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Increasing quantity and types of available data and the need for more advance analyses are outpacing current tools in environmental science. R allows us to …
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How does a US federal agency analyze tens of millions of records across 30,000 sites in over 50 countries analyze these data efficiently and effectively? Five …
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This talk will trace the evolution of a report from a mostly text free dashboard into a text heavy R markdown report with dynamic text blocks. The report in …
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California Integrated Travel Project’s mission is to make transit across California simpler and more affordable. As part of this, we created an open source data …
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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 …
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Internal packages are great for boosting productivity and promoting good practice, but what kinds of challenges do we face when designing solutions for …
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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 …
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Power BI is a multi-faceted business intelligence tool with built-in data ingestion, transformation, and visualization features. The learning curve for Power BI …
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It happens to us all - a request to "just re-run the code" turns into a project nightmare. The materials left to you are poorly documented and …
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The Belmaker lab fieldwork involves underwater surveys where we document the observed marine species. Historically, Microsoft’s Access was used for data input. …
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Some of my best friends use Python...and all of my coworkers use SAS.
Statistics Canada is the official statistical agency of Canada and employs over 6,000 …
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Many companies within the insurance and financial sector maintain a large number of legacy models in the platform Microsoft Excel. With the advent of data …
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My organization currently has over 250 oceanographic sensors deployed around the coast of Nova Scotia, Canada. Together, these generate around 4 million rows of …
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Users often want access to more advanced analytic capabilities in their Tableau dashboards. Together we will walk through an example that highlights how new …
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There are many benefits to using R and no lack of packages that help you solve technical difficulties, but you may still get stuck at selling it to …
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Are you a bilingual data scientist who wants to use Python in RStudio products? Or do you have colleagues who use Python for data science? In this talk, we will …
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Are you curious about Quarto? Maybe you saw it on Twitter or the RStudio::conf agenda. Perhaps this raised questions like: What exactly is Quarto? What about …
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Jupyter Notebooks play a critical role in in the workflow of many users. Notebooks are used to document existing code, to quickly prototype and iterate on …
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A number of packages have emerged in the R ecosystem to help create websites (hugodown), books (bookdown), and blogs (blogdown). In this talk, I'll show you how …
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With the Dutch Mortgage Portfolio Management Group we built a client portal for their customers wanting insights into their mortgage investment portfolio. This …
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Shiny for Python has the ability to be deployed without a server that runs Python. These applications can be served by any web hosting service. They are easily …
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Sometimes, we want a model that generates a range of possible outcomes around each prediction. Other times, we just care about point predictions and may opt to …
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In this talk I introduce webR, a port of R to WebAssembly using Emscripten. WebR brings a full R environment to the browser, enabling R code execution, …
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The ‘arrow’ R package and wider Apache Arrow ecosystem provide an end-to- end solution for querying and computing on in-memory and bigger-than-memory data sets …
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I propose a talk on visualizing distributions and uncertainty using {ggdist}. I will describe how to think systematically about distributional visualization as …
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I would like to create (more) Shiny Dashboards, but...
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Shiny helps data scientists create web applications without requiring web development experience. However, there's still a steep learning curve for writing the …
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For production-grade Shiny applications, regression testing ensures that the application maintains its core functionality as new features are added to the app. …
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This talk reports on a head-to-head comparison of the formula and tidyverse syntaxes in a full semester introductory statistics course, providing data to help …
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As shiny developers, we spend a lot of time working on the server side, and less time on the UI/UX part. According to the Engineering Production-Grade Shiny …
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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 …
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Shiny users can prototype an app in minutes, but... What if it also looked better almost immediately? In this talk, we'll walk through a new look for Shiny's …
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This talk marks the grand introduction of tidyclust, a new package that provides a tidy unified interface to clustering model within the tidymodels framework. …
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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 …
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This summer the RStudio Connect team will announce a feature which has been over two years in the making: “Remote” off-host content execution with launcher in …
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Dealing with date-times is hard. Dealing with date-times without the proper tooling is even harder! clock is an R package that aims to provide comprehensive and …
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R has come quite a long way to enable spatial analysis over the past few years. Packages such as sf have made spatial analysis and mapping easier for many. …
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If you've ever tried to run a workshop using R, you'll be aware of the challenges of getting everyone's laptop set up to able to run your R scripts, Rmarkdown …
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Society benefits when leaders make more evidence-based decisions, but growing privacy concerns hamper researchers’ ability to understand and improve the world. …
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tidymodels is extending support for survival analysis and censored is a new parsnip extension package for survival models. It offers various types of models: …
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Manually testing Shiny applications is often laborious, inconsistent, and doesn’t scale well. Whether you are developing new features, fixing bug(s), or simply …
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I will share how we published an R Shiny application to AWS, the decisions we made, and what we learned in the process.
One challenge we faced was figuring out …
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If you do data analysis, you encounter missing data. Missing data upsets data analysis workflow because you have to make decisions on how to deal with it - do …
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Data scientists have an intuition of what goes into training a machine learning model, but building an MLOps strategy to deploy that model can sound daunting …
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R and Shiny are ready for production use in Enterprise. As Appsilon, we have worked with tens of top global companies on their Shiny projects. Emphasis on UI, …
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Interactive maps are indispensable tools for exploring spatial datasets because of their real-world context and intuitiveness. For a comprehensive understanding …
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Slack is a useful communication tool for communities and businesses. Integrating it with Shiny can make it even more useful. I'll show how my {shinyslack} …
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Even when we run the simplest of R scripts, we are using dozens of R packages. We use packages for data cleaning, writing reports, graphics and modelling. One …
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Attendees will recieve a broad overview of the encoding and decoding process in the human-to-computer loop, how bits are used, and the math that gets us to …
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This talk is an introduction to GitHub Actions (GHA), which is a feature from GitHub that allows us to automate several tasks in R. In this presentation, I aim …
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Multilingual shiny apps are not straightforward to build. Translation affects almost every single aspect of an app. Although there are a few packages designed …
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Machine learning models, applied in the real world, can have unanticipated, harmful side effects. Recommended counter-measures include structured documentation …
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R is often maligned as a poor fit for production deployment systems. At dv01 we deploy Plumber API that serves machine learning models to Tape Cracker, a client …
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In January 2021, Alaska residents seeking a COVID-19 vaccine appointment faced a convoluted maze of websites. The software was made for providers—not for …
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Data suggests that less than 3% of data scientists are women of color. My journey and that of many other women who fall at the intersection of being …
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In this presentation, I will share my experiences at the intersection of the R and Kaggle communities. As Kaggle's first Notebooks Grandmaster, I will talk …
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The California Department of Public Health’s (CDPH) COVID-19 response has required processing and communicating large amounts of data with quick turnaround …
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This session highlights two anomaly detection use cases in production: identification of problematic life sciences manufacturing units and identification of …
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R users are part of data ecosystems comprising both statistical and non- statistical applications. We may work with SAS or Stata datafiles; non-R users may help …
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During the pandemic, epidemiologists have been forced to adapt to the unprecedented scale of the data and high cadence of reporting.
At the UK Health Security …
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The Carpentries is a global community of volunteers who collaboratively develop and deliver lessons to build capacity in data and coding skills to researchers …
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When someone asks about essential skills for data careers, I often hear responses like R, Python, and machine learning. However, I argue that creativity is an …
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In 2022, African countries need to vaccinate most of their population against COVID-19. With an influx of millions of doses, countries need to plan in near …
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Back in 2020, Atorus first released our package Tplyr. The aim of Tplyr was to build a reusable framework that makes all the data preparation for clinical …
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Getting involved in open source is an amazing learning experience and helps you grow your skills as a developer, but to a new contributor there are so many …
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In this talk I want to explore R's capabilities for fast, interactive graphical applications. This exploration is driven by my ongoing port of the 1991 action …
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Data Scientists have a unique position to drive change and efficiency within organizations workflows. By simplifying workflows to its core expectations, we can …
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As R users, we make choices daily about what packages to use in our work. After discovering a package that may suit our needs, we consider its qualities and …
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Data literacy is a tool to build understanding- of the world and ourselves. Data, AI and tech are sometimes portrayed as scary and unknowable; however, data can …
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Lots of people I meet want to start their own business. "I know how to use R," they figure, "so I should be able to go out on my own, find …
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In community conversations at the Data Science Hangout, we’ve talked about misalignment between what recruiters are looking for and who is actually a great fit …
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Have you ever had so much fun building a data visualization that it felt like a thrill ride? What if your dataviz actually WAS a thrill ride—a 3D virtual …
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Enterprise-Level Data Science Success includes many factors beyond the nuts and bolts of core data science work. It is not just about data, databases, data …
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This talk presents a gathering of resources from the RStudio community for industry job-seekers who are transitioning from academia. Examples include packages …
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If you find yourself waiting hours for your queries to run, this talk is for you.
Learn from my query mistakes and avoid crashing your database.
In this talk, …
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Dependencies don't have to be hell. In this talk we'll discuss how renv makes it easier to diagnose problems, move projects between environments, and …
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After a visit to the ER, I discovered an ocean of personal data: more than 3 million rows of data about one of my favorite subjects: me. My watch averages …
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It can be difficult for small teams to make an impact in large organizations. In this talk I will discuss how my small team, at the National Institute of …
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No matter the requirements of the project, data are rarely ready for analysis without some intervention up front, often described as cleaning or tidying up your …
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Demystifying the Art of creating custom Libraries for your organization. Imagine a world where a company has its own R library, this stores the most common …
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I'll discuss designing a socially-conscious and socially-critical data science course. This talk will be interesting to anyone who designs or delivers …
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R is more than just a tool for data analysis– it can help streamline and automate processes, including managing and monitoring data pipelines. This presentation …
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Is it possible to do meaningful work in R on a $35 computer? How about a $15 computer? And what does that mean for education, data science, and computing on …
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The inner workings of {ggplot2} are difficult to grasp even for experienced users because its internal object-oriented (ggproto) system is hidden from user- …
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It can be daunting to start using R when no one else in your office is! Using a case study from an administrative higher education office, learn how you can …
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The R7 package is a new OOP system designed to be a successor to S3 and S4. It has been designed and implemented collaboratively by the RConsortium Object- …
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The first step of any data analysis is importing data, but for tables in a database this can be a surprisingly challenging step that takes analysts out of their …
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This talk discusses how we used R to solve some of the challenges we faced when all classes were emergency onlined as a result of COVID. Instructors improvised …
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I'll walk through a few potential uses of parsing out the functions and packages in projects.
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Over the years, the R community has experienced an increase in the number, diversity and domain background of users. However, incorporation of R in the field of …
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The friendly competition between R and python has gifted us with two stellar packages for workflow-style predictive modeling: tidymodels in R, and scikit- learn …
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E-commerce requires passing data between many components like managing a shopping cart, taking payment, fulfilling orders, and sending emails. I've successfully …
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If you've invested a lot of time and energy on a data science project, you might be ready to move on to new and exciting things. Don't let your old projects …
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dm bridges the gap in the data pipeline between standalone data frames and relational databases. Implementing a "grammar of joined tables", it …
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When people first learn about R’s capabilities to create fully integrated systems, automated visuals, and seamless data pipelines, the reaction can span from …
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