r-data-science

What problem does it solve?

This Skill eliminates the manual, repetitive coding often involved in R-based data analysis, visualization, and statistical workflows. It standardizes practices, accelerates report generation, and ensures adherence to modern R best practices, saving significant time and reducing complexity for data professionals.

Core Features & Use Cases

• Tidyverse-First Data Wrangling: Efficiently clean, transform, and prepare data using dplyr, tidyr, and janitor for analysis.
• Publication-Quality Visualizations: Generate compelling ggplot2 charts and interactive plotly graphics that effectively communicate insights.
• Robust Statistical Analysis: Perform a wide range of statistical tests, regression modeling (linear, logistic, Poisson), and epidemiological calculations (RR, OR, age-adjusted rates).
• Reproducible Research & Reporting: Structure projects for reproducibility using renv for package management and Quarto for dynamic, professional reports and dashboards.
• Use Case: Imagine you need to analyze a new public health dataset, generate an epidemic curve, calculate age-adjusted rates, and produce a professional report. This Skill guides you through the entire process, ensuring best practices and saving you from writing boilerplate code.

Quick Start

Use the r-data-science skill to analyze the attached 'surveillance_data.csv' file, generate an epidemic curve, and summarize key findings in a Quarto report.