R Cursorrules Prompt File Best Practices

Meta-Regel: wie man .cursorrules-Dateien selbst gut strukturiert, statt eine für ein bestimmtes Framework.

Original-Beschreibung der Autoren: Cursor rules for R development with best practices integration.

Die Regel

---
description: "Cursor rules for R development with best practices integration."
globs: **/*
alwaysApply: false
---
You are an R programming assistant, make sure to use the best practices when programming in R:

## Project Structure and File Organization
- Organize projects into clear directories: 'R/' (scripts), 'data/' (raw and processed), 'output/' (results, plots), 'docs/' (reports). For R packages, use 'inst/' for external files; for non-packages, consider 'assets/'.
- Use an 'Rproj' file for each project to manage working directories and settings.
- Create reusable functions and keep them in separate script files under the 'R/' folder.
- Use RMarkdown or Quarto for reproducible reports combining code and results. Prefer Quarto if available and installed.
- Keep raw data immutable; only work with processed data in 'data/processed/'.
- Use 'renv' for dependency management and reproducibility. All the dependencies must be installed, synchronized, and locked.
- Version control all projects with Git and use clear commit messages.
- Give a snake_case consistent naming for the file names. The file names should not be too long.
- Avoid using unnecessary dependencies. If a task can be achieved relatively easily using base R, use base R and import other packages only when necessary (e.g., measurably faster, more robust, or fewer lines of code).

## Package Structure
- If the R project is an R package, make sure to mention the dependencies used inside the package within the 'DESCRIPTION' file. All dependencies must have their version number mentioned (e.g: R6 (>= 2.6.1))
- If the R project is an R package, make sure a 'LICENSE' file is available. 
- If the R project is an R package, make sure a 'NEWS.md' file is available which should track the package's development changes.
- If the R project is an R package, make sure that each external file used inside the package is saved within the 'inst' folder. Reading the file should be done using the 'system.file' function. 
- If the R project is an R package, Always use 'devtools::load_all' before testing the new functions. 
- If the R project is an R package, run 'devtools::check()' to ensure the package has no issues. Notes are okay; avoid warnings and errors.
- If the R project is an R package, document functions using roxygen2. Use 'devtools::document()' to generate the required documentation (.Rd files) and 'NAMESPACE' file.

## Naming Conventions
- snake_case: variables and functions (e.g., \`total_sales\`, \`clean_data()\`). 
- UpperCamelCase: for R6, S3, S4, S7 class names (e.g., \`LinearModel\`).
- SCREAMING_SNAKE_CASE: constants and global options (e.g., \`MAX_ITERATIONS\`).
- Avoid ambiguous names (e.g., use \`customer_id\` instead of \`id\`).
- Use verbs for function names (e.g., \`plot_data\`, \`calculate_mean\`).
- Avoid function or variable names that has already been assigned by R, for example avoid 'sd', it's already a function in R. Another example would be 'data'.
- When working with R6 classes, always prepend a '.' to private methods and fields. An example of a method would be '.get_data()' which will be used as 'private$.get_data()'. 
    
## Coding Style
- Follow the [tidyverse style guide](https://style.tidyverse.org/).
- Use spaces around operators (\`a + b\`, not \`a+b\`).
- Keep line length <= 80 characters for readability.
- Use consistent indentation (2 spaces preferred).
- Use '#' for inline comments and section headers. Comment only when necessary (e.g., complex code needing explanation). The code should be self‑explanatory.
- Write modular, reusable functions instead of long scripts.
- Prefer vectorized operations over loops for performance.
- Always handle missing values explicitly (\`na.rm = TRUE\`, \`is.na()\`).
- When creating an empty object to be filled later, preallocate type and length when possible (e.g., 'x <- character(length = 100)' instead of 'x <- c()').
- Always use <- for variables' assignment, except when working with 'R6' classes. The methods inside the 'R6' classes are assigned using '='
- When referencing a function from a package always use the '::' syntax, for example 'dplyr::select'
- Always use 'glue::glue' for string interpolation instead of 'paste0' or 'paste'
    
## Performance and Optimization
- Profile code with \`profvis\` to identify bottlenecks.
- Prefer vectorized functions and the apply family ('apply', 'lapply', 'sapply', 'vapply', 'mapply', 'tapply') or 'purrr' over explicit loops. When using loops, preallocate type and memory beforehand.
- Use data.table for large datasets when performance is critical and data can fit in memory.
- When reading a CSV, prefer 'data.table::fread' or 'readr::read_csv' depending on the codebase. If the codebase is tidyverse‑oriented, prefer 'readr'; otherwise use 'data.table'.

- Use duckdb when data is out of memory.
- Avoid copying large objects unnecessarily; use references when possible.
    
## Testing and Validation
- Write unit tests with \`testthat\`.
- Use reproducible random seeds (\`set.seed()\`) for consistent results.
- Test functions with edge cases (empty inputs, missing values, outliers).
- Use R CMD check or \`devtools::check()\` for package development.
    
## Reproducibility
- Use RMarkdown or Quarto for reproducible reports combining code and results. Prefer 'Quarto' if already available and installed.
- Capture session info with \`sessionInfo()\` or \`sessioninfo::session_info()\`.
- Pin package versions with \`renv\`.
- Store scripts, data, and results in version control.
- Document all analysis steps in README or report files.
    
## Collaboration and Documentation
- Write docstrings using roxygen2 for functions and packages.
- Maintain a clear README with project goals, setup instructions, and usage.
- Use descriptive commit messages and branches for feature development.
- Share results via HTML/PDF reports or dashboards (Shiny, flexdashboard).
- Comment code for clarity, but prefer self-explanatory variable and function name
… (hier gekürzt — Kopieren/Download liefert die vollständige Regel)

So nutzt du sie

Die Regel kopieren (Button oben) oder als Datei herunterladen und im Projekt unter .cursor/rules/ ablegen — Cursor lädt sie beim nächsten Start automatisch. Ältere Cursor-Versionen lesen alternativ eine einzelne .cursorrules-Datei im Projektstamm; dort einfach den Regel-Text ohne den Kopfblock zwischen den ----Zeilen einfügen.

Der Regel-Text ist englisch — Cursor versteht ihn unabhängig von der Sprache, in der Sie mit dem Editor chatten.

Im Detail

Diese Regel behandelt nicht ein Framework, sondern das Schreiben von .cursorrules-Dateien selbst: Wie strukturiert man Prompt-Regeln so, dass Cursor sie zuverlässig befolgt? Sie gibt Hinweise zu Aufbau (klare Abschnitte, konkrete Beispiele statt vager Floskeln), Länge (kurz und eindeutig statt Roman) und Priorisierung von Anweisungen. Nützlich für alle, die eigene Projekt- oder Team-Regeln für Cursor schreiben wollen, statt fertige Vorlagen zu kopieren. Kein Ersatz für sprachspezifische Regeln, sondern die Meta-Ebene darüber — hilfreich, wenn bestehende Cursor-Regeln in einem Projekt nicht greifen oder widersprüchlich wirken.

Praxis-Tipp

Vor dem Schreiben eigener .cursorrules diese Regel als Checkliste nutzen, z. B. mit dem Prompt „Prüfe meine .cursorrules-Datei gegen die Best-Practices-Regeln und schlage Kürzungen vor“.

Lizenz & Quelle

• Lizenz: CC0 1.0
• Quelle: PatrickJS/awesome-cursorrules (GitHub)

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