Programming in the Tidyverse

on YouTube ↗51m

" summary

Explains how tidyverse packages like dplyr and ggplot2 use data masking and why that breaks simple reuse: a loop passing column names yields a mean of a string (NA), while mapping with purrr or dplyr scoped verbs (mutate_all / across variants) cleanly replaces loops and scales columns. Describes three reuse techniques: forwarding ... to group_by/AES, subsetting with the .data pronoun, and tidy-eval interpolation using bang-bang / quo (and the upcoming double-curly) to quote/unquote blueprints so pipelines can be wrapped into reusable functions.

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" chapters (7)

1. 0:00Introduction
2. 1:00From analysis to production
3. 8:30Why tidy evaluation
4. 14:20Do you need tidy eval
5. 21:55Easier tidy eval methods
6. 31:00Advanced interpolation
7. 36:30Takeaways and Q&A

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