How to set up lintr in RStudio?

After installing lintr from CRAN, enable it in RStudio via the lintr addin under Tools > Addins. For detailed setup, refer to the vignette('editors') which covers editor integration, including RStudio configuration and shortcuts.

lintr vs styler: which one should I use for R code?

lintr identifies style and syntax issues for manual fixing, while styler automatically formats code. They are complementary; use both for comprehensive code quality: lintr to catch problems and styler to fix formatting, as noted in the README's complementary relationship.

How to disable a specific linter in lintr?

Create a .lintr configuration file in your project and modify the linter list using functions like lintr::linters_with_defaults(). You can exclude linters by name or adjust settings to suppress unwanted checks.

Does lintr work with R Markdown or Shiny apps?

Yes, lintr can analyze R code within R Markdown chunks and Shiny scripts, treating the R code separately. However, it may not lint non-R parts, so ensure proper configuration for mixed-content projects.

How to integrate lintr with GitHub Actions for CI?

Use usethis::use_github_action('lint-project') for general projects or 'lint' for packages to generate workflow files automatically. This sets up automated linting in your CI pipeline, as described in the README's GitHub Actions section.

What are common false positives in lintr and how to handle them?

Semantic linters might flag unused variables in interactive scripts or valid coding patterns. Use inline comments like # nolint to suppress warnings or adjust linter configurations in .lintr files to reduce noise.

Open-Awesome

lintr

NOASSERTIONRv3.3.0-1

Static code analysis tool for R that checks for style adherence, syntax errors, and potential semantic issues.

Visit Website GitHub

1.3k stars198 forks0 contributors

What is lintr?

lintr is a static code analysis tool for the R programming language that checks code for adherence to style guidelines, syntax errors, and potential semantic issues. It helps R developers identify and fix problems before running their code, improving overall code quality and consistency.

Target Audience

R developers, data scientists, and package maintainers who want to enforce coding standards and catch errors early in their development workflow.

Value Proposition

Developers choose lintr because it provides comprehensive, configurable static analysis specifically designed for R, integrates seamlessly with popular editors and CI systems, and complements automatic formatting tools like styler.

Overview

Static Code Analysis for R

Use Cases

Best For

Enforcing consistent coding style across R projects
Catching syntax errors before running R scripts
Integrating code quality checks into CI/CD pipelines for R packages
Teaching R programming with immediate feedback on code issues
Maintaining large R codebases with multiple contributors
Automating code review processes for R development teams

Not Ideal For

Projects where R code is generated automatically and human readability is not a priority, such as in certain data pipeline scripts
Teams that rely exclusively on automatic formatting with styler and prefer to avoid manual style review processes
Environments focused on rapid interactive data analysis where code execution speed trumps style consistency

Pros & Cons

Pros

Comprehensive Style Enforcement

lintr checks for adherence to coding standards like naming conventions, spacing, and line length, ensuring consistency across R projects as highlighted in its style checking feature.

Early Error Detection

It identifies syntax errors and potential semantic issues before runtime, preventing failures, which is a core part of its syntax validation capability.

Flexible Configuration Options

Supports custom linter sets and defaults like tidyverse, allowing teams to tailor checks to their needs, as shown in the configurable rules and examples in the README.

Strong IDE and CI Integration

Integrates seamlessly with RStudio and GitHub Actions via helper functions, making it easy to incorporate into development workflows, as mentioned in the IDE integration section.

Cons

Performance Overhead on Large Codebases

Running multiple linters on extensive R scripts can slow down development and continuous integration pipelines, especially with complex semantic analysis.

Complex Custom Rule Creation

Developing custom linters requires advanced knowledge of R's abstract syntax tree, which can be challenging for less experienced developers, limiting adaptability.

Language-Specific Limitation

lintr is designed only for R, so it cannot be used for static analysis in multi-language projects, restricting its utility in broader software ecosystems.

Frequently Asked Questions

Related Projects

awesome-awesomeness

A curated list of awesome awesomeness

Stars33,500

Forks3,606

Last commit2 years ago

prophet <img class="emoji" alt="heart" src="https://cdn.jsdelivr.net/gh/qinwf/awesome-R@3c66da6e291bcc0520b1649125b0bed750896a9a/heart.png" height="20" align="absmiddle" width="20">

Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

Stars20,238

Forks4,631

Last commit1 month ago

PHP Parser

A PHP parser written in PHP

Stars17,437

Forks1,123

Last commit3 months ago

TypeScript ESLint

:sparkles: Monorepo for all the tooling which enables ESLint to support TypeScript

Stars16,281

Forks2,917

Last commit1 day ago

Community-curated · Updated weekly · 100% open source

Found a gem we're missing?

Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.

Submit a project Star on GitHub