Rstudio::conf(2020)

Jake Thompson | Branding and Packaging Reports with R Markdown | RStudio (2020)

The creation of research reports and manuscripts is a critical aspect of the work conducted by organizations and individual researchers. Most often, this process involves copying and pasting output from many different analyses into a separate document. Especially in organizations that produce annual reports for repeated analyses, this process can also involve applying incremental updates to annual reports. It is important to ensure that all relevant tables, figures, and numbers within the text are updated appropriately. Done manually, these processes are often error prone and inefficient. R Markdown is ideally suited to support these tasks. With R Markdown, users are able to conduct analyses directly in the document or read in output from a separate analyses pipeline. Tables, figures, and in-line results can then be dynamically populated and automatically numbered to ensure that everything is correctly updated when new data is provided. Additionally, the appearance of documents rendered with R Markdown can be customized to meet specific branding and formatting requirements of organizations and journals. In this presentation, we will present one implementation of customized R Markdown reports used for Accessible Teaching, Learning, and Assessment Systems (ATLAS) at the University of Kansas. A publicly available R package, ratlas, provides both Microsoft Word and LaTeX templates for different types of projects at ATLAS with their own unique formatting requirements. We will discuss how to create brand-specific templates, as well as how to incorporate the templates into an R package that can be used to unify report creation across an organization. We will also describe other components of branding reports beyond R Markdown templates, including customized ggplot2 themes, which can also be wrapped into the R package. Finally, we will share lessons learned from incorporating the R package workflow into an existing reporting pipeline. https://rstudio.com/resources/rstudioconf-2020/branding-and-packaging-reports-with-r-markdown/

Alicia Schep | Auto-magic Package Development | RStudio (2020)

Vega-lite is a high-level grammar of interactive graphics implemented in Javascript; it renders interactive visualizations in the browser based on a JSON specification. In Python and Javascript, the Altair and vega-lite-api packages have demonstrated how the development of APIs to build Vega-Lite graphics can be partially automated based on the Vega-Lite JSON schema, which describes the required format for a Vega-Lite JSON specification. This talk will describe the development of the ‘vlbuildr’ package for building Vega-Lite specifications in R and the ‘vlmetabuildr’ package for building the ‘vlbuildr’ package. The ‘vlbuildr’ package seeks to provide a pipe-friendly, “R-like” functional interface for building up simple to complex specifications for Vega-Lite graphics, which can in turn be rendered as an HtmlWidget by the ‘vegawidget’ R package. Building such an API in a fully automated way from the Vega-Lite schema presents considerable challenges, so the approach taken here was to rely on partial automation. Human judgement dictates the basic contours of the API, such as what groups of functions to include and how various types of building blocks will go together. The part that is automated is filling in many details such as the different variants of a group of functions, the exact parameters needed for each function, and the documentation of those parameters -- the parts that would be extremely tedious to port over!

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

Dr. Travis Gerke | UnicoRns are real | RStudio (2020)

Common advice from experienced data scientists to job-seekers is to avoid job postings that describe a "data science unicorn": someone who has experience performing an unrealistically large array of technical and business-related job duties. Seeking a unicorn is viewed as a potential indicator that the company fails to understand their data science needs, and that new hires will not be poised for success due to lacking support and resources [Robinson & Nolis, 2019]. The R language, particularly when used with RStudio products, has evolved to enable production-level activities in the areas of data wrangling, reporting/dashboarding, database/software engineering, machine learning, and web application development. It is increasingly plausible that a data scientist will be able to efficiently perform a wide variety of job functions with experience only in a single language (R). Indeed, even entry level R users may tread into "unicorn" territory. Current standards for data scientist job descriptions and salaries do not accommodate this nuance, leaving both job-seekers and hiring managers unable to distinguish job requirements which should be read as warning signs from listings which are idyllic matches for the modern R unicorn. In this talk, we present data aggregated from several large compensation analytics companies which summarize current benchmarks for data science job descriptions and corresponding salary ranges. We then suggest job description language to target modern R users, considering both job duty compatibility and job post findability. These descriptions are presented with likely salary range pairings. Attention is given to deviations from traditional degree requirements, years of experience, and demands for multiple programming language literacy which may lack relevance for the R unicorn. Our overarching goal is to provide job description templates which encourage optimal matchmaking between R job seekers and organizations in need of their talents

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

Aymen Waqar | Building an iPad dashboard using plumber & RStudio Connect in Pharma | RStudio (2020)

As companies are becoming aware of the need to embrace data-driven solutions, R has gained a huge momentum over recent years. Getting the insights to users has become a very important factor of Data Scientist work. While our world has advanced there is a need to build not only web applications, but also applications on mobile that are available offline. We would like to share with you how within months we have gone from nothing to a production-ready application that handles 500 concurrent users in healthcare. There are plenty of challenges to solve including restricted environments, internal processes and users availability. We will show you how to overcome them and iterate fast, navigating through complex infrastructure and integrating with proxy architecture to serve applications to end users in compliant manner. With RStudio Connect and Plumber you can deploy a scalable REST API that can feed insights to your users. This allows you to go one step further and implement native applications for tablets and smartphones. With the right tools, mindset and priorities you can achieve personal success by introducing a digital transformation within your organization, starting with something as small as converting a business critical Excel file that is slow, difficult to edit and maintain, to a robust application. Step by step your organization will evolve and become empowered by your insights uncovering even more untapped potential

Therese Anders | Peer review in data science courses | RStudio (2020)

Peer review enables instructors of large data science classes to provide substantive feedback to students beyond what is feasible with standard code review via automated grading and continuous integration. It facilitates peer learning, which is shown in literature to have positive learning outcomes, and can reduce the burden of grading by course staff. The ghclass package provides a suite of functions to manage courses via GitHub repositories. The package has recently been supplemented with the functionality to implement peer review. Developed during my 2019 summer internship with RStudio in collaboration with my mentor Mine Çetinkaya-Rundel, the peer review functions in ghclass interface with the GitHub API to create review repositories, move files between authors and reviewers, submit feedback, and collect grades. In this presentation, I will give a demonstration of the peer review functions in ghclass. A set of six functions allows instructors to 1) create a random review roster, 2) set up the review repository infrastructure within a GitHub organization, 3) move assignments from authors to reviewers, 4) collect grades, 5) return the feedback, and 6) obtain a rating of the review from the authors. I reflect on the pedagogy of implementing peer review in introductory data science classes and talk about lessons learned from a real-world test run of the package in the Fall semester 2019 at the University of Edinburgh, conducted by Mine Çetinkaya-Rundel. The presentation highlights ghclass as an R command-line based, open source, low profile, and powerful solution to enable peer review in classes ranging from a size of two to approximately 400 students. A 5 minute presentation in our Lightning Talks series

Mine Çetinkaya-Rundel

Colin Fay | Production-grade Shiny Apps with golem | RStudio (2020)

Shiny is an amazing tool when it comes to creating web applications with R. Almost anybody can get a small Shiny App in a matter of minutes, provided they have a basic knowledge of R. As of today, we can safely tell that it has become the de-facto tool for web application in the R world. Building a proof-of-concept application is easy, but things change when the application becomes larger and more complex, and especially when it comes to sending that app to production—until recently there hasn't been any real framework for building and deploying production-grade Shiny Apps. This is where 'golem' comes into play: offering Shiny developers an opinionated framework for creating production-ready Shiny Applications. With 'golem', Shiny developers now have a toolkit for making a stable, easy-to-maintain, and robust for production web application with R. 'golem' has been developed to abstract away the most common engineering tasks (for example, module creation, addition of external CSS or JavaScript file, ...), so you can focus on what matters: building the application. And once your application is ready to be deployed, 'golem' guides you through testing, and brings you tools for deploying to common platforms. In this talk, Colin and Vincent will present the 'golem' package, first talking about the "why 'golem'?", then presenting the general philosophy behind this framework, and help you get started building your first Shiny App with 'golem'

Katherine Simeon | Every voice matters: An analysis of @WeAreRLadies | RStudio (2020)

As a rotating curation, @WeAreRLadies is a twitter account that has a different curator (i.e., tweeter) each week with a mission to highlight female and minority genders and their work in R. So far, curators have tweeted from 18 different countries and represent a variety of domains and levels of R expertise, ranging from R novices to those developing their own packages. With 45 R-Ladies curators to date, the account has become a popular R-related twitter resource, gaining more than 13,000 followers in the past year and hundreds of interactions each week. This talk will present a text analysis and reflection on over a year of Twitter text data from @WeAreRLadies. As the founder and maintainer of this account, I witness firsthand the bidirectional relationship between one’s learning journey and their use of R. In this talk, I will attempt to quantify this through a text analysis that explores how one’s experiences learning and using R relates to how they talk (or tweet) about it. By analyzing tweet text as well as other metrics provided by twitter (e.g., number of likes, replies, and clicks), I will showcase different ways curators have engaged with the R Twitter community and explore how account engagement has changed as the number of curators and followers continue to grow. I will also discuss how curators’ different areas of expertise have resulted in tweets and discussions that both demonstrate the variety of tools available in R, and spotlight unifying ideas and best practices in R programming. Finally, I will reflect on lessons learned and future directions for @WeAreRLadies, as well as its contribution to the R-Ladies Global initiative. Overall, this talk will discuss how diverse perspectives of @WeAreRLadies curators have enriched the conversations in the R Twitter community by validating different learning journeys and by promoting and amplifying underrepresented voices. A 5-minute presentation in our Lightning Talks series

Davis Vaughn | Sliding Windows and Calendars | RStudio (2020)

A number of R packages exist to make computing moving averages on a single numeric series straightforward. But generally “real” life is much messier than that! Try computing a moving average over a twenty-day sliding window when you have a time series with missing data. Oh! By the way, you should also skip over weekends when looking back twenty days. And you know that random holiday that your company celebrates that no one else does? Skip over that too. These are hard but realistic problems, and until now there has been a lack of tools necessary to solve them. In this talk, I’ll present two packages designed to tackle these issues, slide and almanac. slide is a package designed to perform arbitrary sliding window calculations. The simplest example of this would be a moving average. What makes slide unique is its support for sliding relative to an index, such as a date vector, which allows you to correctly compute the boundaries of that twenty day window. almanac is package for creating custom business calendars, and then adjusting dates relative to them. Inspired by lubridate, almanac allows you to shift dates by a set number of “business” days while respecting the weekends and holidays defined by a user-specified calendar. For example, shifting a Friday forward by 1 business day would land on a Monday, unless that Monday happened to be a holiday, in which case the next business day would actually be Tuesday. Together, slide and almanac provide the tooling necessary to solve the problem mentioned earlier. Additionally, because slide works with any arbitrary function, we can use the same procedure to compute rolling regressions, cumulative sums, and any other sliding computation. A 5-minute presentation in our Lightning Talks series

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

Rstudio::conf(2020)

Resources tagged Rstudio::conf(2020)#

Jake Thompson | Branding and Packaging Reports with R Markdown | RStudio (2020)

Max Kuhn | Total Tidy Tuning Techniques | RStudio (2020)

Joe Cheng | Styling Shiny apps with Sass and Bootstrap 4 | Posit (2020)

Eduardo Ariño de la Rubia | Value in Data Science Beyond Models in Production | RStudio (2020)

Dewey Dunnington | Best practices for programming with ggplot2 | RStudio (2020)

Alicia Schep | Auto-magic Package Development | RStudio (2020)

Desiree De Leon | Of Teacups, Giraffes, and R Markdown | RStudio (2020)

Namita Nandakumar | R + Tidyverse in Sports | RStudio (2020)

Gordon Shotwell | Technical Debt is a Social Problem | Posit (2020)

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

Dr. Travis Gerke | UnicoRns are real | RStudio (2020)

Brandon Farr | 15 Years of R in Quantitative Finance | Posit (2020)

Kevin Ushey | renv: Project Environments for R | RStudio (2020)

Hadley Wickham | State of the Tidyverse 2020 | RStudio (2020)

Evangeline Reynolds | Flipbooks | RStudio (2020)

Danielle Navarro | Toward a grammar of psychological experiments | RStudio (2020)

Colin Gillespie | How to win an AI Hackathon, without using AI | RStudio (2020)

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

Andrew Mangano | Growth Hacking - Product Analytics at Scale using R and RStudio | RStudio (2020)

Ian Lyttle | Small Team, Big Value: Using R to Design Visualizations | RStudio (2020)

Mine Çetinkaya-Rundel | Making the Shiny Contest | RStudio (2020)

Thomas Lin Pedersen | Extending your ability to extend ggplot2 | RStudio (2020)

Tyson Barrett | List-columns in data.table | RStudio (2020)

Henrik Bengtsson | Future: Simple Async, Parallel & Distributed Processing in R | RStudio (2020)

Dr. Carson Sievert | Reproducible Shiny apps with shinymeta | RStudio (2020)

Ben Joaquin | Data Science in Meatspace | RStudio (2020)

Aymen Waqar | Building an iPad dashboard using plumber & RStudio Connect in Pharma | RStudio (2020)

Karl Feinauer | Using Jupyter with RStudio Server Pro | RStudio (2020)

David Smith | MLOps for R with Azure Machine Learning | RStudio (2020)

Ron Keizer | Building a Medical Device with R | Posit (2020)

Neal Richardson | Accelerating Analytics with Apache Arrow | RStudio (2020)

Ryan Timpe | Learning R with humorous side projects | RStudio (2020)

Jay Campanell | A high school student’s journey to bring R into the classroom | Posit (2020)

Javier Luraschi | Updates on Spark, MLflow, and the broader ML ecosystem | RStudio (2020)

Bryan Lewis | Parallel computing with R using foreach, future, and other packages | RStudio (2020)

Gergely Daroczi | Getting things logged | RStudio (2020)

Dr. Sydeaka Watson | Neural Networks for Longitudinal Data Analysis | RStudio (2020)

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

Therese Anders | Peer review in data science courses | RStudio (2020)

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

Paige Bailey | Deep Learning with R | RStudio (2020)

Dr. Tyler Morgan-Wall | 3D ggplots with rayshader | RStudio (2020)

Katie Masiello | Professional Case Studies | RStudio (2020)

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

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

Miriah Meyer | Effective Visualizations | RStudio (2020)

Daniel Falbel | What's new in TensorFlow for R | RStudio (2020)

Tina Cormier | Embracing R in the Geospatial Community | RStudio (2020)

Lionel Henry | Interactivity and Programming in the Tidyverse | RStudio (2020)

Colin Fay | Production-grade Shiny Apps with golem | RStudio (2020)

Jared Lander | R: Then and Now | RStudio (2020)

William Chase | The Glamour of Graphics | RStudio (2020)

Riva Quiroga | The development of "datos" package for the R4DS Spanish translation| RStudio (2020)

Nima Safaian | Rpanda trading simulation - from an idea to a multi-user shiny app | RStudio (2020)

Carl Howe & Greg Wilson | Data Science Education in 2022 | RStudio (2020)

Jeff Leek | Data science education as a public health intervention in E. Baltimore | RStudio (2020)

Lauren Chadwick | Meet You Where You R | RStudio (2020)

Jesse Sadler | vctrs: Creating custom vector classes with the vctrs package | RStudio (2020)

Rebecca Barter | Becoming an R blogger | RStudio (2020)

Mike K Smith | Learn to teach, for goodness sake | RStudio (2020)

Kelly Bodwin | Course Material Creation in the R Ecosystem | RStudio (2020)

Katherine Simeon | Every voice matters: An analysis of @WeAreRLadies | RStudio (2020)

Jon Harmon | Learning by Teaching: Mentoring at the R4DS Online Learning Community | RStudio (2020)

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

Hunter Glanz | The Five Principles of Data Science Education | RStudio (2020)

Dr. Amelia McNamara | Making a tidy dress | RStudio (2020)

Davis Vaughn | Sliding Windows and Calendars | RStudio (2020)

Colin Rundel | `livecode`: broadcast your live coding sessions from and to RStudio | RStudio (2020)

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

Athos Damiani | Sound annotation with Shiny and wavesurfer | RStudio (2020)

Amanda Gadrow | Lessons about R I learned from my cat | RStudio (2020)

Jenny Bryan | Object of type ‘closure’ is not subsettable | RStudio (2020)

Emily Riederer | RMarkdown Driven Development | RStudio (2020)

Claus Wilke | Spruce up your ggplot2 visualizations with formatted text | RStudio (2020)

Jim Hester | Azure Pipelines and GitHub Actions | RStudio (2020)

Larry Fenn | Journalism with RStudio, R, and the tidyverse | RStudio (2020)

George Kastrinakis | Building a data science pipeline for the FT with RStudio Connect | RStudio

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