Debugging a single failing test in R with Atlas in 2026 involves isolating the specific `testthat` test, walking the R call graph, forming a hypothesis, and fixing the underlying R code, not the assertion. Atlas integrates directly with your R toolchain, including `testthat` for testing, `renv` for package management, and `Air` for formatting, providing a terminal-native AI experience.
How Atlas isolates a failing testthat test in R
Isolating a failing `testthat` test in R is the first step in debugging with Atlas. In 2026, Atlas leverages its `bash` tool to run specific `testthat` tests, ensuring minimal output for focused analysis. This approach mirrors manual debugging techniques, making the process intuitive for R developers.
Atlas's `bash` tool allows you to execute any shell command, including R commands to run specific tests. When debugging a single failing test in an R package, you can instruct Atlas to run `devtools::test()` with a `filter` argument. For example, `atlas bash -- devtools::test(filter = 'my_failing_test')` will execute only the test matching 'my_failing_test' within your `tests/testthat/` directory. This precision significantly reduces the output noise, allowing you to concentrate on the specific assertion failure and the R code it exercises. Atlas reads the output from this command, identifying the exact failure message and line numbers, which then informs its subsequent analysis steps. This capability is crucial for efficiently pinpointing issues in complex R packages managed with `renv`.
Walking the R call graph with Atlas's lsp tool
After isolating a failing `testthat` test, understanding the R code's execution path is critical. Atlas's `lsp` tool, available in 2026, enables developers to walk the call graph of R functions, using operations like `goToDefinition` and `findReferences` to trace how data flows and functions interact within your R package.
Once Atlas has identified a failing `testthat` test and the R code it exercises, it uses its `lsp` tool to work through the codebase. The `lsp` tool connects to Model Context Protocol servers, exposing powerful language server capabilities. For an R developer, this means Atlas can perform operations like `goToDefinition` on a function call within the failing test to jump directly to its source in an `R/` file. Similarly, `findReferences` can reveal all locations where a particular R function is called, helping to understand its impact and dependencies. This allows Atlas to build a comprehensive understanding of the call path, from the `test_that` block in `tests/testthat/` through various R functions and tidyverse pipelines, all without leaving the terminal. This deep code understanding is essential for forming accurate hypotheses about the root cause of the failure.
Hypothesis testing and temporary logging in R with Atlas
Forming and checking a hypothesis is a core part of debugging R code. Atlas, in 2026, facilitates this by allowing you to add temporary logging or re-run tests with verbose flags using its `edit` and `bash` tools. This iterative process helps confirm or refute assumptions about the failing R function.
With a clear understanding of the R call graph, Atlas can form a hypothesis about why a `testthat` test is failing. To check this hypothesis, Atlas can use its `edit` tool to insert temporary `print()` statements or `message()` calls into the R production code. For instance, `atlas edit --file R/my_function.R --insert 'print(paste("Debug value:", my_variable))' --at-line 42` would add a debug line. Alternatively, if the R package or `testthat` framework supports a verbose flag, Atlas can re-run the test using `bash` with that flag enabled, like `atlas bash -- devtools::test(filter = 'my_failing_test', verbose = TRUE)`. After observing the output, Atlas can refine its hypothesis or proceed to fix the code. The ability to quickly add and remove temporary debugging code is a significant advantage, as Atlas snapshots file changes as git patches, making it easy to roll back these temporary edits later.
Fixing R code and re-running testthat tests with Atlas
Once the root cause of a failing `testthat` test in R is identified, Atlas helps fix the production code. In 2026, Atlas uses its `edit` or `apply_patch` tools to modify R files, then re-runs the single test and the full `testthat` suite to confirm the fix and ensure no regressions.
After confirming the hypothesis, Atlas proceeds to fix the R production code. For small, focused changes, the `edit` tool is used, for example, `atlas edit --file R/my_function.R --replace 'old_code' --with 'new_code'`. If the fix involves changes spanning several hunks or requires a more complex modification, Atlas can use `apply_patch` to ensure a robust and accurate update. This prevents chaining brittle `edit` commands. After applying the fix, Atlas first re-runs the single failing `testthat` test using `bash` to confirm the specific issue is resolved. Then, it runs the entire `testthat` suite, typically via `atlas bash -- devtools::test()`, to ensure the fix hasn't introduced any regressions across the R package. Finally, Atlas removes any temporary logging added during the hypothesis-checking phase, ensuring a clean codebase.
Atlas's safety and review process for R code changes
Atlas prioritizes safety and transparency when making changes to R code. Every Atlas tool call is permission-gated, and all proposed file edits are presented as unified diffs for approval. This ensures R developers maintain full control over their codebase in 2026.
Atlas is designed with a strong emphasis on user control and safety. Before any tool call, including `bash`, `edit`, or `apply_patch`, Atlas checks against allow, ask, and deny rules, prompting for permission if necessary. When Atlas drafts a plan to modify R files, it first operates in a read-only plan agent, presenting its proposed steps. Before switching to a build agent to execute changes, it seeks user approval. Crucially, for every file edit, Atlas computes a unified diff, which is surfaced for your review and approval before it writes any changes to your R files. This means you see exactly what R code Atlas intends to change, whether it's in an `R/` file, a `DESCRIPTION` file, or a `tests/testthat/` block. This granular control ensures that you, the R developer, are always in the loop and can approve or reject any proposed modification, maintaining the integrity of your R package and `renv` environment.
Step by step
- 01Use Atlas's `bash` tool to run the specific failing `testthat` test, for example: `atlas bash -- devtools::test(filter = 'my_failing_test')`.
- 02Read the `testthat` assertion and the R module it exercises, then use Atlas's `lsp` tool with `goToDefinition` to trace the R function's source.
- 03Walk the R call path further using `lsp`'s `findReferences` to understand function interactions within your R package.
- 04Form a hypothesis and check it: add temporary `print()` or `message()` logging to R files with Atlas's `edit` tool, or re-run the test with a verbose flag via `bash`.
- 05Fix the production R code using Atlas's `edit` tool for small changes, or `apply_patch` for more extensive modifications across several hunks.
- 06Re-run the single `testthat` test with `atlas bash -- devtools::test(filter = 'my_failing_test')` to confirm the fix.
- 07Run the full `testthat` suite with `atlas bash -- devtools::test()` to ensure no regressions were introduced.
- 08Remove any temporary logging from your R files using Atlas's `edit` tool, leveraging its git patch snapshots for easy rollback.
Frequently asked questions
- How does Atlas run a specific `testthat` test in R?
- Atlas uses its `bash` tool to execute `devtools::test()` with a `filter` argument. For example, `atlas bash -- devtools::test(filter = 'my_specific_test')` will run only the R test matching that pattern, providing focused output for debugging.
- Can Atlas help me understand the call stack of a failing R function?
- Yes, Atlas's `lsp` tool is designed for this. It can perform `goToDefinition` to jump to function declarations and `findReferences` to see where R functions are called, effectively mapping out the call graph within your R package.
- What R tools does Atlas integrate with for debugging?
- Atlas integrates deeply with the R toolchain. It uses `testthat` for running tests, understands `DESCRIPTION`-based packages, and can read `renv.lock` for package dependencies. It also supports formatting R code with `Air`.
- How does Atlas ensure I approve R code changes?
- Atlas operates with a strong emphasis on user control. Every proposed R file edit is presented as a unified diff for your review and explicit approval before it is written. All tool calls are also permission-gated, ensuring you maintain full oversight.
- Can Atlas help me refactor R code using `dplyr` or `purrr`?
- Yes, Atlas can assist with R code refactoring. It can be asked to convert traditional `for` loops into more idiomatic `dplyr` or `purrr` pipelines, leveraging its understanding of R syntax and common patterns.
- How does Atlas handle temporary debugging code in R?
- Atlas allows you to add temporary `print()` or `message()` statements to your R code using its `edit` tool. Since Atlas snapshots file changes as git patches, it's straightforward to remove these temporary additions after debugging is complete, ensuring a clean codebase.
Try Atlas in your terminal
The terminal-native AI coding agent. Free core, single binary.
Install AtlasRelated guides
Debug a Single Failing Test with Atlas in 2026
How to debug one failing test with Atlas in 2026: run it in isolation with bash, walk the call graph with the lsp tool, and fix the code, not the assertion.
Atlas for R: A Terminal-Native AI Coding Agent for tidyverse, roxygen2, and testthat in 2026
Atlas is a terminal-native AI coding agent for R in 2026. It reads roxygen2 docblocks and renv.lock, rewrites loops as dplyr or purrr pipelines, and runs devtools::test().
Run the test suite and triage failures in R with Atlas in 2026
Efficiently triage R test failures with Atlas. Turn a wall of red testthat output into a prioritized list of distinct root causes, leveraging renv and Air.
Write unit tests for untested code in R with Atlas in 2026
In 2026, R developers use Atlas to add robust testthat unit tests to existing modules. Atlas reads DESCRIPTION files, copies testthat conventions, and runs devtools::test().
Document a module with a README in R with Atlas in 2026
In 2026, R developers use Atlas to generate accurate, up-to-date README documentation for their R packages. Atlas leverages `lsp`, `read`, and `grep` to describe what R code actually does, not what it was intended to
Trace a runtime bug from a stack trace in R with Atlas in 2026
Pinpoint and fix runtime bugs in R applications using Atlas, the terminal-native AI coding agent. Go from a production stack trace to a precise fix without a debugger attached, leveraging R's `testthat` and `renv`.
Review a Pull Request in R with Atlas in 2026
In 2026, R developers use Atlas to review pull requests, catching subtle bugs by examining diffs with full file context, running `testthat` suites, and checking `renv` dependencies. Atlas ensures robust R code reviews.
Plan a Multi-File Change Before Editing in R with Atlas in 2026
In 2026, R developers use Atlas to design and review multi-file changes across DESCRIPTION packages and testthat suites before modifying any code. Ensure your renv dependencies and Air formatting are considered in a