ggsignif | Easily add significance brackets to your ggplots | Data Visualization library

I see several things.

1. Your p.adj is 1 all the time. So I can't create a label with *

2. Although you are receiving some warnings I am not receiving any error and the code is doing what I expected.

3. You can resize the ylim and define the height of the labels.

library(tidyverse) library(ggsignif) library(ggpubr)

Facets don't seem to work with {ggsignif}, but you could fake them, by looping over your dates, and then patching the plots together.

Below one way

Just add additional values to arguments in geom_signif

One way would be to use an interaction between the x and group variables:

The issue is that geom_signif inherits the global aesthetics you set inside ggplot(). In particular, as you set fill=Follow ggplot is expecting a variable with this name in the dataframe you passed via the data argument of geom_signif.

One option to solve this issue would be to make fill=Follow a local aesthetic to geom_col which also requires to map Follow on the group aes inside geom_text.

Significance refers to a distribution requiring many values per group. But for each group (e.g. S1-green-A) there is only one value in your dataset. Thus you draw a barplot and not a boxplot. You can group more values together e.g. by ignoring the color:

I did not manage to combine A) geom_density_ridges and B) geom_signif. The reason is that (A) requires numerical variable as x and categories as y, while (B) requires numerical variable as y and categories as x. And I have not managed to overwrite this behaviour. But I assume that you have chosen ridge_plots over simple boxplots as you are interested in a more informative visualization of the distribution. To do so, there is a much better solution than ridge_plots, the so called violin plots. See below a standard boxplot (with labelled significance):

Thanks for clarifying your expected outcome. Here is one way to include geom_signif() annotations without altering the original plot:

If you really want to do that (which I would advise against), you can make your x-variable a factor and set the levels to include a blank observation; add a blank observation with that level; and plot.

For example: