ggridges | Ridgeline plots in ggplot2 | Data Visualization library

We can feed one layer a version of the data without Species, so it calculates the whole thing as our background context, and another layer that includes Species to map that to fill and to the appropriate facet.

You can achieve this by setting custom labels with scale_x_continuous().

Depending on your desired result a second option would be to set the order via the limits argument of scale_y_discrete:

You could add a secondary y axis with a no-op function:

This could be achieved by increasing the expansion of the y scale via the expand argument of scale_y_discrete:

Note: the following solution is a hack, and is only a workaround. This probably shouldn't be used in any sort of production code. You should either get the spotifyr package maintainer to update the package, or create your own cloned package as described here:

It looks to me that the spotifyr::scopes() function, which get_spotify_authorization_code() relies on, points to this page: https://developer.spotify.com/documentation/general/guides/scopes/. This url comes back with a 404 Error, and looks like it no longer exists.

I believe it should be pointing here instead: https://developer.spotify.com/documentation/general/guides/authorization/scopes/

You could technically overwrite the scopes function in the spotifyr namespace, using assignInNamespace, with a new function including the proper url. The below works for me when I test it:

You want to color the ridges according to the density of the individual traces, so you have to get access to that statistic. Fortunately, ggridges allows that with the stat='density' option and the ..density.. input to the asthetic.

The code:

The issue is the value you used for rel_min_height. Not sure what's the reason but you could make your code work by reducing the value of rel_min_height to e.g. 0.001 or making use of the default value of 0 as I do below:

There are two ways to construct the red line. You can either (1) use geom_line through points representing the group means, or (2) fit a regression through the data.

(1) will be truncated to fit the data, (2) can be extended beyond the data, but will only look right if there is an overall linear relationship between your x and y.

Code for (1)