The “tidy eval” framework is implemented in the rlang package and is rolling out in packages across the Tidyverse and beyond. There is a lively conversation these days, as people come to terms with tidy eval and share their struggles and successes with the community. Why is this such a big deal? For starters, never before have so many people engaged with R’s lazy evaluation model and been encouraged and/or required to manipulate it. I’ll cover some background fundamentals that provide the rationale for tidy eval and that equip you to get the most from other talks.
Jenny is a software engineer on the tidyverse team. She is a recovering biostatistician who takes special delight in eliminating the small agonies of data analysis. Jenny is known for smoothing the interfaces between R and spreadsheets, web APIs, and Git/GitHub. She’s been working in R/S for over 20 years and is a member of the R Foundation. She also serves in the leadership of rOpenSci and Forwards and is an adjunct professor at the University of British Columbia.
