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Track Cherry Blossom
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Drugs not bugs: effective use of R in pharma
Eye candy: surprising and delightful uses of R
It takes a village: building communities of practice
Rapidly responding to world events with R
What they forgot to teach you about your career
Working with people is hard
Track National Harbor 10+11
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Databases
Generating high quality data
Just typing R code: advanced R programming
Teaching data science
Unexpected uses of R
Working with code is hard
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Business intelligence
Cat herding: solving big problems by bringing people together
Quarto deep dive
RMarkdown and Quarto
Some of my best friends use Python
Take a sad process and make it better: project and process makeovers
Track Potomac D
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Data science in production
I like big apps: shiny apps that scale
Machine learning
Pour some glitter on it: polishing the design of your shiny apps
Updates from the tidymodels team
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Lunch
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Breakfast
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Welcome Reception
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Lunch
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Breakfast
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Good practices for applied machine learning - from model development to model deployment
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The Past and Future of Shiny
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Quarto for the Curious
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Evening Dinner & Activities
#10
Making Data Pipelines in R: A Story From A “Self-Taught” Perspective
#11
R, Python, and Tableau: A Love Triangle
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R Markdown + RStudio Connect + R Shiny: A Recipe for Automated Data Processing, Error Logging, and Process Monitoring
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Lunch
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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
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