rmarkdown

rmarkdown

Quick reference guide for rmarkdown

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

Benjamin Braun | Publishing Customer Facing Products with RStudio Connect | RStudio (2022)

Benjy Braun, Chief Architect for 202 Group, shows why he and the 202 Group team decided to use RStudio Connect to build customer facing applications and secure websites while focusing on data science and not having to worry about hiring for—or learning—a bunch of web tools like JavaScript and php. With well organized git repos and Rmarkdown, you can build websites for customers with embedded shiny apps and dynamic visualizations and publish and deploy them using RStudio Connect. Using this method, you won’t confine your analysis to static documents like PowerPoint and pdfs and your customers will always see the latest and most up do date information. The tools are out there to do this and it’s easy to start and iterate with more features overtime.

Session: Data science in production

Maya Gans | Creating a Design System for Shiny and RMarkdown | RStudio (2022)

A design system is a set of standards to manage design at scale by reducing redundancy while creating a shared language and visual consistency across different pages and channels. This talk will go into detail about the history of design systems, what components to include when creating a design system, and lastly various examples of implementation. By the end of the talk, you should have the tools to create your own design system within Shiny/RMarkdown to be used by yourself, your brand, or your company.

Session: Pour some glitter on it: Polishing the design of your shiny apps

Tracy Teal | These are a few of my favorite things (about Quarto presentations) | RStudio (2022)

Quarto is the next generation of RMarkdown, and comes with a new presentation format, revealjs. In this talk, I’ll show a few of my favorite things about making interactive HTML presentations with Quarto. Along the way you’ll learn about the visual editor, multiple columns, delivering an effective talk with speaker notes and mode, making your presentations pop with transitions, effective ways to incorporate code into your presentation, beautiful themes (and how to match your corporate style guide), and sharing it as pdf and HTML. I’ll finish off with a brief look at generating a powerpoint presentation from a template.

Session: Quarto deep dive

Yihui Xie | Highlights of the Knitr Package | Posit (2022)

In this talk, I will highlight a few selected new features of the knitr package introduced in the past two years including: 1) the new way to write chunk options as “#|” comments in the chunk body; 2) new ways to reuse both chunk body and chunk options from another code chunk; 3) a few useful new engines, namely comment (comment out arbitrary content), verbatim (write arbitrary verbatim content), embed (embed external files and display their content verbatim), and exec (execute an arbitrary command); and 4) a few new chunk options such as file (to include external files as the chunk body) and lang. Hopefully these new features can help both knitr users and developers make better use of knitr.

Session: RMarkdown and Quarto

John Helveston | Using formr to create R-powered surveys with individualized feedback | RStudio

This talk demonstrates how the formr study framework extends the power and flexibility of R to surveys. Using R and RMarkdown code, researchers and teachers can use the formr platform to generate both simple surveys and complex studies with individualized feedback. The platform is built on a web-based application programming interface for R via OpenCPU, enabling complex features such as automated email and text message reminders, adaptive testing, graphical and interactive feedback, and integration with external data sources. In this talk, I introduce some of the formr basics and showcase two examples of how I have used it, including making conjoint surveys with randomized images and timed, randomized quizzes for my students.

About John: John Paul Helveston is an Assistant Professor in the Engineering Management and Systems Engineering Department at the George Washington University. He studies technological change, with a particular interest in accelerating the transition to environmentally sustainable and energy-saving technologies. His research centers around how consumer preferences, market dynamics, and policy affect the emergence of critical technologies, such as electric vehicles and solar energy. He is an expert on China’s rapidly emerging electric vehicle industry as well as the critical relationship between the US and China in developing and mass producing low carbon energy technologies. He applies an interdisciplinary approach to research, with expertise in discrete choice modeling and conjoint analysis as well as interview-based case studies. He has conducted extensive fieldwork in China, collaborating with colleagues at Tsinghua university, Beijing Normal University, and China’s State Information Center on past projects. He is a fluent speaker of Mandarin Chinese and also an award-winning swing dancer. John holds a Ph.D. and M.S. in Engineering and Public Policy from Carnegie Mellon University and a B.S. in Engineering Science and Mechanics from Virginia Tech

JooYoung Seo | Accessible Data Science Beyond Visual Models | RStudio

Full title: Accessible Data Science Beyond Visual Models: Non-Visual Interactions with R and RStudio Packages

Data science is full of vision-dominant practices, and most data scientists rely heavily on visual models.

However, data science itself should require insight and computational thinking beyond what is just seen by eyes.

JooYoung Seo, who is a blind data scientist and who was working for RStudio’s accessibility projects over the summer 2020, will talk about his experience with some non-visual techniques to interact with data.

If you would like to know more about various ways of making data science accessible via R, and new accessibility features introduced in RStudio IDE and Shiny, his demonstration without sight will be thought-provoking.

About JooYoung: JooYoung Seo is a Ph.D candidate in the Learning, Design, and Technology program at the Pennsylvania State University, and internationally certified accessibility professional whose research and development focuses on accessible computing for all. As an RStudio’s double-certified data science instructor (i.e., Tidyverse + Shiny), who is blind, he is committed to making data science ecosystem more accessible to people with and without dis/abilities using R. To this end, he has been actively contributing to R open-source projects including Shiny, RMarkdown, bookdown, and distill for accessibility, and interned on the RStudio IDE and Shiny team as an accessibility engineer in summer 2020

Shiny Team

Mike K Smith | Using rmarkdown and parameterised reports | RStudio

My brain is lazy, shallow and easily distracted. Learn how I use notebooks to keep my present-self organised, my future-self up to speed with what I was thinking months ago, and also how I use parameterised reports to share results for both quantitative and non-quantitative audiences across multiple endpoints. I can update and render outputs for a variety of outputs from a single markdown notebook or report. I’ll show you how I organise my work using the Tidyverse, use child documents with parameterisation and also how this is served out to my colleagues via RStudio Connect.

About Mike K Smith: I have 25 years experience of working in the Pharmaceutical industry (Pfizer), with more than 15 years working on modelling and simulation projects. I am a keen advocate of smarter drug development with a particular interest in Bayesian methods, dose-response, reproducible research and knowledge management. My particular expertise is in the use of simulation methodology to predict drug outcomes, find efficient trial designs, assess decision criteria and evaluate analysis methodologies. My current role at Pfizer is as specialist in computation and modeling solutions - evaluating and deploying new tools and training colleagues. I am an RStudio certified tidyverse trainer

Ellis Hughes | Approaches to Assay Processing Package Validation | RStudio (2020)

In this talk I will discuss the steps that have been created for validating internally generated R packages at SCHARP (Statistical Center for HIV/AIDS Research and Prevention) and the lessons learned while creating packages as a team. Housed within Fred Hutch, SCHARP is an instrumental partner in the research and clinical trials surrounding HIV prevention and vaccine development. Part of SCHARP’s work involves analyzing experimental biomarkers and endpoints which change as the experimental question, analysis methods, antigens measured, and assays evolve. Maintaining a validated code base that is rigid in its output format, but flexible enough to cater a variety of inputs with minimal custom coding has proven to be important for reproducibility and scalability. SCHARP has developed several key steps in the creation, validation, and documentation of R packages that take advantage of R’s packaging functionality. First, the programming team works with leadership to define specifications and lay out a roadmap of the package at the functional level. Next, statistical programmers work together to develop the package, taking advantage of the rich R ecosystem of packages for development such as roxygen2, devtools, usethis, and testthat. Once the code has been developed, the package is validated to ensure it passes all specifications using a combination of testthat and rmarkdown. Finally, the package is made available for use across the team on live data. These procedures set up a framework for validating assay processing packages that furthers the ability of Fred Hutch to provide world-class support for our clinical trials

Braulio Cuandon & Ana Alyeska Santos | Using R to Create Reproducible Engineering Test Reports

Talk from rstudio::conf(2020)

Engineers at Biosense Webster, a Johnson and Johnson medical device company that specializes in diagnosing and treating cardiac arrhythmias, write multiple test reports to comply with FDA regulatory standards. These intricate reports require 36 hours of an engineer’s time on average, constraining the engineers from completing investigations and studies in a timely matter. Writing scripts in R that create reproducible reports can significantly reduce the time spent by an engineer creating these reports allowing them to do a much thorough investigation with a larger scope. Through Shiny, engineers could conveniently have their parameters and recorded data processed and stored in a database by accessing a web link and filling out the required information within a user-friendly interface. Upon the generation of the report, accurate and properly formatted test reports, compliant to both the company and FDA regulatory standards, are produced through Rmarkdown and knitr knitting all the outputs with complete data analysis tools such as normality plots and process capability measurements to a word document that follows company required headers, footers, and headings. The reproducible report creation shown in this report can be extended to other types of test reports and protocols. The pilot phase that has been conducted has shown that complete report production has been decreased from 36 hours to an hour

Evangeline Reynolds | Flipbooks | RStudio (2020)

Good examples facilitate accomplishing new or unpracticed tasks in a programmatic workflow. Tools for communicating examples have improved in recent years. Especially embraced are tools that show code and its resultant output immediately thereafter — the case of Jupytr notebooks and Rmarkdown documents. But creators using these tools often must choose between big-picture or narrow-focus demonstration; creators tend to either demo a complete code pipeline that accomplishes a realistic task or instead demonstrate a minimal example which makes clear the behavior of a particular function, but how it might be used in a larger project isn’t clear. Flipbooks help address this problem, allowing the creator to present a full demonstration which accomplishes a real task, and gives the viewer the opportunity to focus on unfamiliar steps. A set of flipbook building functions parse code in a data manipulation or visualization pipeline and then build it back up incrementally. Aligned superimposition of new code and output atop previous code and output makes it easy to identify how each code change triggers changes in output. The presentation will guide attendees in creating their own Flipbooks (with Xaringan slides) or mini Flipbooks (gif output)

Rob Hyndman | How Rmarkdown changed my life | RStudio (2020)

Over the last few years, Rmarkdown seems to have taken over my life, or at least my written communication. These days I use Rmarkdown to maintain my website, write my blog, write textbooks, write academic papers, prepare slides for talks, keep my CV up-to-date, help my students write theses, prepare university policy documents, write letters, prepare exams, write reports for clients, and more. I haven’t quite got to the point of using it for shopping lists, but perhaps that’s my next Rmarkdown template. I will reflect on the journey in getting to this point, what I’ve lost and what I’ve gained. I will also speculate on what might be next in the Rmarkdownification of my life

Frank Harrell | R for Graphical Clinical Trial Reporting | RStudio (2020)

For clinical trials a good deal of effort goes into producing both final trial reports and interim reports for data monitoring committees, and experience has shown that reviewers much prefer graphical to tabular reports. Interactive graphical reports go a step further and allow the most important information to be presented by default, while inviting the reviewer to drill down to see other details. The drill-down capability, implemented by hover text using the R plotly package, allows one to almost entirely dispense with tables because the hover text can contain the part of a table that pertains to the reviewer’s current focal point in the graphical display, among other things. Also, there are major efficiency gains by having a high-level language for producing common elements of reports related to accrual, exclusions, descriptive statistics, adverse events, time to event, and longitudinal data. This talk will overview the hreport package, which relies on R, RMarkdown, knitr, plotly, Hmisc, and HTML5. RStudio is an ideal report development environment for using these tools

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

Caroline Ledbetter | Rproject templates to automate and standardize your workflow | RStudio (2020)

Many teams and organizations have tasks and structures that are standard across projects. Lack of consistency and documentation can lead to lost productivity when team members join collaborations or previous work is consulted by your future self. Setting up folder structures can be particularly tedious. This talk will demonstrate using Rstudio project templates as part of an organizational package to automatically setup file structures, establish git repositories and add standardized readme files. It will also show how including report templates for Rmarkdown files can lead to more consistent and professional reports. Project info can be optionally stored so that project information can be easily added automatically to the top of reports and included in snippets for code file headers. Creating standard, easy to implement documentation and procedures can be particularly effective in encouraging skeptical collaborators to use git and Rmarkdown. Organizational packages can also be a great place to house functions that are specific and common to an organizations needs. The talk will showcase this functionality using the CIDAtools package that we developed. While the CIDAtools package was developed to address issues that sometimes arise from the less structured environment of academia, the tools presented can be equally useful in an industry setting. A 5-minute presentation in our Lightning Talks series

Javier Luraschi | Datasets in Reproducible Research with ‘pins’ | RStudio (2020)

Open source code is an essential piece in making science reproducible. Tools like ‘rmarkdown’ and GitHub facilitate running and sharing outcomes with colleagues and with the broad scientific community at large. However, it is less clear what tools should be used to retrieve, store and share datasets; while it is possible to make datasets part of your workflows today, it is usually hard and we are often left with manually sharing or downloading links to datasets. Not only that, but it’s also hard to share or discover datasets. In this talk we will introduce for the first time the ‘pins’ package. A package designed to: pin, discover and share resources. Meaning that, you can use ‘pins’ to simplify your data science workflows by easily fetching resources from GitHub, Kaggle, CRAN and RStudio Connect. We will present a ‘pin’ as a generic resource that can contain tabular datasets like CSVs, unstructured data like JSON files, image archives as ZIP files and so on. This talk will be highly interactive showing you how to get started by installing ‘pins’ from CRAN, retrieve and cache resources, share and discover useful and fun data resources to improve and enhance your day-to-day workflows

Emily Riederer | RMarkdown Driven Development | RStudio (2020)

RMarkdown enables analysts to engage with code interactively, embrace literate programming, and rapidly produce a wide variety of high-quality data products such as documents, emails, dashboards, and websites. However, RMarkdown is less commonly explored and celebrated for the important role it can play in helping R users grow into developers. In this talk, I will provide an overview of RMarkdown Driven Development: a workflow for converting one-off analysis into a well-engineered and well-designed R package with deep empathy for user needs. We will explore how the methodical incorporation of good coding practices such as modularization and testing naturally evolves a single-file RMarkdown into an R project or package. Along the way, we will discuss big-picture questions like “optimal stopping” (why some data products are better left as single files or projects) and concrete details such as the {here} and {testthat} packages which can provide step-change improvements to project sustainability

Sharla Gelfand | Don’t repeat yourself, talk to yourself! Reporting with R | RStudio (2020)

If you’re responsible for analyses that need updating or repeating on a semi-regular basis, you might find yourself doing the same work over and over again. The principle of “don’t repeat yourself” from software engineering motivates us to use functions and packages, the core of repetition in the R universe. For analyses, it can be difficult to know how to use this principle and move beyond “copying and pasting scripts and changing the data file and the object names and updating the dates and results in RMarkdown”, especially when there’s some element of human intervention required, whether it be for validating assumptions or cleaning artisanal data. This talk will focus on those next steps, showcasing opportunities to stop repeating yourself and instead anticipate the needs of and communicate effectively with your future self (or the next person with your job!) using project-oriented workflows, clever interactivity, templated analyses, functions, and yes, your own packages

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