I just started using this family of functions. They’re awesome! Very helpful.

Lol me too! Previously I almost always used theme_bw().

In R 4.5, grepv() has been added as a shortcut to this. A small nicety.

stat.ethz.ch/R-manual/R-d...

Nice! Thanks for sharing. I’m going to pass this on to my team.

This is awesome! I’ve been using across and where a lot lately— they really simplify the task.

You may like na_if (dplyr.tidyverse.org/reference/na...) and, the inverse, replace_na (tidyr.tidyverse.org/reference/re...).

Maybe the marquee package would help? I haven’t used it much yet myself. marquee.r-lib.org

So apparently there is already a run in background shortcut provided with the targets package. I guess I should have checked before asking 🙃

Very cool! Thanks for sharing.

Any idea if this can be applied when reading CSVs? Every once in awhile I get a different date format from our data providers. It would be great to handle these discrepancies when I read the data into my database.

Maybe Rob Hyndman’s texts would be of interest: robjhyndman.com/publications/

More on the tooling side but want to make sure you were aware of these rstats packages, which I believe Rob is a coauthor on: tidyverts.org

Are we able to see what’s coming in 1.2.0?

I have been searching for this solution as well. I have not found a better workflow than what you described. I recently decided to limit my use of quarto and I am reverting back to exporting tables and figures from R and manually inserting them into Word for the majority of my projects.

I ended up using:

pointblank::col_vals_expr(
expr = ~ nchar(x) == 4
)

This blog post says it was on the roadmap: duckdb.org/2021/11/26/d...