Sounds fascinating. I'd love to read the thesis.
However, from an R point of view I think we need to see what you are doing in the code and what the data coding looks like. We have no idea of what you are doing with antecedents . For example are you treating them by media or by theme. Perhaps by language? All of the above?
Have a look at
A minimal reproducible example consists of the following items:
A minimal dataset, necessary to reproduce the issue
The minimal runnable code necessary to reproduce the issue, which can be run
on the given dataset, and including the necessary information on the used packages.
Let's quickly go over each one of these with examples:
Minimal Dataset (Sample Data)
You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue.
Let's say, as an example, that you are working with the iris data frame
head(iris)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 5.1 3.5 1.4 0.…
and
Why reprex?
Getting unstuck is hard. Your first step here is usually to create a reprex, or reproducible example. The goal of a reprex is to package your code, and information about your problem so that others can run it and feel your pain. Then, hopefully, folks can more easily provide a solution.
What's in a Reproducible Example?
Parts of a reproducible example:
background information - Describe what you are trying to do. What have you already done?
complete set up - include any library() calls and data to reproduce your issue.
data for a reprex: Here's a discussion on setting up data for a reprex
make it run - include the minimal code required to reproduce your error on the data…
and see if they help in expanding on your question.