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Video
Fairness and Data Science: Failures, Factors, and Futures
In recent years, numerous highly publicized failures in data science have made evident that...
Video
Empowering Data Scientists to Build Spectacular Shiny Apps
In this talk, Appsilon's CEO and CTO show their vision of challenges facing Shiny...
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Easy larger-than-RAM data manipulation with {disk.frame}
Learn how to handle 100GBs of data with ease using {disk.frame} - the larger-than-RAM-data...
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Designing Randomized Studies using Shiny
This talk will walk through building a self-contained randomized study using Shiny and learnr...
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Custom theming in Shiny & R Markdown with bslib & thematic
Custom theming in Shiny and R Markdown often requires writing styling rules in both...
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Cognitive speed: How the Tidyverse helped the British Red Cross respon...
We will discuss the importance of cognitive speed, defined here as the rate in...
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Categorical Embeddings: New Ways to Simplify Complex Data
Categorical embeddings are a relatively new method, utilizing methods popularized in Natural Language Processing...
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Bringing the Tidyverse to Python with Siuba
Last January I left my job to spend a year developing siuba, a python...
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Bigger Data With Ease Using Apache Arrow
R is unparalleled in its ability to transform raw data into a wide array...