Ah, no, we used `datawizard::to_factor()` to convert label attributes into factor levels.

One advantage of that data is that it has labelled data, and you can see the automatic labelling feature in later plots.

... which you can do by adding additional "layers", if you use the gt-format or tinytable-format.

Not sure about the specific requirements for APA 7 style, but I guess you may need some additionally tweaking of the returned table object.

Wanna dive deeper into the table universe? Check out these links:
👉 easystats.github.io/insight/arti...
👉 vincentarelbundock.github.io/tinytable/

Happy printing, everyone! 🖨️ #rstats #easystats

That "tt" option is now fully rolled out across several #easystats packages, powered by the amazing {tinytable} package. This means you can create tables in a gazillion different output formats! How cool is that? 🤯

And you can totally control the vibe! Use the `format` argument to get "markdown" (for a classic kable look), "html" (for a sleek gt-table), or the new kid on the block, "tt" (for a tinytable masterpiece!).

... and when they print, it's thanks to some behind-the-scenes magic with `insight::format_table()` and `insight::export_table()`! ✨

But there's more! Many #easystats functions also have a `display()` method. Think of it as your personal table stylist, making everything look super user-friendly! 💅

Even if you're not tackling these super complex questions, {modelbased} is generally just a fantastic tool for really getting your head around your statistical models. Go on, take a peek! You might just fall in love: easystats.github.io/modelbased/

#rstats #easystats #marginaleffects #inference

Dealing with interrupted time series where a sudden event just messed with everything?
easystats.github.io/modelbased/a...

Curious about disparities, different trajectories of hidden groups, and what makes them tick?
easystats.github.io/modelbased/a...

Got a thing for social and health inequalities?
easystats.github.io/modelbased/a...

Or maybe you're into the nitty-gritty of intersectional analysis?
easystats.github.io/modelbased/a...

True to the #easystats vibe, {modelbased} keeps things simple, flexible, and easy-peasy so you can truly unleash the power of your models without pulling your hair out.

Ever wondered about cause and effect in observational data without needing a time machine?
easystats.github.io/modelbased/a...

The {modelbased} R package is here to be your statistical sidekick! It's an #rstats gem that helps you squeeze every last drop of insight from your models. It's got a super user-friendly interface to pull out all those estimands from a huge variety of models (doi.org/10.21105/jos...).

Just dodging is not yet implemented in {tinyplot}, but hopefully coming soon!

As you can see, many plot types already work, just some fine-tuning left to do...

Since `display(format = "tt")` returns a `tinytable` object, you can easily modify the table to meet your needs.

Here's the default HTML rendering.