Grow your data science skills at posit::conf(2024)

August 12th-14th in Seattle

This talk gives an overview of three major use cases for multilingual RMarkdown: building self-documenting data pipelines, rapidly prototyping data science assets, and building ad hoc reports. Our focus is on why multilingual Rmd is valuable *in addition to* the reasons Rmdis already a valuable format (a good general case for Rmd exists here.) The case for multilingual Rmd focuses on flexibility, collaboration, time-to-value, and indecisiveness (in a good way!). Three examples demonstrate why multi-lingual Rmd should be a part of a data scientist’s toolkit.

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