Make grouped tibble appear as regular column

[paulk2jonas]

I'm writing this post to help anyone coming here with a problem similar to mine.

The problem:
I used a larger data set to create a small table I wanted, and to do so, I used dplyr::group_by(). I didn't want to use these groups as row groups, but gt automatically uses that column to create grouped rows, and I didn't find any way to convince gt to remove the grouped rows (I only found a workaround to supress them: #140, but that only makes them disappear, which I didn't want)

The solution:
Hidden deep on gt::tab_options(), there's a parameter called row_group.as_column which, when set to TRUE, makes the column appear as I intended to.

[paulk2jonas]

When I say this parameter is hidden deep on the tab_options() function, I mean that it isn't really talked about on any of the functions I looked into regarding row groups (on tab_row_group() there's a link to tab_options(), but the link is among many links to other general tab_ functions relating to several table elements, and there's no clear indication that inside tab_options() there's anything to futher modify row_groups. In fact, I only found this specific parameter because I was curious about the others_label parameter from tab_row_group()'s description, and row_group.as_column happened to be right under row_group.default_label.

In my opinion, which I recognize that might not be all that significant as I am a quite new member of the R community, tab_options() should be made into smaller, more specific functions. The solution, to me, is right inside of it: all parameters are grouped using a group_name.specific_parameter nomenclature. It's not very intuitive for anyone looking for something that is inside it but doesn't know that it is there (just as I was). Having specific functions, like row_group() or row_group_options() would make these features way easier to find on the reference page. I'm not sure about the usefullness of using grouped tibbles to make row groups, as I belive most (if not all) uses of group_by() will end up with row groups of a single row each. I just started using tibble transformations on my workflows, so I'm not experienced on their uses and results, and if I'm wrong about something here, it problably starts by this part.