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rstudio::conf(2022)

#1

Good practices for applied machine learning - from model development to model deployment

#2

Hello Quarto: Share • Collaborate • Teach • Reimagine

#3

The Past and Future of Shiny

#4

Quarto for the Curious

#5

A new way to build your Shiny app's UI

#6

Running Shiny without a server

#7

Data science training in communities with limited technology resources and opportunities

#8

An introduction to R7

#9

Evening Dinner & Activities

#10

Making Data Pipelines in R: A Story From A “Self-Taught” Perspective

#11

R, Python, and Tableau: A Love Triangle

#12

R Markdown + RStudio Connect + R Shiny: A Recipe for Automated Data Processing, Error Logging, and Process Monitoring

#13

Lunch

#14

Breakfast

#15

Welcome Reception

#16

You should be using renv

#17

Designing for people is hard

#18

Exploratory Spatial Data Analysis in the tidyverse

#19

Demystifying MLOps

#20

The Future of missing data

#21

These are a few of my favorite things (about Quarto presentations)

#22

Making awesome automations with GitHub Actions

#23

tidyclust - expanding tidymodels to clustering

#24

Project Immortality: Using GitHub To Make Your Work Live Forever

#25

Highlights of the knitr package from the past two years

#26

Garbage Data, And What To Do About Them

#27

Dashboard-Builder: Building Shiny Apps without writing any code

#28

Breakfast

#29

Lunch

#30

censored - Survival Analysis in tidymodels

#31

It's about time

#32

dbcooper: Turn any database into an R or Python package

#33

Creating a Design System for Shiny and RMarkdown

#34

Lunch

#35

Breakfast

#36

The R integration in Power BI: Why R developers should be Interested

#37

Do It For Yourself: Creating a data input platform using R

#38

Visualizing distributions and uncertainty using ggdist

#39

Lunch

#40

Scaling and automating R workflows with Kubernetes and Airflow

#41

R Shiny - From Conception to the Cloud

#42

Breakfast

#43

Packages and Process

#44

leafdown: Interactive multi-layer maps in Shiny apps

#45

Robust R Deployments: Building a Pipeline from RStudio to Production

#46

Building a ggplot2 rollercoaster: Creating amazing 3D data visualizations in R

#47

{shinytest2}: Regression testing for Shiny applications

#48

Sometimes you just need words

#49

Achieving a seamless workflow between R, Python and SAS from within RStudio

#50

Zero-setup R workshops with GitHub Codespaces