RStudio

Data visualization for real-world machine learning

Visual representations of data inform how machine learning practitioners think, understand, and decide. Before charts are ever used for outward communication about a ML system, they are used by the system designers and operators themselves as a tool …

Styling Shiny & R Markdown with bootstraplib & thematic

The pharmaceutical industry has witnessed a growing interest in open source languages such as R and Python as an alternative to SAS for many activities related to clinical research. Hop on board for a whistle-stop tour of our efforts within GSK …

StackEm High! Ensembles Using tidymodels

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RMarkdown

A four-hour workshop that will take you on a tour of how to get from data to manuscript using R Markdown. You'll learn - The basics of Markdown and knitr- How to add tables for different outputs- Workflows for working with data- How to include and …

Reproducible shiny apps with shinymeta

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 variables, parameter values, models/algorithms, etc. Although the …

Shiny in Production: Building bridges from data science to IT

We know that adopting documentation, testing, and version control mechanisms are important for creating a culture of reproducibility in data science. But once you've embraced some basic development best practices, what comes next? What does it take …

This one is not like the others: Applicability Domain methods in R

Even though a model prediction can be made, there are times when it should taken with some skepticism. For example, if a new data point is substantially different from the training set, its predicted value may be suspect. In chemistry, it is not …

Reproducibility and the role of code in reproducible data science

Unfortunately we do not currently have an abstract, copy of the slides or link to the video to this presentation

Machine learning