Talks from our 2020 conference in San Francisco, CA
Open Source Software for Data Science - J.J. Allaire
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.
Object of type ‘closure’ is not subsettable - Jenny Bryan
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...
Welcome to rstudio::conf 2020
We opened rstudio::conf with the story of Pim Bongaerts, Data Scientist and Marine Biologist at the California Academy of Sciences.
Not So Standard Deviations Episode 100 - Roger Peng & Hilary Parker
In episode 100 of Not So Standard Deviations, the first ever episode prepared in advance, Hilary and Roger discuss creativity, its role in data science, & how it can be fostered through conversation.
Panel: Career Advice for Data Scientists - Jen Hecht
This panel will be focused on how you build a career around R! Our panelists are all passionate about R and have each taken a different path to build a career around that passion.
Styling Shiny apps with Sass and Bootstrap 4 - Joe Cheng
Customizing the style--fonts, colors, margins, spacing--of Shiny apps has always been possible, but never as easy as we’d like it to be.
Total Tidy Tuning Techniques - Max Kuhn
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...
State of the tidyverse - Hadley Wickham
Effective Visualizations - Miriah Meyer
Reproducible Shiny apps with shinymeta - Dr. Carson Sievert
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...
Making the Shiny Contest - Mine Çetinkaya-Runde
In January 2019 RStudio launched the first-ever Shiny contest to recognize outstanding Shiny applications and to share them with the community. We received 136 submissions...
Practical Plumber Patterns - James Blair
Plumber is a package that allows R users to create APIs out of R functions. This flexible approach allows R processes to be accessed by toolchains and frameworks outside of R.
Stochastic Block Models with R: Statistically rigerous clusting with rigorous code - Nick Strayer
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.
Data, visualization, and designing AI - Fernanda Viegas & Martin Wattenberg
Recent progress in machine learning has raised a series of urgent questions: How can we train and debug deep learning models? How can we understand what is going on inside a neural network?
Neural Networks for Longitudinal Data Analysis - Dr. Sydeaka Watson
Longitudinal data (or panel data) arise when observations are recorded on the same individuals at multiple points in time.
3D ggplots with rayshader - Dr. Tyler Morgan-Wall
Learn how a single line of code can transform your data visualizations into stunning 3D using the rayshader package.
The Glamour of Graphics - William Chase
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.
MLOps for R with Azure Machine Learning - David Smith
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.
R for Graphical Clinical Trial Reporting - Frank Harrell
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.
Toward a grammar of psychological experiments - Danielle Navarro
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.