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rstudio::conf(2022)

#1

Lunch

#2

Breakfast

#3

Welcome Reception

#4

Lunch

#5

Breakfast

#6

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

#7

The Past and Future of Shiny

#8

Quarto for the Curious

#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

Demystifying MLOps

#16

tidyclust - expanding tidymodels to clustering

#17

Project Immortality: Using GitHub To Make Your Work Live Forever

#18

Highlights of the knitr package from the past two years

#19

Garbage Data, And What To Do About Them

#20

censored - Survival Analysis in tidymodels

#21

dbcooper: Turn any database into an R or Python package

#22

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

#23

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

#24

Scaling and automating R workflows with Kubernetes and Airflow

#25

R Shiny - From Conception to the Cloud

#26

Packages and Process

#27

Robust R Deployments: Building a Pipeline from RStudio to Production

#28

Sometimes you just need words

#29

Hello Quarto: Share • Collaborate • Teach • Reimagine

#30

A new way to build your Shiny app's UI

#31

Running Shiny without a server

#32

Data science training in communities with limited technology resources and opportunities

#33

An introduction to R7

#34

You should be using renv

#35

Designing for people is hard

#36

Exploratory Spatial Data Analysis in the tidyverse

#37

The Future of missing data

#38

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

#39

Making awesome automations with GitHub Actions

#40

Dashboard-Builder: Building Shiny Apps without writing any code

#41

Breakfast

#42

Lunch

#43

It's about time

#44

Creating a Design System for Shiny and RMarkdown

#45

Visualizing distributions and uncertainty using ggdist

#46

leafdown: Interactive multi-layer maps in Shiny apps

#47

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

#48

{shinytest2}: Regression testing for Shiny applications

#49

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

#50

Zero-setup R workshops with GitHub Codespaces