Releasing an Word document table into the land of markdown, a practical overview of sharing your machine learning model with others, and taking local control of checking the builds of your package across computing architectures.
Episode Links
- This week's curator: Colin Fay - [@_ColinFay]](https://twitter.com/_ColinFay) (Twitter)
- Convert a Word table to Markdown
- How Can Someone Else Use My Model?
- How to debug your package in a {rhub} fedora container before sending to CRAN?
- Entire issue available at rweekly.org/2023-W26
Supplement Resources
{datapasta}RStudio addins and R functions that make copy-pasting vectors and tables to text painless https://milesmcbain.github.io/datapasta- Matt Kaye's series "The missing semester of your DS education" https://matthewrkaye.com/series.html#the-missing-semester-of-your-ds-education
- Put R in production: Tools and guides to put R models in production https://putrinprod.com
{checkhelper}A package to help you deal withdevtools::checkoutputs https://thinkr-open.github.io/checkhelper- Remote Explorer Visual Studio Code extension https://marketplace.visualstudio.com/items?itemName=ms-vscode.remote-explorer
{crew}: A distributed worker launcher framework for asynchronous and distributed computing https://wlandau.github.io/crew- Data4Good Explores Visualizing Freshwater Resources on a Global Scale https://appsilon.com/visualizing-fresh-water-resources-data
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- Eric Nantz: @theRcast (Twitter) and @[email protected] (Mastodon)
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