Implementing an Analysis

In this section, we will add the R code to perform our t-test calculations. This builds on the UI we created in the previous section.

1. Locate the Implementation File

In jamovi, the analysis logic lives in the .b.R file. Open R/ttest.b.R in your editor. You will see a generated template:

ttestClass <- R6::R6Class(
    "ttestClass",
    inherit = ttestBase,
    private = list(
        .run = function() {
            # Your code goes here
        })
)

Note

What is R6?

jamovi uses the R6 object-oriented system. Instead of writing simple functions, we write a Class.

inherit = ttestBase: Inherits the core plumbing from jamovi.
private = list(.run = …): This is where you put your code.
self: Use this keyword to access the analysis’ data, options, and results (e.g., self$options).

2. Writing the Logic

Let’s implement the t-test. We’ll start by calling the standard R t.test() function. Note that we access it using the full namespace (stats::t.test) instead of just typing t.test(). This guarantees R runs the core version without naming collisions and speeds up the module by skipping search path lookups.

Never use library()!

ttestClass <- R6::R6Class("ttestClass",
    inherit=ttestBase,
    private=list(
        .run=function() {
            # 1. Construct the formula (e.g., "len ~ supp")
            formula <- jmvcore::constructFormula(self$options$dep, self$options$group)
            formula <- as.formula(formula)
        
            # 2. Run the analysis (using the stats namespace)
            results <- stats::t.test(formula, self$data, var.equal=self$options$varEq)
        
            # 3. Populate the results panel
            self$results$text$setContent(results)
        })
)

Understanding the Code

Construct the formula: We use jmvcore::constructFormula instead of paste(). It automatically handles column names with spaces (e.g., "the fish") by adding backticks, preventing your analysis from crashing.
Run the analysis: We pass the formula and data to the robust stats::t.test().
Populate the results: When you ran jmvtools::addAnalysis() in the previous tutorial, it automatically generated a boilerplate ttest.r.yaml file containing a single Preformatted Text result element named text. We use self$results$text$setContent(results) to safely inject our raw R output into that result container in the jamovi UI.

3. Install and Test

Install the updated module:

jmvtools::install()

Test in jamovi

Go to ☰ -> Open -> Data Library -> Tooth Growth.
Select SuperAwesome -> Independent Samples T-Test.
Assign len to Dependent Variable and supp to Grouping Variable.

Results in jamovi

t-test results

Test in R

Since jamovi modules are R packages, you can test them in your R console using devtools:

# If you don't have devtools installed, uncomment the line below:
# install.packages('devtools')

# Install for the R console
devtools::install()

data(ToothGrowth)
SuperAwesome::ttest(data=ToothGrowth, dep='len', group='supp')

Results in R

t-test results

Next Step: The raw text output works, but before we build richer results, let’s protect it from crashing on empty inputs: Input Checks.