devSJR 
@kernpanik Usually, I also try to stick to base #rstats or lightweight packages (#tinyplot, #tinytable, #rdatatable, ...). Methinks, since most tutorial promote the tidyverse, some do not know base equivalent. However, base data frame operations may require more careful handling of row order, factor levels, and preserving the data frame structure. dplyr maintains a consistent behavior across grouped operations.
John Blischak@devSJR A few more:
#tinytest https://doi.org/10.32614/CRAN.package.tinytest
#tinytable https://doi.org/10.32614/CRAN.package.tinytable
#basetheme https://doi.org/10.32614/CRAN.package.basetheme
tinytest: Lightweight and Feature Complete Unit Testing Framework
Provides a lightweight (zero-dependency) and easy to use unit testing framework. Main features: install tests with the package. Test results are treated as data that can be stored and manipulated. Test files are R scripts interspersed with test commands, that can be programmed over. Fully automated build-install-test sequence for packages. Skip tests when not run locally (e.g. on CRAN). Flexible and configurable output printing. Compare computed output with output stored with the package. Run tests in parallel. Extensible by other packages. Report side effects.
Adrian Gadient-Brügger@granmogul @emjonaitis I summarised possibilities to export #rstats #tables to Word / Excel here: https://www.adrianbruegger.com/post/quick-descriptive-tables/
#tinytable is a new package that also sounds promising
