Revise Your Code with AI

Last updated on 2026-02-13 | Edit this page

Overview

Questions

How can I use AI to check and improve my code?
What are effective strategies for code review with AI assistants?
How do I use chat interfaces for iterative code refinement?
When should I trust AI suggestions for code revisions?

Objectives

Use AI chat interfaces to review and analyze code
Identify code issues with AI assistance
Iteratively refine code based on AI feedback
Validate AI suggestions critically
Develop a workflow for AI-assisted code revision

Using AI directively

AI-powered tools can serve as an additional pair of eyes when reviewing and improving your code. This chapter explores how to effectively use AI chat interfaces to check, revise, and validate your R code, ensuring better quality and maintainability.

Why Use AI for Code Review?

AI assistants can help identify:

Logic errors: Potential bugs or incorrect implementations
Performance issues: Inefficient code patterns
Style problems: Code that doesn’t follow best practices
Documentation gaps: Missing or unclear comments
Security vulnerabilities: Potential security risks

Callout

AI as a Complement, Not Replacement

AI code review should complement, not replace:

Your own understanding of the code
Human peer reviews
Automated testing and linting tools
Domain expertise

Setting Up for AI Code Review

Using GitHub Copilot Chat in your browser

Currently (as of end 2025), RStudio has no built-in AI chat interface

However, you can use GitHub Copilot Chat within your browser.

To this end:

Navigate to https://github.com/copilot
For an extended list of models:
- Ensure you have a GitHub Copilot subscription
- Sign in

If you have any other AI chat interface you prefer (e.g., ChatGPT, Claude), you can use that as well.

When using a browser, you have to manually copy and paste code snippets between RStudio and the chat interface to get reviews and suggestions. While this is less seamless than an integrated solution, it still allows you to leverage AI for code review.

Alternative: Use the `chattr` app within RStudio

The chattr package provides an interface to chat with AI models directly within RStudio.

The chattr_app() function will open a Shiny app where you can interact with various AI models. Alternatively, you can run the app via the RStudio “Addins”” menu by selecting “Open chat”.

Unfortunately, running the chat interface will “block” your R session until you close the app.

A workaround is to run the app in “job” mode using

R

chattr::chattr_app(as_job = TRUE)

That way, you can continue working in your main R session while chatting with the AI in a separate window. The con is that the chattr app won’t be able to directly interact with your current document in RStudio.

Via the same “Addins” menu, you can also select code in your R script and choose “Send prompt” to get AI feedback on that specific code snippet, which will be copied back directly below your selection.

As of now, I personally find using GitHub Copilot Chat in the browser more flexible for code review tasks, but the chattr package is a promising option for integrated RStudio workflows.

If you are interested in a more sophisticated AI integration into your IDE, you might have to consider to try another IDE such as Positron or Visual Studio Code.

Basic Code Review Workflow

The following steps are a general workflow for using AI to review and improve your R code.

Step 1: Request an Initial Review

Select your code and ask AI to review it:

R

# Example code to review
calculate_mean <- function(numbers) {
  sum(numbers) / length(numbers)
}

Prompt for AI: > Review this function for potential issues and suggest improvements.

Challenge

Challenge 1: Basic Code Review

Ask AI to review the following function. What issues does it identify?

R

process_data <- function(data) {
  result <- data[data$value > 0]
  mean_val <- sum(result$value) / nrow(result)
  return(mean_val)
}

Issues AI Might Identify

No NA handling: Function will fail if there are NA values
Division by zero: If no rows match the condition, nrow(result) is 0
Column existence: Assumes value column exists without checking
No input validation: Doesn’t verify data is a data frame

Improved version:

R

process_data <- function(data) {
  # Input validation
  if (!is.data.frame(data)) {
    stop("Input must be a data frame")
  }
  if (!"value" %in% names(data)) {
    stop("Data frame must contain 'value' column")
  }
  
  # Filter and calculate
  result <- data[data$value > 0 & !is.na(data$value), ]
  
  if (nrow(result) == 0) {
    warning("No positive values found")
    return(NA)
  }
  
  mean(result$value, na.rm = TRUE)
}

Step 2: Ask Specific Questions

Be specific about what you want to check:

Good prompts:

“Does this function handle edge cases correctly?”
“Are there any performance bottlenecks in this loop?”
“Is this code following tidyverse style guidelines?”
“Could this code be more readable?”

Less effective prompts:

“Is this good?”
“Check this code”

Step 3: Request Improvements

R

# Original code
for(i in 1:length(data)) {
  result[i] <- data[i] * 2
}

Prompt: > How can I make this code more efficient and R-idiomatic?

AI might suggest:

R

# Vectorized approach (much faster)
result <- data * 2

Round 1: Initial Review

R

# Your initial code
analyze_sales <- function(sales_data) {
  total <- 0
  for(i in 1:nrow(sales_data)) {
    total <- total + sales_data[i, "amount"]
  }
  return(total / nrow(sales_data))
}

AI Feedback: “This uses a slow loop. Consider vectorization.”

Round 2: Apply Suggestions

R

analyze_sales <- function(sales_data) {
  mean(sales_data$amount)
}

AI Feedback: “Good! Consider adding NA handling and input validation.”

R

analyze_sales <- function(sales_data) {
  if (!is.data.frame(sales_data)) {
    stop("Input must be a data frame")
  }
  if (!"amount" %in% names(sales_data)) {
    stop("Data must contain 'amount' column")
  }
  
  mean(sales_data$amount, na.rm = TRUE)
}

Callout

Iterative Improvement

Don’t expect perfect code in one iteration. Use AI as a collaborative partner:

Get initial feedback
Make changes
Ask for review again
Repeat until satisfied

Common Code Issues AI Can Help Identify

1. Logic Errors

R

# Problematic code
if (x > 0 & y > 0) {  # What if x or y is NA?
  process(x, y)
}

AI can suggest:

R

if (!is.na(x) && !is.na(y) && x > 0 && y > 0) {
  process(x, y)
}

Did you notice the change from & to &&? If you don’t know why, ask AI!

2. Performance Problems

R

# Slow: Growing vector in loop
result <- c()
for(i in 1:10000) {
  result <- c(result, calculate(i))
}

AI can suggest:

R

# Fast: Pre-allocate vector
result <- vector("numeric", 10000)
for(i in 1:10000) {
  result[i] <- calculate(i)
}

# Even better: Vectorize if possible using sapply() or purrr
result <- purrr::map_dbl(1:10000, calculate)

3. Code Readability

R

# Hard to read
f <- function(x, y, z) { x + y * z / (x - y) }

AI can suggest:

R

# More readable
calculate_metric <- 
  function(base_value, multiplier, divisor) {
    adjustment <- multiplier * divisor
    denominator <- base_value - multiplier
    
    base_value + (adjustment / denominator)
  }

Challenge

Challenge 2: Iterative Code Improvement

Start with this code and iteratively improve it with AI assistance:

R

my_function <- function(x) {
  y <- c()
  for(i in 1:length(x)) {
    if(x[i] > 0) {
      y <- c(y, x[i] * 2)
    }
  }
  return(y)
}

Ask AI to help you:

Improve performance
Add error handling
Improve readability
Add documentation

Improved Version After Iterations

R

#' Double positive values in a numeric vector
#'
#' Takes a numeric vector and returns a new vector containing
#' only the positive values, each doubled.
#'
#' @param x A numeric vector
#' @return A numeric vector of doubled positive values
#' @examples
#' double_positives(c(-1, 2, -3, 4))  # Returns c(4, 8)
double_positives <- function(x) {
  # Input validation
  if (!is.numeric(x)) {
    stop("Input must be a numeric vector")
  }
  
  # Vectorized filtering and transformation
  positive_values <- x[x > 0 & !is.na(x)]
  positive_values * 2
}

Key improvements:

Descriptive function name
Roxygen documentation
Input validation
Vectorized operations (much faster)
NA handling

Using Chat for Code Explanation

AI can help you understand unfamiliar code:

R

# Complex code you found
result <- df %>%
  group_by(category) %>%
  summarise(
    mean_val = mean(value, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  filter(mean_val > quantile(mean_val, 0.75))

Prompt:

Explain what this code does step by step.

AI response would explain:

Groups data by category
Calculates mean value per category
Drops grouping structure
Filters for top 25% of means

Often it is a good idea to ask the AI to incorporate explanations as comments in the code itself. That way, you have both the code and the explanation together for future reference and rereading.

Double-Checking AI Suggestions

Always Validate AI Recommendations

R

# AI suggests: "Use this for better performance"
result <- parallel::mclapply(data, complex_function)

Questions to ask yourself:

Does this actually work with my data?
Is parallel processing appropriate here?
Will this work on all platforms (Windows issues with mclapply)?
Is the complexity worth the performance gain?

Callout

Test AI Suggestions

Never blindly accept AI code suggestions:

Run the code with test data
Verify results match expectations
Check edge cases
Benchmark performance claims
Ensure compatibility with your environment

Creating Test Cases

Use AI to help generate tests:

Prompt:

Generate test cases for this function, including edge cases.

R

# Your function
safe_divide <- function(a, b) {
  if (b == 0) return(NA)
  a / b
}

AI-generated tests:

R

# Test cases
test_that("safe_divide works correctly", {
  expect_equal(safe_divide(10, 2), 5)
  expect_equal(safe_divide(0, 5), 0)
  expect_true(is.na(safe_divide(5, 0)))
  expect_true(is.na(safe_divide(Inf, 0)))
  expect_equal(safe_divide(-10, 2), -5)
})

While this will provide the needed code for tests, make sure to review the tested values and adapt the tests to your specific needs.

Advanced Code Review Techniques

1. Security Review

Prompt:

Review this code for security vulnerabilities.

R

# Potentially unsafe
query <- paste0("SELECT * FROM users WHERE id = ", user_input)

AI might warn: SQL injection risk!

2. Style Consistency

Prompt:

Does this code follow tidyverse style guidelines?

R

# Inconsistent style
myFunction<-function(x,y){return(x+y)}

AI suggests:

R

# Consistent style
my_function <- function(x, y) {
  x + y
}

3. Documentation Review

Prompt:

Is this function well-documented? Suggest improvements.

R

calc <- function(x, y) {
  x * y + mean(x)
}

AI suggests adding:

R

#' Calculate weighted metric
#'
#' Multiplies two vectors element-wise and adds the mean of the first vector
#'
#' @param x Numeric vector for values
#' @param y Numeric vector for weights
#' @return Numeric vector of weighted values plus mean adjustment
calc <- function(x, y) {
  x * y + mean(x)
}

Challenge

Challenge 3: Comprehensive Code Review

Perform a complete AI-assisted review of this analysis function:

R

analyze <- function(d) {
  d2 <- d[d$val > 0]
  m <- sum(d2$val) / length(d2$val)
  s <- sqrt(sum((d2$val - m)^2) / length(d2$val))
  list(m, s)
}

Review for:

Correctness
Efficiency
Readability
Documentation
Error handling

Comprehensive Improved Version

R

#' Calculate mean and standard deviation for positive values
#'
#' Filters a data frame to positive values in the specified column
#' and calculates descriptive statistics
#'
#' @param data A data frame containing the data to analyze
#' @param value_col Name of the column containing values (default: "val")
#' @return A named list with 'mean' and 'sd' of positive values
#' @examples
#' analyze_positive_values(data.frame(val = c(-1, 2, 3, 4)))
analyze_positive_values <- function(data, value_col = "val") {
  # Input validation
  if (!is.data.frame(data)) {
    stop("Input must be a data frame")
  }
  if (!value_col %in% names(data)) {
    stop(paste("Column", value_col, "not found in data"))
  }
  if (!is.numeric(data[[value_col]])) {
    stop(paste("Column", value_col, "must be numeric"))
  }
  
  # Filter positive values and remove NAs
  positive_values <- data[[value_col]][
    data[[value_col]] > 0 & !is.na(data[[value_col]])
  ]
  
  # Check if any values remain
  if (length(positive_values) == 0) {
    warning("No positive values found")
    return(list(mean = NA, sd = NA))
  }
  
  # Calculate statistics using built-in functions
  # (more accurate than manual calculation)
  list(
    mean = mean(positive_values),
    sd = sd(positive_values)
  )
}

Improvements:

Clear, descriptive name
Full documentation
Input validation
Column name parameter
NA handling
Edge case handling
Uses built-in statistical functions
Named list output

What you might have noticed at this point: well documented and robust code often spends more lines on validation and documentation than on the actual logic itself.

That’s fine!!!

Writing maintainable code is more important than keeping it short.

From this code, you can also automatically generate documentation pages using roxygen2, which is a great bonus for future users (including yourself). Or you can use the documentation to generate vignettes or tutorials.

Best Practices for AI-Assisted Code Review

1. Start with Specific Questions

Instead of: “Review this code”

Try: “Does this function handle missing data correctly?”

2. Review in Small Chunks

Don’t send entire scripts for review
Focus on one function or logical block at a time
Easier to understand and act on feedback

3. Ask “Why” Questions

“Why is this approach better?”
“Why might this fail?”
“Why is this more efficient?”

Understanding helps you learn, not just copy.

4. Combine Multiple Perspectives

AI review
Peer review
Automated linting (e.g. lintr package)
Testing (e.g. testthat package)

5. Document Changes

Keep track of improvements.

R

# Version 1 (original): Simple but no error handling
# Version 2 (after AI review): Added input validation
# Version 3 (after testing): Improved edge case handling

But best not via comments, rather via a proper version control system such as Git. The latter is especially important when collaborating in teams and neatly integrated into RStudio.

In combination with GitHub, you can even use AI tools to help you write better commit messages, set up automated post-push actions for linting, testing, and documentation generation. And the GitHub Copilot can even help you via the GitHub web interface to review pull requests, suggest solutions for issues and improvements, as we will discuss in more detail in later chapters.

Discussion

Group Discussion

Discuss with your peers:

What types of code issues have you discovered using AI review?
Have you encountered situations where AI gave incorrect advice?
How do you balance AI suggestions with your own judgment?
What are the limitations of AI code review compared to human review?

Limitations and Cautions

AI Cannot Replace

Domain expertise: Understanding of your specific field
Context awareness: Knowledge of project history and constraints
Testing: Actual execution and validation
Team standards: Specific conventions in your organization

Common AI Mistakes

Suggesting overly complex solutions
Not understanding project-specific constraints
Proposing outdated or deprecated approaches
Missing subtle domain-specific issues

When to Seek Human Review

Security-critical code
Complex algorithms
Code with business logic
Performance-critical sections
Code you don’t fully understand

Key Points

AI chat interfaces provide valuable code review assistance
Use iterative refinement: review, improve, and re-review
Ask specific questions about correctness, performance, and style
Always validate and test AI suggestions before accepting them
Combine AI review with human peer review and automated tools
Be aware of AI limitations and seek human expertise for critical code
Document the review process and improvements made

Revise Your Code with AI

Overview

Questions

Objectives

Using AI directively

Why Use AI for Code Review?

AI as a Complement, Not Replacement

Setting Up for AI Code Review

Using GitHub Copilot Chat in your browser

Alternative: Use the chattr app within RStudio

R

Basic Code Review Workflow

Step 1: Request an Initial Review

R

Challenge 1: Basic Code Review

R

Issues AI Might Identify

R

Step 2: Ask Specific Questions

Step 3: Request Improvements

R

R

Iterative Refinement Process

Round 1: Initial Review

R

Round 2: Apply Suggestions

R

Round 3: Further Refinement

R

Iterative Improvement

Common Code Issues AI Can Help Identify

1. Logic Errors

R

R

2. Performance Problems

R

R

3. Code Readability

R

R

Challenge 2: Iterative Code Improvement

R

Improved Version After Iterations

R

Using Chat for Code Explanation

R

Double-Checking AI Suggestions

Always Validate AI Recommendations

R

Test AI Suggestions

Creating Test Cases

R

R

Advanced Code Review Techniques

1. Security Review

R

2. Style Consistency

R

R

3. Documentation Review

R

R

Challenge 3: Comprehensive Code Review

R

Comprehensive Improved Version

R

Best Practices for AI-Assisted Code Review

1. Start with Specific Questions

2. Review in Small Chunks

3. Ask “Why” Questions

4. Combine Multiple Perspectives

5. Document Changes

R

Group Discussion

Limitations and Cautions

AI Cannot Replace

Common AI Mistakes

When to Seek Human Review

Alternative: Use the `chattr` app within RStudio