From https://twitter.com/sharon000/status/1107771331012108288:

From https://tidyr.tidyverse.org/dev/articles/pivot.html:

There are two important new features inspired by other R packages that have been advancing of reshaping in R:

• The reshaping operation can be specified with a data frame that describes precisely how metadata stored in column names becomes data variables (and vice versa). This is inspired by the cdata package by John Mount and Nina Zumel. For simple uses of pivot_long() and pivot_wide(), this specification is implicit, but for more complex cases it is useful to make it explicit, and operate on the specification data frame using dplyr and tidyr.
• pivot_long() can work with multiple value variables that may have different types. This is inspired by the enhanced melt() and dcast() functions provided by the data.table package by Matt Dowle and Arun Srinivasan.

If you want to work in the above way we suggest giving our cdata package a try. We named the functions pivot_to_rowrecs and unpivot_to_blocks. The idea was: by emphasizing the record structure one might eventually internalize what the transforms are doing. On the way to that we have a lot of documentation and tutorials.

• Block Records and Row Records
• Designing Transforms for Data Reshaping with cdata
• Coordinatized Data: A Fluid Data Specification
• Fluid Data