Error Fixing

Errors detected, diagnosed, and fixed - before you even ask

AiR watches your R console for errors, reads the full stack trace, diagnoses the root cause, and proposes a targeted fix with a color-coded diff. Accept with one click.

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How It Works

Three steps: Detect, Diagnose, Fix

AiR's error handling pipeline runs automatically. The moment an error appears in your R console, AiR picks it up and starts working.

Detect

AiR monitors your R console output in real-time. When a script throws an error, a knit fails, or a warning appears, it captures the full error message, stack trace, and render log.

Diagnose

The error is sent to AI along with your full file content, your R environment context, and project structure. The AI identifies the root cause - not just the symptom.

Fix

AiR generates a targeted fix and presents it as a color-coded diff. You see exactly what lines changed, what was added, and what was removed. Accept or reject with one click.

Coverage

Every kind of R error, handled

From simple syntax mistakes to complex knitting failures, AiR handles the full spectrum of errors you encounter in R.

Knit Failures

R Markdown rendering errors, chunk execution failures, YAML parsing issues, and missing dependencies that prevent knitting.

Error in render_rmd(): could not find function 'geom_points'

Runtime Errors

Function call failures, object not found, wrong argument types, subscript out of bounds, and other execution errors.

Error in mean(df$column): object 'df' not found

Missing Packages

Functions called without loading the required package. AiR identifies which package is needed and adds the library() call.

Error: could not find function 'ggplot'

Type Mismatches

Passing character data to numeric functions, factor level issues, wrong data types in model formulas.

Error in lm(): variable 'group' is not numeric

Syntax Errors

Unmatched parentheses, missing commas, incorrect pipe usage, and other structural code issues.

Error: unexpected ')' in "mutate(x = mean(y)"

Data Issues

NA propagation, empty dataframes after filtering, column name mismatches, and encoding problems.

Error: 0 observations after filter() - check your conditions

Diffs

See exactly what changed before it touches your file

Every fix is shown as a color-coded diff. Green lines are additions, red lines are deletions. Nothing is applied until you approve.

analysis.Rmd

+2-1

library(ggplot2)

library(dplyr)

+library(scales)

ggplot(diamonds, aes(x = carat, y = price)) +

- geom_points(alpha = 0.1) +

+ geom_point(alpha = 0.1) +

scale_y_continuous(labels = comma) +

theme_minimal()

AcceptReject

Accepted the wrong fix? No problem. Every change can be undone. AiR tracks what it modified so you can always revert.

Iteration

First fix didn't work? AiR tries again.

If the initial fix doesn't resolve the error, AiR reads the new error output and diagnoses the remaining issue. It iterates until the problem is solved.

1st attempt

Fixes typo: geom_points → geom_point

New error: could not find function 'comma'

2nd attempt

Adds missing library: library(scales)

Knits successfully

Context

Fixes that understand your entire project

AiR doesn't just see the error line. It reads your full file, knows your loaded datasets and their column types, understands your project structure, and accounts for all of this when proposing fixes.

Your Active File

The complete contents of the file where the error occurred. AiR sees surrounding code, function definitions, and variable declarations.

R Environment

Every loaded dataframe, its dimensions, column names and types, missing data percentages, and value ranges.

Error Output

The full error message, stack trace, render log, and any warnings that preceded the error.

Project Structure

Your project files, R scripts, data files, and their relationships. If the fix needs to reference another file, AiR knows where it is.

Model Auto-Fix

The same error-fixing intelligence, applied to model code

When model fitting fails - missing packages, type mismatches, convergence issues - AiR sends the generated R code and the full error output to AI. The code is diagnosed, fixed, and re-executed automatically. You see model results, not error messages.

Model code fails to produce results

Random Forest on wine_quality - missing package

AI diagnoses: missing randomForest package

Adds library(randomForest) and fixes namespace conflict

Fixed code runs successfully

Results displayed with “Auto-fixed” badge

Stop debugging. Start building.

Let AiR handle the errors while you focus on your analysis.

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