How does Atlas ensure my R package dependencies are stable during a refactor?

Atlas integrates with `renv` by reading your `renv.lock` file to understand your package's dependencies. While refactoring, Atlas can prompt you to run `renv::restore()` via `bash` to ensure your environment remains consistent, and it will not invent new dependencies.

Can Atlas help me convert old R `for` loops to `dplyr` or `purrr` pipelines?

Yes, Atlas can be asked to convert `for` loops to `dplyr` or `purrr` pipelines. It leverages its understanding of R's AST declarations to propose idiomatic `tidyverse` transformations, presenting a unified diff for your approval before applying the change.

What if Atlas proposes a change to an R file that I've already modified manually?

Atlas's `apply_patch` tool is designed for this. If an R file has drifted from the state Atlas expects, `apply_patch` will fail with a `Failed to find context` error. Atlas will surface this error, allowing you to reconcile the changes manually or instruct Atlas on how to proceed.

How does Atlas handle `roxygen2` documentation and `NAMESPACE` regeneration in R?

Atlas understands `roxygen2` docblocks. After making changes to exported functions, you can ask Atlas to run `devtools::document()` via `bash` to regenerate your `NAMESPACE` file and update documentation, ensuring consistency with your refactored R code.

Can Atlas help me maintain consistent R code style with `Air` during a refactor?

Yes, Atlas can be instructed to format changed R files using `Air`. After applying structural changes, you can ask Atlas to run `Air --check R/my_module.R` via `bash` to ensure your refactored code adheres to your project's formatting standards, then apply fixes if needed.

How does Atlas ensure I review every change it makes to my R codebase?

Atlas ensures full transparency. It drafts a plan in a read-only agent, asks for approval before executing, and computes a unified diff for every single file edit. This diff is surfaced for your explicit approval before Atlas writes any changes to your R files, giving you granular control.

Refactor a Legacy R Module with Atlas in 2026

Refactoring a legacy R module without introducing regressions or breaking existing callers is a critical task in 2026, and Atlas streamlines this process by integrating directly with your R toolchain. It leverages `testthat` for behavior pinning, `renv` for dependency management, and `Air` for consistent formatting, all while providing granular control and transparent diffs for every change.

How Atlas Maps Public R Module Surfaces and Callers

In 2026, understanding the public surface of an R module is the first step in any refactoring effort. Atlas uses its `lsp` tool to map all exported functions and their callers, providing a comprehensive view of the module's external dependencies.

Before Atlas touches any code, it builds a precise understanding of the R module's public interface. The `lsp` tool's `documentSymbol` operation scans your `R/` directory, identifying all exported functions and objects declared in your `DESCRIPTION` file or via `roxygen2` docblocks. For each identified symbol, Atlas then executes `findReferences` to enumerate every single callsite across your codebase. This crucial initial mapping ensures that Atlas has a complete list of all external dependencies on the module, preventing silent breakage at unknown call sites during the refactor. This process is permission-gated, allowing you to review the scope before any analysis begins.

Pinning R Module Behavior with testthat and Atlas

Ensuring a refactor doesn't alter behavior is paramount in 2026. Atlas pins the existing behavior of an R module by running its `testthat` suite with `bash`, establishing a green baseline before any structural changes are applied.

A successful refactor in R means preserving existing functionality. Atlas achieves this by first establishing a behavioral baseline. It uses the `bash` tool to execute your existing `testthat` suite, typically by running `devtools::test()` or `Rscript -e 'testthat::test_dir("tests/testthat/")'`. Atlas records the output, confirming all tests pass and establishing a 'green' baseline. This baseline serves as the immutable reference point throughout the refactoring process. Any subsequent change introduced by Atlas will be immediately validated against this baseline, ensuring that the module's behavior remains unchanged. This step is critical for confidence, especially when dealing with complex `tidyverse` pipelines or intricate statistical models.

Applying Structural Changes to R Code with Atlas's apply_patch

Restructuring R code requires precision and safety, especially in 2026. Atlas employs its `apply_patch` tool to make structural changes, anchoring on context lines and refusing to apply patches if the file has drifted, ensuring integrity.

Once the public surface is mapped and behavior is pinned, Atlas proceeds with structural changes. The `apply_patch` tool is central to this. When Atlas proposes a change, it generates a unified diff that includes context lines around each hunk. This is not a blind line-by-line replacement. If the target R file has been modified externally and the context lines no longer match, `apply_patch` will fail with a `Failed to find context` error. This robust mechanism prevents Atlas from applying changes to a drifted file, thereby avoiding unintended consequences and ensuring that every edit is applied against the expected code state. Atlas also computes a unified diff for every file edit and surfaces it for your approval before writing, giving you full control over the changes to your `R/` files.

Iterative Testing and Safety for R Refactors with Atlas

In 2026, iterative testing is key to safe R refactoring. Atlas re-runs the `testthat` suite with `bash` after each `apply_patch` hunk lands, providing immediate feedback and preventing the accumulation of errors.

Atlas adopts an iterative approach to refactoring, prioritizing safety and immediate feedback. Instead of applying all changes at once and testing at the end, Atlas re-runs the `testthat` suite using the `bash` tool after each individual hunk from `apply_patch` successfully lands. This means that if a small structural change inadvertently breaks a test, you know exactly which change caused it. This granular testing strategy minimizes debugging time and ensures that the module's behavior remains consistent throughout the refactoring process. Atlas also reads `git` branches, status, and diffs, and can stage and create commits on your behalf, allowing you to checkpoint your work frequently and roll back changes if necessary, using `git` patches for snapshots.

Tracking R Call Site Migrations with Atlas's todowrite Tool

Managing the migration of R call sites during a refactor is simplified in 2026 with Atlas. The `todowrite` tool tracks remaining call sites, ensuring a partially migrated module is never mistaken for a finished one.

Refactoring often involves migrating multiple call sites to a new interface. Atlas helps manage this complexity with its `todowrite` tool. After identifying all call sites with `lsp findReferences`, Atlas can generate a `todowrite` list. As each call site is successfully migrated and verified, it is marked off the list. This provides a clear, persistent record of remaining work, preventing a partially migrated R module from being mistakenly considered complete. This systematic tracking ensures that all dependencies are updated, and the refactor is truly finished, maintaining the integrity of your `DESCRIPTION`-based R package.

Atlas's Safety and Review Mechanisms for R Code Changes

Atlas provides multiple layers of safety for R code changes in 2026, including permission-gated tool calls, read-only planning, and unified diffs for every edit, ensuring developer control and transparency.

Atlas is designed with developer control at its core. Every Atlas tool call, whether it's `bash` to run `devtools::test()` or `apply_patch` to modify an `R/` file, is permission-gated against `allow`, `ask`, and `deny` rules. This means you explicitly approve actions before they execute. Atlas drafts a plan in a read-only plan agent and asks for approval before switching to a build agent to make changes. Furthermore, Atlas computes a unified diff for every file edit and surfaces it for your approval before writing to disk. This transparent review process, combined with its ability to snapshot file changes as `git` patches for easy rollback, provides unparalleled safety and confidence when refactoring critical R codebases.

Step by step

01Initialize Atlas in your R package directory, ensuring a `DESCRIPTION` file and `R/` directory are present, and `renv.lock` is available for dependency context.
02Ask Atlas to map the module's public surface: `atlas lsp documentSymbol --path R/my_module.R` to identify exported functions, then `atlas lsp findReferences --symbol my_exported_function` for each to enumerate all callers.
03Pin the module's current behavior: Use `atlas bash --command 'devtools::test()'` to run your `testthat` suite and record the green baseline before any changes.
04Draft a refactoring plan with Atlas, focusing on structural changes. Atlas will propose `apply_patch` operations for your `R/` files.
05Approve each `apply_patch` hunk proposed by Atlas. After each successful application, immediately re-run tests with `atlas bash --command 'devtools::test()'` to verify behavior.
06Use `atlas todowrite add 'Migrate callsite in R/another_file.R'` for each remaining callsite identified by `lsp findReferences`, marking them off as they are updated.
07Once refactoring is complete, ask Atlas to format changed files with `Air`: `atlas bash --command 'Air --check R/my_module.R'` and regenerate `NAMESPACE` with `roxygen2` if necessary.
08Review the final unified diffs and commit the changes using Atlas's `git` integration, ensuring all `renv` dependencies are consistent.

Frequently asked questions

How does Atlas ensure my R package dependencies are stable during a refactor?: Atlas integrates with `renv` by reading your `renv.lock` file to understand your package's dependencies. While refactoring, Atlas can prompt you to run `renv::restore()` via `bash` to ensure your environment remains consistent, and it will not invent new dependencies.
Can Atlas help me convert old R `for` loops to `dplyr` or `purrr` pipelines?: Yes, Atlas can be asked to convert `for` loops to `dplyr` or `purrr` pipelines. It leverages its understanding of R's AST declarations to propose idiomatic `tidyverse` transformations, presenting a unified diff for your approval before applying the change.
What if Atlas proposes a change to an R file that I've already modified manually?: Atlas's `apply_patch` tool is designed for this. If an R file has drifted from the state Atlas expects, `apply_patch` will fail with a `Failed to find context` error. Atlas will surface this error, allowing you to reconcile the changes manually or instruct Atlas on how to proceed.
How does Atlas handle `roxygen2` documentation and `NAMESPACE` regeneration in R?: Atlas understands `roxygen2` docblocks. After making changes to exported functions, you can ask Atlas to run `devtools::document()` via `bash` to regenerate your `NAMESPACE` file and update documentation, ensuring consistency with your refactored R code.
Is Atlas compatible with my existing `testthat` test suite for R?: Absolutely. Atlas uses the `bash` tool to execute your existing `testthat` test suite, typically by running `devtools::test()` or `Rscript -e 'testthat::test_dir("tests/testthat/")'`. It expects your tests to be located under `tests/testthat/` and uses their output to pin and verify behavior.
Can Atlas help me maintain consistent R code style with `Air` during a refactor?: Yes, Atlas can be instructed to format changed R files using `Air`. After applying structural changes, you can ask Atlas to run `Air --check R/my_module.R` via `bash` to ensure your refactored code adheres to your project's formatting standards, then apply fixes if needed.
How does Atlas ensure I review every change it makes to my R codebase?: Atlas ensures full transparency. It drafts a plan in a read-only agent, asks for approval before executing, and computes a unified diff for every single file edit. This diff is surfaced for your explicit approval before Atlas writes any changes to your R files, giving you granular control.

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