authoring Books and Technical Documents with R Markdown, blogdown: Creating Websites with R Markdown, and R Markdown: The Definitive Guide.

Speakers: Yihui Xie and Alison Hill with guest lecturers Jiena Gu and Hao Zhu

flexdashboard

Contributors#

JJ Allaire

Carson Sievert

Garrick Aden-Buie

Barret Schloerke

Winston Chang

Christophe Dervieux

Joe Cheng

Rich Iannone

Resources featuring flexdashboard#

Why RStudio is now Posit (J.J. Allaire | Posit CEO) - KNN Ep. 158

Carson Sievert: Towards the next generation of Shiny UI

Quarto with the Quarto Team | An Open-Source Chat

Jorge Valente Hernández Castelán | Algunas formas de ver a Uber en datos: Shiny en acción

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

R Markdown Advanced Tips to Become a Better Data Scientist & RStudio Connect | With Tom Mock

Julia Silge | Monitoring Model Performance | RStudio

Mike Garcia | R in Pharma: Intro to Shiny | Posit

Brendan Graham | Using R Bridge the Gap in Electronic Medical Record Reporting | Posit (2020)

Yihui Xie | One R Markdown Document, Fourteen Demos | RStudio (2020)

Advanced R Markdown 2-day Workshop - rstudio::conf(2019L)

Posts about flexdashboard#

Custom Google Analytics Dashboards with R: Building The Dashboard