fix-debug-analysis

name	fix-debug-analysis
description	Guide for debugging JASP analysis functions via code inspection and saveRDS state capture. Use when fixing bugs, troubleshooting errors, or debugging R analysis functions in JASP modules through MCP sessions. Also use when a user provides a .jasp file to reproduce an issue.

Fix & Debug JASP Analysis (MCP Session)

Quick reference for debugging JASP analysis functions through MCP sessions.

Note: browser() and recover() require interactive R console and do not work through MCP's btw_tool_run_r.

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1) Debugging Approaches

There are two approaches, in order of preference:

Approach A: Code Inspection (try first)

Many bugs — especially logic errors, missing branches, wrong conditions — are solvable by reading the code and tracing the control flow. This is faster and doesn't require instrumenting code.

1. Reproduce: Bootstrap a runAnalysis() call (Step 0) and confirm the issue
2. Read: Trace the code path from the entry-point function through the relevant helpers
3. Identify: Look for logic errors — wrong conditions, missing option checks, incorrect branching
4. Fix: Edit the source, hot-reload, and verify

Use this when: Output is missing, wrong options are checked, a feature works in one analysis type but not another, UI options don't match R-side logic.

Approach B: saveRDS State Capture (escalation)

When the bug depends on runtime values that can't be deduced from code reading alone.

1. Instrument: Add saveRDS() before the error location
2. Capture: Hot-reload and run analysis, copy debug path from console
3. Inspect: Load saved state and examine values via MCP
4. Fix: Develop and test fix using captured state
5. Verify: Remove debug code, hot-reload, confirm fix works

Use this when: Error depends on specific data values, unexpected NULL/type, dimension mismatches, or the code path is too complex to trace by reading.

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2) Reproducing the Issue

Step 0: Bootstrap a Reproducible Analysis Run

Before debugging, you need a working runAnalysis() call that reproduces the error. Choose the first applicable source:

Option A: User provides a .jasp file

jaspFile <- "path/to/file.jasp"
opts     <- jaspTools::analysisOptions(jaspFile)
dataset  <- jaspTools::extractDatasetFromJASPFile(jaspFile)
encoded  <- jaspTools:::encodeOptionsAndDataset(opts, dataset)
set.seed(1)
results  <- jaspTools::runAnalysis("AnalysisName", encoded$dataset, encoded$options, encodedDataset = TRUE)

If the .jasp file contains multiple analyses, analysisOptions() returns a list — index with [[1]], [[2]], etc. Pick the analysis that matches the error context.

Option B: Extract from existing unit tests (most common fallback)

When no .jasp file is provided, search test files first. Test files contain pre-configured options and dataset references that are known to produce complete output.

1. Find the test file for the analysis in tests/testthat/:

   grep -r "AnalysisName" tests/testthat/
   

2. Determine the input pattern used in the test. Tests use one of two patterns:

   Pattern 1 — .jasp example file (look for analysisOptions(jaspFile) or extractDatasetFromJASPFile):

   # Copy the loading code from the test, adjusting the path for non-test context
   jaspFile <- file.path("examples", "Example Name.jasp")
   opts     <- jaspTools::analysisOptions(jaspFile)[[1]]  # note: may need [[1]] for multi-analysis files
   dataset  <- jaspTools::extractDatasetFromJASPFile(jaspFile)
   encoded  <- jaspTools:::encodeOptionsAndDataset(opts, dataset)
   set.seed(1)
   results  <- jaspTools::runAnalysis("AnalysisName", encoded$dataset, encoded$options, encodedDataset = TRUE)
   

   Pattern 2 — inline options (look for analysisOptions("AnalysisName") with explicit option assignments):

   # Copy the options setup from the test verbatim
   options <- jaspTools::analysisOptions("AnalysisName")
   options$dependent  <- "contNormal"    # copy from test
   options$group      <- "contBinom"     # copy from test
   # ... copy ALL option assignments from the test ...
   set.seed(1)
   results <- jaspTools::runAnalysis("AnalysisName", "debug.csv", options)
   

3. Modify options to match the bug-triggering scenario (e.g., enable/disable specific checkboxes).

4. Verify reproduction: Check that the issue is reproduced — this could be a "fatalError" status, an error message in a specific output element, incorrect values, missing output, etc., depending on what the user reported.

Option C: Build options from scratch (last resort)

Only when no tests or examples exist:

options <- jaspTools::analysisOptions("AnalysisName")
# Set required inputs — check .robttCheckReady() or equivalent readiness function
# to discover which options must be non-empty
options$dependent <- "contNormal"
options$group     <- "contBinom"
set.seed(1)
results <- jaspTools::runAnalysis("AnalysisName", "debug.csv", options)

Tip: jaspTools::analysisOptions("AnalysisName") returns all options with their QML defaults. Inspect it with str(options) to understand available options and their types.

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3) saveRDS Workflow (Approach B)

Use these steps when code inspection alone is insufficient and you need to examine runtime values.

Step 1: Identify Error Location

From the error message and stack trace, locate the function and approximate line where the error occurs.

Example: Stack trace shows .buildTable() → table$addFootnote() → error

Step 2: Instrument Code

Add saveRDS() just before the line that's failing:

.buildTable <- function(jaspResults, options) {
    # ... existing code ...

    someVariable <- computeSomething(data, options)

    # DEBUG: REMOVE - save state before error
    debug_dir <- tempdir()
    saveRDS(list(
        someVariable = someVariable,
        relatedData = relatedData,
        fit = fit,
        options = options
        # Include ALL relevant variables
    ), file.path(debug_dir, "debug_state.rds"))
    message("DEBUG: Saved to ", file.path(debug_dir, "debug_state.rds"))

    # The line that's failing
    processData(someVariable)
}

Critical rules:

• Always use marker comment # DEBUG: REMOVE
• Never save jaspResults (crashes R)
• Save to tempdir() (auto-cleanup)
• Include message() to print path to console
• Save ALL variables that might be relevant

Step 3: Hot-Reload and Capture

# Via btw_tool_run_r in MCP
devtools::load_all()

# Re-run the analysis (use same code that triggered original error)
set.seed(1)
results <- jaspTools::runAnalysis("AnalysisName", encoded$dataset, encoded$options, encodedDataset = TRUE)

# Console output will show:
# DEBUG: Saved to C:/Users/.../Temp/RtmpXXX/debug_state.rds

Copy the debug path from the console output.

Step 4: Inspect Captured State

# Via btw_tool_run_r in MCP
debug_path <- "C:/Users/.../Temp/RtmpXXX/debug_state.rds"
debug_data <- readRDS(debug_path)

# Examine structure
str(debug_data)

# Inspect specific variables
print(debug_data$someVariable)
sapply(debug_data$someVariable, class)
any(sapply(debug_data$someVariable, is.null))

# Check attributes
for (i in seq_along(debug_data$relatedData)) {
    cat("Item", i, "attribute:", attr(debug_data$relatedData[[i]], "someAttr"), "\n")
}

Goal: Identify the exact values causing the error.

Step 5: Develop Fix

Based on inspection, develop fix logic using the saved objects:

# Via btw_tool_run_r in MCP
# Test the fix logic interactively using saved state

# Example: Filter out invalid values
someVariable_clean <- Filter(function(x) !is.null(x) && is.finite(x), debug_data$someVariable)
print(someVariable_clean)  # Verify it works

# Try the fix
for (i in seq_along(someVariable_clean)) {
    cat("Would process item:", someVariable_clean[[i]], "\n")
}

Once fix logic works, implement it in the source file.

Step 6: Clean Up and Verify

1. Apply fix to source file
2. Remove all debug code (saveRDS(), message(), and "# DEBUG: REMOVE" markers)
3. Hot-reload and verify:

# Via btw_tool_run_r in MCP
devtools::load_all()

set.seed(1)
results <- jaspTools::runAnalysis("AnalysisName", encoded$dataset, encoded$options, encodedDataset = TRUE)

# Check overall status
cat("Status:", results$status, "\n")

4. Verify the specific issue is resolved — don't just check results$status:

   • If the bug was missing output: confirm the output element now exists in results$results
   • If the bug was wrong values: check the specific table/cell values
   • If the bug was an error in a subcomponent: navigate to that component and verify no error
   • If the bug was a crash: confirm status is "complete"

5. Search for any remaining debug code before committing:

grep -r "DEBUG: REMOVE" R/
grep -r "saveRDS.*tempdir" R/

---

4) What to Save

Location	Objects to save	DON'T save
Model fitting	dataset, options, function args, intermediate values	jaspResults, ... (ellipsis args)
Row building	fit, attr(fit, "group"), computed rows, options	Parent containers, environments
Table assembly	rows list, intermediate data.frames	Full fit objects if not needed
Error handling	Error object, variables being processed when error occurred	Large intermediate objects

Golden rule: When unsure, save it. Missing a variable means re-running the entire capture process.

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5) Real-World Example

Error: jaspTable$addFootnote expects 'message' to be a string!

Workflow:

1. Loaded .jasp file and reproduced error:

   jaspFile <- "path/to/file.jasp"
   opts <- jaspTools::analysisOptions(jaspFile)
   dataset <- jaspTools::extractDatasetFromJASPFile(jaspFile)
   encoded <- jaspTools:::encodeOptionsAndDataset(opts, dataset)
   set.seed(1)
   results <- jaspTools::runAnalysis("AnalysisName", encoded$dataset, encoded$options, encodedDataset = TRUE)
   # → Status: fatalError
   

2. Identified error location: Stack trace → .buildTable() at specific line

3. Instrumented code:

   footnotes <- unique(lapply(dataList, attr, which = "footnote"))
   
   # DEBUG: REMOVE
   saveRDS(list(
       footnotes = footnotes,
       dataList = dataList
   ), file.path(tempdir(), "footnote_debug.rds"))
   message("DEBUG: Saved to ", file.path(tempdir(), "footnote_debug.rds"))
   
   for (i in seq_along(footnotes))
       table$addFootnote(footnotes[[i]])
   

4. Captured state:

   devtools::load_all()
   results <- jaspTools::runAnalysis(...)
   # Console: DEBUG: Saved to C:/Users/.../RtmpXXX/footnote_debug.rds
   

5. Inspected:

   debug_data <- readRDS("C:/Users/.../RtmpXXX/footnote_debug.rds")
   str(debug_data$footnotes)
   # List of 2
   #  $ : chr "Some footnote text..."
   #  $ : NULL  ← THE PROBLEM
   

6. Root cause: unique() preserves NULL values → loop called addFootnote(NULL) → error

7. Implemented fix:

   footnotes <- unique(lapply(dataList, attr, which = "footnote"))
   footnotes <- Filter(Negate(is.null), footnotes)  # Filter NULLs
   for (i in seq_along(footnotes))
       table$addFootnote(footnotes[[i]])
   

8. Verified:

   devtools::load_all()
   results <- jaspTools::runAnalysis(...)
   # → Status: complete ✓
   

Time: ~5 minutes from error to verified fix.

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6) Advanced Techniques

Conditional Saving

For errors in specific iterations/groups:

# Only save when condition is met
for (i in seq_along(items)) {
    if (i == 47) {  # Error only in iteration 47
        saveRDS(list(item = items[[i]], i = i), file.path(tempdir(), "debug_iter47.rds"))
        message("DEBUG: Saved iteration 47")
    }
    result <- process(items[[i]])
}

Multiple Checkpoints

Narrow down error location by saving at multiple points:

# Checkpoint 1
saveRDS(list(step = "before_transform", data = data),
        file.path(tempdir(), "checkpoint1.rds"))

data_transformed <- transform(data)

# Checkpoint 2
saveRDS(list(step = "after_transform", data_transformed = data_transformed),
        file.path(tempdir(), "checkpoint2.rds"))

Save with Timestamp

For multiple runs:

timestamp <- format(Sys.time(), "%Y%m%d_%H%M%S")
saveRDS(list(...), file.path(tempdir(), paste0("debug_", timestamp, ".rds")))

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7) Common Error Patterns

Pattern 1: Unexpected NULL

Symptom: "argument is NULL" or "expects X to be a Y"

Debugging:

saveRDS(list(suspect_var = suspect_var, related_vars = list(...)), ...)
# Inspect: is.null(debug_data$suspect_var)

Pattern 2: Wrong Type/Class

Symptom: "cannot coerce X to Y" or "is not a valid type"

Debugging:

saveRDS(list(var = var, class = class(var), str = capture.output(str(var))), ...)
# Inspect: class(debug_data$var), attributes(debug_data$var)

Pattern 3: Dimension Mismatch

Symptom: "dims [product X] do not match length of object [Y]"

Debugging:

saveRDS(list(obj = obj, dims = dim(obj), length = length(obj)), ...)
# Inspect: dim(debug_data$obj), length(debug_data$obj)

Pattern 4: Index Out of Bounds

Symptom: "subscript out of bounds" or "undefined columns selected"

Debugging:

saveRDS(list(container = container, index = i, length = length(container)), ...)
# Inspect: i vs length(debug_data$container), names(debug_data$container)

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8) Safety Checklist

Before committing code:

• All # DEBUG: REMOVE markers removed
• All saveRDS() calls removed
• All debug message() calls removed
• Verified with: grep -r "DEBUG: REMOVE" R/
• Verified with: grep -r "saveRDS.*tempdir" R/
• Hot-reloaded and tested: analysis completes successfully