The jamovi Results Model

Before we dive into building complex tables, it’s worth establishing a strong Mental Model of how jamovi handles results. In our first analysis, we used a simple “text” box, but jamovi’s true power lies in its Rich Results system.

1. The Results Definition vs. The Content

Unlike traditional R scripts where you simply print() output to the console piecemeal, building a beautiful jamovi analysis is a structured, two-step process:

1. The Results Definition (.r.yaml): Think of this as the architectural blueprint for your output. This file defines exactly what result containers will appear (e.g., tables, plots, or HTML) and their rigid structure (like how many columns a table has, or the title of a plot).
2. The Content (.b.R): This is your R code where you perform the actual statistical calculations and then “pour” those numbers into those containers.

2. The Results Tree

To make this two-step process seamless, jamovi reads your .r.yaml results definition and automatically generates an R object called self$results.

Think of self$results as a tree that perfectly mirrors your YAML file. If your definition looks like this:

# .r.yaml
items:
  - name: myTable
    type: Table
    ...
  - name: myPlot
    type: Image
    ...

Then inside your .b.R logic file, those exact result containers are waiting for you, accessible by the very names you gave them in the results definition:

# .b.R
self$results$myTable  # Accesses the table container
self$results$myPlot   # Accesses the plot container

You simply calculate your statistics, and then call functions like self$results$myTable$setRow() to fill them up.

Next Step: Now that you understand the results definition model, let’s create a Rich Table!