Virtual useR! 2024

This presentation will introduce the newly designed 'Cloud Fundamentals for scientific workflows' lesson, created using The Carpentries Workbench template, including the R packages sandpaper, pegboard and varnish. This lesson explores the principles of cloud data management as applied in an innovative environment science data platform. This interactive lesson navigates participants through the platform's repertoire of functionalities, informing them on elements like data storage and logical representation, organisation of databases via data layers and containers, the management of diverse working environments, and the platform's unique operation as a 'data lake' for raw data storage. Furthermore, the session will unravel an end-to-end data management process on the cloud platform, spotlighting real-world usage through a practical example using a tide gauge dataset. Designed with a learner-centric approach, this lesson includes engaging challenges to enhance comprehension. It offers participants a solid grounding in cloud computing and its practical application in scientific research.

Clinical research is one of the fastest growing fields in the world, and R is becoming increasingly important as a way to handle data, especially as more and more studies are conducted with small numbers of patients, or in collaboration with multiple institutions to collect data and conduct research. Rather than using R to analyze data, clinical researchers have typically focused on study design, data collection, and validation, while coding has been done by professional developers, but now more and more clinical researchers are trying to use R themselves, including data management. To this end, we have been providing R training for clinical researchers, but there is a lot of room for improvement compared to professional training services, such as reflecting the latest R-related technology trends and making the training experience better. In this session, I will share how we decided to use Quarto, what we considered in order to provide R training for clinical researchers, how we actually used Quarto, the advantages and disadvantages of using Quarto, our achievements, and our future plans.

The R Development Guide (R Dev Guide) serves as a comprehensive resource to facilitate the onboarding process for new contributors to the R project. Its initial draft emerged in 2021 which was made possible through a funding from the R Foundation. Subsequently, a significant update took place during Google Season of Docs (GSoD) 2022. This update involved the inclusion of new chapters and sections, addressing important aspects such as translations, adopting a git workflow for proposed patches, and providing novice-friendly instructions on installing R from source. Work has continued post-GSoD 2022, to implement further updates and improvements, many in response to feedback on earlier versions. This talk reviews the current status of the guide, shedding light on the positive impact it has had on the R community, particularly among new contributors. With the enhancements to the Guide, contributing has become more accessible, especially for newcomers, compared to relying solely on the official documentation. The talk aims to showcase the evolving nature of the guide and underscore its role in fostering a more inclusive and supportive environment for those engaging with the R project.

This presentation aims to introduce the R-Ladies Paris community, a local chapter of the global organization R-Ladies Global, which strives to reduce gender inequalities and enhance the visibility, participation, and recognition of contributions from underrepresented genders within the R community. We will discuss our strategies to bring R enthusiasts together, fostering a collaborative environment where individuals can learn from each other. We will share insights on how to maintain an active presence to encourage member engagement. Moreover, we'll provide tips and pieces of advice on how we make our meetups accessible to connect with a broader audience. We will address the resources provided by R-Ladies Global to support the activities of chapters, which could serve as encouragement for you to embark on your R-Ladies journey in the future.

Slides: https://rladiesparis.github.io/rladies_paris_talk_useR2024/

With an increasing number of academic journals requiring authors to submit code, an increasing number of PhD students developing R packages, and more open source packages requiring maintenance, the list of R programming skills required of new quantitative PhD students is ever growing. Many of these PhD students don’t have backgrounds in computer science, but find themselves writing code and developing software on a daily basis. They don’t always have supervisors with backgrounds in computer science either. So how do we help students go from writing spaghetti code, to working with good software development practices? In this talk, I’ll outline what training is currently offered to PhD students, gaps that have been identified (often by students themselves), and a suggestion of how we can better prepare PhD students for quantitative research so that none of them say “If I knew then what I know now, I would have done things entirely differently.”

Do you have a bookshelf full of R books that you’ve been meaning to read, but haven’t gotten around to yet? The Data Science Learning Community (DSLC) can help you achieve your learning goals! You may have heard of us previously as the R4DS Online Learning Community, but we’re about much more than any single book. We organize weekly book clubs to help data science learners and practitioners read books such as R for Data Science, Advanced R, and Mastering Shiny. In contrast with other online book clubs, our safe, nurturing, small-group cohorts finish reading their books cover-to-cover. Come learn the tips and tricks that lead to the success of our clubs. Also learn how we support one another by asking and answering programming questions in our friendly and inclusive Slack help channels, and how we work to ensure that every question receives an answer. Discover how you can help us expand into additional high-level topics by getting the support you need to learn those topics yourself. Whether you’re brand new to R or are a seasoned veteran, we have something for you!

The data.table package in R is a powerful tool for data analysis, combining efficient C code with user-friendly R syntax. To ensure its long-term sustainability, the NSF POSE program has funded a project from 2023 to 2025 to build a self-sustaining ecosystem around data.table. In this presentation, we will discuss the importance of performance testing in the development of data.table and present a general approach that can be applied to other R packages. By creating performance tests based on historical regressions, we can measure the package's efficiency over time and memory usage, ensuring that code and version releases do not impact its performance. We will demonstrate the use of the atime package to benchmark execution time and memory usage, providing developers with confidence in maintaining efficient performance and reliability. This approach not only benefits data.table but also serves as a model for other R package developers to enhance the performance and popularity of their own projects.

In recent years, statisticians and analysts from pharma industry and regulatory agencies have increased adoption of open-source software such as R. R brings great benefits by providing a wealth of cutting-edge statistical tools, extension packages for interactive dashboard and documentation as well as adaptability to the latest data science trends. However, publicly available drug submissions with the R language as the core analysis language has been lacking and limits wider adoption within the Pharma industry. The R Consortium R Submission Work Group seeks to test the concept that a R-based language submission language can be bundled into a submission package and transferred successfully to FDA reviewers. As of May 2024, the R consortium R submissions working group has successfully completed three pilot submissions and received FDA CDER response letters. To our knowledge, these are the first publicly available submission packages that include components of open-source languages. In this talk, I will introduce the R consortium R submission Working Group and the completed Pilot 1, 2 and 3 findings, issues that we encountered, learnings as well as current work being done in Pilot 4.