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    R Engineering

    1

    Turn interactive R scripts into production-grade packages and reproducible data pipelines.

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    R Engineering

    R Engineering

    Example session with this skill installed

    Refactor this R script for a linear regression analysis. It currently uses global variables and copy-pasted plotting code. Make it reproducible and modular.

    • Read your context and instructions
    • Compiled the r engineering

    I have refactored the analysis script into a functional workflow. I separated the data simulation, model fitting, and visualization into pure functions and added an explicit seed for reproducibility.

    Refactored R Analysis

    #' @title Linear Analysis Workflow
#' @description Modularized regression analysis with ggplot2 output.

library(tidyverse)

#' Generate synthetic data with explicit seed
generate_analysis_data <- function(n = 100, seed = 42) {
  set.seed(seed)
  tibble(
    x = rnorm(n),
    y = 2 * x + rnorm(n, sd = 0.5)
  )
}

#' Fit linear model and return summary
fit_regression_model <- function(base_data) {
  lm(y ~ x, data = base_data)
}

#' Generate standardized diagnostic plot
plot_regression_results <- function(base_data, model) {
  ggplot(base_data, aes(x = x, y = y)) +
    geom_point(alpha = 0.6) +
    geom_smooth(method = "lm", color = "royalblue") +
    labs(
      title = "Linear Regression Analysis",
      subtitle = paste("R-squared:", round(summary(model)$r.squared, 3)),
      x = "Independent Variable (x)",
      y = "Observed Value (y)"
    ) +
    theme_minimal()
}

# Execution Flow
main <- function() {
  data <- generate_analysis_data(n = 150)
  model <- fit_regression_model(data)
  viz <- plot_regression_results(data, model)
  
  print(summary(model))
  return(viz)
}

analysis_plot <- main()
ggsave("regression_output.png", analysis_plot, width = 8, height = 6)

    Next steps

    1. Move these functions into a utils.R file to keep your main execution script clean.
    2. Add unit tests using testthat to verify generate_analysis_data returns the expected dimensions.
    3. Wrap this into a Quarto (.qmd) document for automated reporting.

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    About this skill

    Messy R scripts and brittle analysis pipelines stall production workflows. This skill transforms interactive R sessions into maintainable, reproducible code for data science and statistical modeling.

    What it does

    • Standardizes workflows by enforcing consistent project structures across scripts, Quarto reports, and R packages.
    • Refactors notebooks by extracting repeated logic into reusable, modular functions to reduce technical debt.
    • Optimizes performance through vectorized operations and efficient data manipulation using tidyverse or data.table.
    • Ensures reproducibility by making environment dependencies, file paths, and random seeds explicit.
    • Debugs pipelines by isolating failures related to data shape, factor levels, and missing value handling.

    How it works

    1. Context analysis where the agent inspects your project layout, dependencies, and style conventions.
    2. Logic decoupling to separate data ingestion, cleaning, modeling, and visualization into distinct layers.
    3. Validation against existing test runners or sample data to ensure transformation accuracy.

    Frameworks & tools

    Built for the R ecosystem including tidyverse, data.table, R Markdown, Quarto, and standard R package development workflows.

    Why this beats prompting it yourself

    Generic LLM prompts often produce "copy-paste" snippets that ignore R's specific scoping rules and non-standard evaluation quirks. This skill prioritizes reproducibility and package-oriented structures, ensuring code works in a automated CLI environment, not just an IDE.

    Use cases

    • Converting a loose collection of .R files into a structured, documentable R package.
    • Migrating messy Jupyter or Rmd notebooks into production-ready data pipelines.
    • Debugging non-deterministic errors in complex statistical simulations.
    • Auditing analysis code for proper vectorization and memory efficiency.

    Known limitations

    Does not support low-level C++/Rcpp integration optimization without specific headers. Requires an existing CRAN or Bioconductor environment for full dependency validation.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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