Rstudio::conf(2020)

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

Many models have structural parameters that cannot be directly estimated from the data. These tuning parameters can have a significant effect on model performance and require some mechanism for finding reasonable values. The tune and workflow packages enable tidymodels users to optimize these parameters using a variety of efficient grid search methods as well as with iterative search techniques (such as Bayesian optimization)

Max Kuhn

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

ML in production is one of the most obvious ways that data science organizations create value in business. However, these models are at the very end of a long story of how quantitative research changes and enhances organizations. In this talk I will discuss how I have found DS organization to be truly transformative outside of ML in the loop. Bio: Eduardo Ariño de la Rubia is a DS manager and educator. He loves R and RStudio. He has a Masters in Negotiation, Conflict Resolution and Peacebuilding, which is probably the most useful training he could have received

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

The ggplot2 package is widely acknowledged as a powerful, dynamic, and easy-to-learn graphics framework when used in an interactive environment

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!

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

How do you make your R Markdown lessons feel friendly for learners you’ll never meet? How do you make it engaging so they sit and stay a while? How do you make it memorable so they come back to visit again? In this talk, I’ll share lessons learned from my experience of making a series of online statistics modules (co-authored by Hasse Walum) that feel accessible and fun– housed entirely in an R Markdown site, complete with a whimsical, illustrated narrative about teacup giraffes. I’ll show how adding good characters with your audience in mind, good design, and good play helped me make the most of HTML output. To help you get started, I’ll share resources that Alison Hill and I have developed–including a series of cookbooks and out-of-the-box templates– so that you will have a leg up on applying these ideas to R Markdown collections of your own

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

This talk will use a case study, most likely in hockey, to showcase the many ways in which R and the Tidyverse can be used to analyze sports data as well as the unique priorities and considerations that are involved in applying statistical tools to sports problems

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

Technical debt is a big problem for the R community. Even though R has excellent support for testing, documentation and packaging code it has the reputation that it is not suitable for production applications because data scientists don’t pay enough attention to technical debt within their codebases. Most people think of technical debt as an engineering problem. We choose to make our current work cheaper at the expense of needing to do more work down the road. But when you look closely at the root causes of technical debt they are almost always about interpersonal relationships. Developers have trouble empathizing with other users of their code and so don’t spend the time to make that code easy for future developers to use and understand. In this talk I argue that we should think about technical debt as a social problem because it gives us insight into why it’s so hard to pay back. I then provide a practical roadmap of how to introduce best practices into your data science team

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

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

Use of R in the investment industry is established and growing. This talk will discuss changes seen in 15 years of practice within asset management firms. I hope discussion of lessons learned and recommendations will benefit those currently in finance and those interested in hearing how the flexibility of R manifests in the financial world

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

The renv package helps you create reproducible environments for your R projects. With renv, you can make your R projects more:

• Isolated: Installing a new or updated package for one project won’t break your other projects, and vice versa.
• Portable: Easily transport your projects from one computer to another, even across different platforms. renv makes it easy to install the packages your project depends on.
• Reproducible: renv records the exact package versions you depend on, and ensures those exact versions are the ones that get installed wherever you go.

In this presentation, I’ll introduce renv and some of its main workflows

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

State of the Tidyverse 2020

Hadley Wickham

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)

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

Why does a psychological scientist learn a programming language? While motivations are many and varied the two most prominent are data analysis and data collection. The R programming language is well placed to address the first need, but there are fewer options for programming behavioural experiments within the R ecosystem. The simplest experimental designs can be recast as surveys, for which there are many options, but studies in cognitive psychology, psychophysics or developmental psychology typically require more flexibility. In this talk I outline the design principles behind xprmntr, an R package that provides wrappers to the a javascript library (jsPsych) for constructing web based psychology experiments and uses the plumber package to call server side R code as needed. In doing so, I discuss limitations to the current implementation and what a “grammar of experiments” might look like

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

Anyone reading a newspaper or listening to the news is led to believe that AI is the solution to all problems. From self-driving cars to detecting disease to catching fraud, there doesn’t seem to be a situation that AI can’t tackle. Once “big data” is thrown into the mix, the AI solution is all but certain. But is AI always needed? Over the last eighteen months, Jumping Rivers has entered (and won) four Hackathons. All Hackathons were characterised with “big data” and the need to improve prediction. All Hackathons were won without using AI (or any sort of machine learning). This talk will focus on one particular competition around reducing leakage at Northumbrian Water. Using a combination of R, Shiny, and Tidyverse (and a few other tricks), we were able to demonstrate within the short Hackathon time frame that clear presentation of data to the front line engineers was more likely to reduce leakage, than simply providing vague estimates of a potential future leak

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

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

Salesforce is not only a cloud software solution out of the box, but also a highly customizable platform that can be modified for a wide range of use cases. In addition to complexity, customer trust is our #1 company value and customer data privacy is abstracted from everyone outside of the customer. Product and Growth Analytics is an emerging field separate from business analytics and data science and focuses on building software product that improve user retention and engagement. Companies like Facebook and AirBnB have robust data science teams focused on product analytics. At Salesforce however, given the scale, customization, and privacy values, product data science is not so straightforward. Utilizing R and Rstudio tools for collaboration and reproducible analytics, the Data Intelligence team is able to solve complex problems at enterprise scale. This talk will preview anonymized predictive and growth analytics work while also highlighting how we work and collaborate cross platform and languages (Python via reticulate)

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

Many R users can feel isolated due to the prevalence of Python or Tableau at their institutions. This talk will focus on how we use R to develop reference implementations of visualizations (using ggplot2), and to develop corporate-themed color maps (using the colorspace package) to bring value to the entire institution. Color maps can be translated into variety of formats, for Tableau, Qlik Sense, d3, etc., and deployed independently from R. For visualizations, our goal is to translate ggplot2 objects to Vega-Lite specifications, using a package we are developing: ggvega. Vega-Lite visualizations are web-native, and are rendered independently from R. Specifications can be designed to be extensible to new data, allowing them serve as templates, to be deployed and updated for use outside of R. Of course, despite isolation within an institution, our work with the larger R open-source communities provides a foundation on which to build; in fact, we have a lot of company and are having a lot of fun

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

The ggplot2 package continue to be one of the most used frameworks for producing graphics in R. While being extremely flexible, the package itself can be constrained by the different types of graphic elements and statistic transformations available. Instead of continuing to add new features, the development in recent years have focused on making ggplot2 extensible by other packages, thus distributing development and maintenance. Despite the best of intentions, ggplot2 can feel daunting to extend, due unusual idiosyncrasies, a foreign object system, and a partly obscured rendering model. This talk intend to remove the mystery of extending ggplot2, by describing the basic ways that it can be extended and showcasing a couple of simple extensions that can be build with very little code. Lastly, it will include discussions of some best practices and gotchas that may come in handy when you start out

Thomas Lin Pedersen

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

The use of list-columns in data frames and tibbles is well documented (e.g. Bryan, 2018), providing a cognitively efficient way to organize results of complex data (e.g. several statistical models, groupings of text, data summaries, or even graphics) with corresponding data. For example, one can store student information within classrooms, player information within teams, or analyses within groups. This allows the data to be of variable sizes without overly complicating or adding redundancies to the structure of the data. In turn, this can improve the reliability to appropriately analyze the data. Because of its efficiency and speed, being able to use data.table to work with list-columns would be beneficial in many data contexts (e.g. to reduce memory usage in large data sets). Herein, I demonstrate how one can create list-columns in a data table using the by argument in data.table and purrr::map(). I compare the behavior of the data.table approaches to the dplyr::group_nest() function and tidyr::unnest(), two of the several powerful Tidyverse nesting and unnesting functions. Results using bench::mark() show the speed and efficiency of using data.table to work with list-columns

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

Future is a minimal and unifying framework for asynchronous, parallel, and distributed computing in R. It is designed for robustness, consistency, scalability, extendability, and adoptability - all in the spirit of “developer writes code once, user runs it anywhere”. It is being used in production for high-performance computing and asynchronous UX, among other things. In this talk, I will discuss common feature requests, recent progress we have made, and what is the pipeline

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

Shiny makes it easy to take domain logic from an existing R script and wrap some reactive logic around it to produce an interactive webpage where others can quickly explore different variables, parameter values, models/algorithms, etc. Although the interactivity is great for many reasons, once an interesting result is found, it’s more difficult to prove the correctness of the result since: (1) the result can only be (easily) reproduced via the Shiny app and (2) the relevant domain logic which produced the result is obscured by Shiny’s reactive logic. The R package shinymeta provides tools for capturing and exporting domain logic for execution outside of a Shiny runtime (so that others can reproduce Shiny-based result(s) from a new R session)

Carson Sievert

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

“The Data Science community is dominated by folks doing amazing work with data that starts in and never leaves cyberspace. This talk is about best practices and playbooks for doing data science that involves meatspace (the opposite of cyberspace) and why R is such a great language for working with data that originated in the physical world. While the concrete examples in this talk will mostly come from the manufacturing space, where I have the most experience, I believe the themes are relevant to many meatspace workflows. We’ll talk through effective playbooks that can help you navigate common tasks throughout the life-cycle of a project. We’ll also weave in how R’s glorious package ecosystem, including Tidyverse, can be combined with other languages like python, and with enterprise products like RStudio Connect to great effect. Specifically, we’ll discuss practices in these areas:

best practices for data collection in meatspace the importance of quantifying measurement system error collecting the correct data for training computer vision models the rarely discussed cost of maintaining models in production”

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

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

This talk is for R admins who want to learn how to set up Jupyter notebooks on RStudio Server Pro. We’ll cover prerequisites, basic configuration, best practices for management, Jupyter Lab, and more

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

David Smith | January 31, 2020 Azure Machine Learning service (Azure ML) is Microsoft’s cloud-based machine learning platform that enables data scientists and their teams to carry out end-to-end machine learning workflows at scale. With Azure ML’s new open-source R SDK and R capabilities, you can take advantage of the platform’s enterprise-grade features to train, tune, manage and deploy R-based machine learning models and applications. In this talk, the attendees will learn how to: •Carry out ML workflows using the authoring experience of their choice, from no-code to code-first options that include Azure ML’s drag-and-drop visual interface for defining workflows and RStudio Server on the Data Science Instance, a hosted VM workstation, for using the Azure ML R SDK from the RStudio browser-based interface. •Use the Azure ML R SDK to manage cloud resources and train, hyperparameter tune, and log and visualize metrics for their models at scale on Azure compute. •Build ML Pipelines in R for defining and orchestrating reusable and reproducible ML workflows. •Deploy, manage, and monitor their R ML models and applications as web services on Azure Container Instance and Azure Kubernetes Service, with an emphasis on robust DevOps and CI/CD for orchestrating and streamlining their end-to-end data science development lifecycle

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

The InsightRX precision dosing platform tailors in-patient drug doses to individual patients’ characteristics and biomarkers, leveraging pharmacological models of drug metabolism and drug effects. These models are implemented in R, exposed through APIs, and called from a cloud-based web application

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

The Apache Arrow project is a cross-language development platform for in-memory data designed to improve system performance, memory use, and interoperability. This talk presents recent developments in the ‘arrow’ package, which provides an R interface to the Arrow C++ library. We’ll cover the goals of the broader Arrow project, how to get started with the ‘arrow’ package in R, some general concepts for working with data efficiently in Arrow, and a brief overview of upcoming features

Neal Richardson

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

What should you name a new dinosaur discovery, according to neural networks? Which season of The Golden Girls should you watch when playing a drinking game? How can you build a LEGO set for the lowest price? R is constantly evolving, so as users, we’re constantly learning. Over the past few years, I’ve found that working on side projects is great for hands-on learning - and for me, the more absurd the project, the better. Side projects provide a safe, low-stakes environment to learn new packages and methodologies before using them in work or in production. Sharing those projects can help publicize the package and increase its accessibility, benefiting both the original author and future users. In this talk, I’ll share my experiences with side projects for learning state-of-the-art data science tools and growing as an R user, including how one project helped me land my dream job

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

My 8th-grade capstone project introduced me to R. The project was a data visualization about breakfast tacos. I used R and other web-based tools. My lightning talk will focus on my experience about using R for class projects and getting support from my parents to help integrate R into the classroom. I will show how students can get started when they don’t know how to use R. I will talk about the project’s toolkit, which includes Posit Cloud, Google Sheets, Chromebook, measurement tools, my phone, and how R is being used in my school. A 5-minute presentation in our Lightning Talks series

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

Steve Weston’s foreach package defines a simple but powerful framework for map/reduce and list-comprehension-style parallel computation in R. One of its great innovations is the ability to support many interchangeable back-end computing systems so that the same R code can run sequentially, in parallel on your laptop, or across a supercomputer. Recent new packages like future package define elegant new programming approaches that can use the foreach framework to run across a wide variety of parallel computing systems. This talk introduces the basics of foreach and future packages with examples using a variety of back-end systems including MPI, Redis and R’s default parallel package clusters

Gergely Daroczi | Getting things logged | RStudio (2020)

One of the greatest strengths of R is the ease and speed of developing a prototype (let it be a report or dashboard, a statistical model or rule-based automation to solve a business problem etc), but deploying to production is not a broadly discussed topic despite its importance. This hands-on talk focuses on best practices and actual R packages to help transforming the prototypes developed by business analysts and data scientist into production jobs running in a secured and monitored environment that is easy to maintain – discussing the importance of logging, securing credentials, effective helper functions to connect to database, open-source and SaaS job schedulers, dockerizing the run environment and scaling infrastructure

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

Longitudinal data (or panel data) arise when observations are recorded on the same individuals at multiple points in time. For example, a longitudinal baseball study might track individual player characteristics (team affiliation, age, height, weight, etc.) and outcomes (batting average, stolen bases, runs, strikeouts, etc.) over multiple seasons, where the number of seasons could vary across players. Neural network frameworks such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs) can flexibly accommodate this data structure while preserving and exploiting temporal relationships. In this presentation, we highlight the use of neural networks for longitudinal data analysis with tensorflow and keras in R

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

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

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

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

Learn how a single line of code can transform your data visualizations into stunning 3D using the rayshader package. In this talk, I will show how you can use rayshader to create beautiful 3D figures and animations to help promote your research and analyses to the public. Find out how to use principles of cinematography to take users on a 3D tour of your data, scripted entirely within R. Leaving the 3D pie charts in the pantry at home, I will discuss how to build interpretable, engaging, and informative plots using all three dimensions

Katie Masiello | Professional Case Studies | RStudio (2020)

The path to becoming a world-class, data-driven organization is daunting. The challenges you will likely face along the way can be thorny, and in some cases, seem outright impossible to overcome. How do you get teams that traditionally butt heads, such as IT and data science, to complement each other and work in unison? How can you efficiently scale the scope and reach of your data products as requirements change? Your time should be spent doing truly valuable work instead of updating charts and reports. How do you prevent the support structure behind your platform from toppling like a house of cards? Despite these challenges, we think that the end result is worth it: an organization that is equipped to make important decisions, with confidence, using data analysis that comes from a sustainable environment. We see this outcome every day

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

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

TensorFlow is the most popular open-source platform for machine learning and it’s ecosystem is evolving incredibly fast. In this talk we will explore what’s new in TensorFlow 2.0 as well as how to build data pre-processing pipelines using the tfdatasets package and how to use pre-trained models with tfhub

Daniel Falbel

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

Geospatial analysts work in a wide range of positions within almost every industry. They work in government, non-profit, academic, and private institutions using geospatial data and technology to answer questions about the environment, agriculture, climate, urban planning and design, marketing, public health, transportation, and myriad other topics. A typical day may include data prep/cleaning, field work, cartography, image analysis, vector analysis, feature engineering, modeling, or database management. This diverse group necessarily uses a diverse set of tools. In this talk, we will explore how R fits into the spatial analyst’s toolkit. What does the geo community think of R? Who uses it? What groups avoid it? What geo-packages are used most? How can we, as a community, make R more appealing for geospatial scientists?

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

In Tidyverse grammars such as dplyr you can refer to the columns in your data frames as if they were objects in the workspace. This syntax is optimised for interactivity and is a great fit for data analysis, but it makes it harder to write functions and reuse code. In this talk we present some advances in the tidy eval framework that make it easier to program around Tidyverse pipelines without having to learn a lot of theory

Lionel Henry

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’

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

R has changed a lot since the meetup was founded 10 years ago. Back then we were using base graphics (or lattice) and the apply family of functions and we didn’t have pipes. At the time there was an impressive 1800 packages on CRAN, now there are over 15,000 extending R’s reach far beyond its traditional domain of statistics and machine learning into publishing, website building and video generation. The community has grown and changed dramatically during that time, with the New York meetup alone going from 25 to over 10,000 members. During this talk we go through a then-and-now of R code and community to palpably see how everything has changed

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

I see a lot of ugly charts. This is to be expected as I work with a lot of academics and data scientists, neither of whom have been trained in how to design attractive charts. I myself produced many ugly charts during my years as a research scientist, when the design process basically came down to random tweaking until things “looked good”. If only I could go back and tell young inexperienced me that there was a better way. In this talk, I will present that better way–a series of design principles that can take any chart from drab to fab. Rather than applying these techniques willy nilly, I will show how they form a layered “Glamour of Graphics” that is structured and can be easily applied to any chart. This Glamour of Graphics has some simple implementations in ggplot, where we will replace geoms, aesthetics, and scales with typography, color, and layout. Finally, I will discuss why looks matter when it comes to charts, and how by following the Glamour of Graphics you can design charts that are more persuasive and more accurately perceived

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

The idea of rpanda commodities trading simulation was many years in the making. As energy trading professionals working in the industry, we had developed insights around how to make risk/reward market calls, and what skills make someone an exceptional commodities trader. Traders are one of the most expensive seats in terms of monetizing value from the assets. We developed rpanda as a simulated environment which replicates closely how real-life physical commodities trading works in order to assist talent development and selection, both in academics and enterprise. My co-founder and I did not know how to design production-ready software, but we always had used R/Shiny for market analysis in our corporate jobs. Rather than hiring expensive app developers, we decided to do it ourselves. We used Rstudio development stack such as Rstudio Connect and open source tools, like plumber to turn our idea into a production-ready app that is used by University of Alberta classes. In this presentation, we share our journey, technical challenges, and how we overcame them

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

More people are learning data science every day, and there are more ways for them to learn than ever before. To understand where we are and where we might be going, this talk looks at what data science education could look like two years from now: far enough away that we can dream, but close enough that we can only dream a little. We explore the balance between automated and collaborative learning, different ways to deliver different kinds of lessons to different kinds of people, and ways in which our tools and practices could improve

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

At RStudio, we wake up and go to bed thinking about the positive impact that open source work and data science has had and can have on the world. To maximize this impact, we find three areas of investment absolutely critical to ensure our open source community keeps up with the world’s changes and outlives us all: 1. Find ways to make R more approachable. 2. Enable teams of all types & sizes (educational, professional, etc.) to be able to leverage the work they’re doing in R, and effortlessly communicate that work to others. 3. Extend the language so our open-source community can continue to be at the forefront of innovation, no matter their preference of tool or language

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

The base R types of vectors enable the representation of an amazingly wide array of data types. There is so much you can do with R. However, there may be times when your data does not fit into one of the base types and/or you want to add metadata to vectors. vctrs is a developer-focused package that provides a clear path for creating your own S3-vector class, while ensuring that the classes you build integrate into user expectations for how vectors work in R. This presentation will discuss the why and how of using vctrs through the example of debkeepr, a package for integrating historical non-decimal currencies such as pounds, shillings, and pence into R. The presentation will provide a step-by-step process for developing various types of vectors and thinking through the design process of how vectors of different classes should work together

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

Even though I’ve completed 4 marathons, you certainly shouldn’t come to me for a training plan on how to achieve your goals for any race you’re about to run. So why do we often turn to “experienced R users” to help us learn R or train an organization? The RStudio certified trainers have been taught modern, evidence-based teaching practices which they use in planning training sessions in order to help delegates achieve THEIR learning goals effectively in a given time-frame. My talk will illustrate some of these teaching concepts and how, by becoming a certified trainer, you can help others learn about R more effectively. A 5 minute presentation in our Lightning Talks series

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

In this talk, I will introduce a suite of three packages designed to aid course material creation in R: {demoR} for displaying code in knitted R Markdown with custom highlighting and formatting; {shindig} for shortcuts to creating simple educational Shiny apps; and {curricular} for easy creation of syllabi, homework exercises, exams, etc. Together, we will explore how these new tools - in conjunction with other existing resources - have been used to create a clean and consistent ecosystem for my R-based Introductory Statistics course. I will share some metrics on student outcomes, as well as my own experiences with the advantages and challenges in building the course. A 5-minute presentation in our Lightning Talks series

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

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

I host a weekly R Office Hour on the R4DS Online Learning Community Slack. By doing so, I have learned more about R than I ever would have thought. Here I'll present concrete examples of how R users can participate in the R community to expand their skills. R users of all skill levels can develop their skills by helping one another learn. Committing to help people with their coding challenges leads to the exploration of answers in areas you might otherwise not examine. 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

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

In this talk, I will outline a unified philosophy of data science education, and provide tips and tools for implementing these principles in the classroom using R and RStudio. Although data science as a professional discipline is well-established, its pedagogy is still in a period of growth. Even within a single university, multiple data science courses may be offered across different departments leading to inevitable redundancy of efforts amidst rich domain-specific innovations. My experience as an instructor in many such courses has lead me to five principles that transcend domain, context, and choice of language: reproducibility, communication, version control, practical application, and data ethics. For each of these full-stack themes, I will share examples of how to leverage tools in R and RStudio to enhance learning. A 5-minute presentation in our Lightning Talks series

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

After at least a year of dreaming about it, I finally produced the #rstats / #Tidyverse dress of my dreams. This involved designing fabric, getting it custom printed, making a pattern from an existing garment, and sewing the dress. I learned a lot of useful lessons during this project, including "do unit tests" (make a practice dress) and "document your work" (get your BFF to take pictures of you)

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

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

In this talk we will demonstrate livecode, a new R package for broadcasting code for live code demonstrations. This package implements a simple webserver (using httpuv) to dynamically publishes the content of a code file (i.e. .R or .Rmd) as you edit it live. This enables your students to have near realtime access to your code as you write it. The broadcast file can be viewed with any webbrowser but the package is specifically designed to be used within RStudio leveraging its builtin viewer. This gives students have direct access to the shared code within the IDE, allowing direct copying into their own source files and/or the console and thereby improving their ability to interact and experiment with your code. 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

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

We observed a huge improvements of Machine Learning tools but the main effort were to help at post annotated dataset step. We still struggle to build a trusty pipeline to make these annotations. The package wavesurfer brings to R users the ability to annotate audio files with ease and reliability, exploring the friendly user interface of Shiny to make this hard and laborious part of the project more joyful and efficient. A 5-minute presentation in our Lightning Talks series

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

Forming good development habits for R projects is pretty straight-forward if you follow the lessons I’ve learned from my cat, whose advice includes “be lazy”, “keep your claws sharp”, and “land on your feet”. Attendees of this talk will learn how to make life easier on colleagues and their future selves by using simple software engineering best practices to build their current projects. Each point will come with cat photos and code samples, the two best parts of the Internet!

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

Your first “object of type ‘closure’ is not subsettable” error message is a big milestone for an R user. Congratulations, if there was any lingering doubt, you now know that you are officially programming! Programming involves considerably more troubleshooting and debugging than many of us expected (or signed up for). The ability to solve your own problems is an incredibly powerful stealth skill that is worth cultivating with intention. This talk will help you nurture your inner problem solver, covering both general debugging methods and specific ways to implement them in the R ecosystem

Jenny Bryan

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

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

The ggtext package provides various functions to add formatted text to ggplot2 figures, both in the form of plot or axis labels and in the form of text labels or text boxes inside the plot panel. Text formatting can be achieved through a small subset of markdown, HTML, and CSS directives. Features currently supported include italics, bold, super- and sub-script, as well as changing font size, font family, and color. Basic support for adding images to formatted text is also available

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

Open source R packages on GitHub often take advantage of continuous integration services to automatically check their packages for errors. This is very useful to catch things quickly, as well and increasing confidence for proposed changes, as the Pull Requests can be checked before they are merged. Travis-CI and Appveyor are the most popular current methods. However newer services, Azure Pipelines and GitHub Actions, show promise for being more powerful and simpler to configure and debug. I will discuss these services and demonstrate some of their capabilities and how to configure them for your own use in packages and reports

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

The Associated Press data team primarily uses R and the Tidyverse as the main tool for doing data processing and analysis. In this talk, some of the technology behind the published stories will be showcased: - Using dbplyr to work off a hosted database containing 380 million opioid records to identify “pill mills”. - Using open-sourced AP style templates for R Markdown and ggplot to quickly produce graphics and reports off breaking news. - Using R Markdown and htmlwidgets to give reporters and editors interactive reports to identify reporting leads

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

Talk from rstudio::conf(2020)

We have recently implemented a new Data Science workflow and pipeline, using RStudio Connect and Google Cloud Services. This has vastly decreased our pipeline complexity, allowing us to bring our models and products into scheduled production more quickly. In addition, our workflow, working closely together as a team on all projects on a regular two-week sprint cycle, has increased the range of projects we have been able to take on and complete. To detail some of the key lessons we’ve learned (and some of the difficulties!), we’ll walk you through one of our recent sprints, where we productionalised the generation of a suite of behavioural and demographic features so that they can be more easily plugged in to a range of models and used across the business by the FT’s platform and product teams

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

Ian Cook | Bridging the Gap between SQL and R | RStudio (2020)

Ian Cook | January 31, 2020 Like it or not, SQL is the closest thing we have to a universal language for working with structured data. Celebrating its 50th birthday in 2020, SQL today integrates with thousands of applications and has millions of users worldwide. Data analysts using SQL represent a large audience of potential R users motivated to expand their data science skills. But learning R can be frustrating for SQL users. One major frustration is the inability to directly query R data frames with SQL SELECT statements. Eager to use R for tasks that are not possible with SQL (like data visualization and machine learning), these users are dismayed to find that they must first learn an unfamiliar syntax for data manipulation. The popularity of the sqldf package (which automatically exports an R data frame into an embedded database, then runs a SQL query on it) demonstrates this frustration. But now there is a way to directly query an R data frame without moving the data out of R. In this talk, I introduce tidyquery, a new R package that runs SQL queries directly on R data frames. tidyquery is powered by dplyr and by queryparser, a new pure-R, no-dependency SQL query parser

Winston Chang | Asynchronous programming in R | RStudio (2020)

Writing regular R code is straightforward: you tell R to do something, it does it, and then it returns control back to you. This is called synchronous programming. However, if you use R to coordinate threads, processes, or network communication, the regular model may be unable to do what you want, or it may only be able to do it with a significant performance penalty. In this talk I'll explain how asynchronous programming with the later package can handle these kinds of programming problems. I'll also show how to provide a synchronous interface for asynchronous code, so that users will have a simple, familiar way to use your code. Materials - github repo

Winston Chang

Nick Strayer | Stochastic Block Models with R: Statistically rigorous clustering | RStudio (2020)

Nick Strayer | January 31, 2020 Often a machine learning research project starts with brainstorming, continues to one-off scripts while an idea forms, and finally, a package is written to disseminate the product. In this talk, I will share my experience rethinking this process by spreading the package writing across the whole process. While there are cognitive overheads involved with setting up a package framework, I will argue that these overheads can serve as a scaffolding for not only good code but robust research practices. The result of this experiment is the SBMR package: a native R package written to fit and investigate the results of Bipartite Stochastic Block Models that forms the backbone of my PhD dissertation. By going over the ups and downs of this process, I hope to leave the audience with inspiration for moving the package writing process closer to the start of their projects and melding research and code more closely to improve both

Nick Strayer

J.J. Allaire | Open Source Software for Data Science | RStudio (2020)

Open Source Software for Data Science

J.J. Allaire | February 1, 2020 Open-source software is fundamentally necessary to ensure that the tools of data science are broadly accessible, and to provide a reliable and trustworthy foundation for reproducible research. This talk will delve into why open source software is so important and discuss the role of corporations as stewards of open source software. I’ll also talk about how RStudio is structured and organized to pursue its mission of creating open source software for data science.

About the speaker J.J. Allaire - JJ Allaire is a software engineer and entrepreneur who has created a wide variety of products including ColdFusion Open Live Writer Lose It! and RStudio

JJ Allaire