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LLMs-Universal-Life-Science-and-Clinical-Skills- R_Programming

R Programming for Biomedical Science

install
source · Clone the upstream repo
git clone https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- "$T" && mkdir -p ~/.claude/skills && cp -r "$T/Skills/Software_Engineering/R_Programming" ~/.claude/skills/mdbabumiamssm-llms-universal-life-science-and-clinical-skills-r-programming && rm -rf "$T"
manifest: Skills/Software_Engineering/R_Programming/SKILL.md
source content

R Programming for Biomedical Science

Overview

R is a premier language for statistical computing and graphics, serving as the backbone for much of modern bioinformatics, particularly through the Bioconductor project. Mastery of R is essential for genomic data analysis, rigorous statistical testing, and publication-quality visualization.

Core Competencies

1. Base R & Functional Programming

  • Data Structures: Vectors, Lists, Data Frames, Matrices, Factors.
  • Control Flow: Apply family (
    lapply
    , 
    sapply
    , 
    tapply
    ) vs. loops.
  • S3/S4 Classes: Understanding R's object-oriented systems, critical for Bioconductor packages.

2. Tidyverse (Modern Data Science)

  • dplyr: Data manipulation (filter, select, mutate, group_by, summarize).
  • tidyr: Tidy data principles (pivot_longer, pivot_wider).
  • readr: Efficient data import.
  • purrr: Functional programming tools.
  • magrittr: Pipe operators (
    %>%
    , 
    |>
    ).

3. Data Visualization

  • ggplot2: Grammar of graphics, layering, themes, and scales.
  • ComplexHeatmap: Advanced heatmaps for omics data.
  • Patchwork/Cowplot: Composing multi-panel figures.

4. Bioconductor Ecosystem

  • Core Classes: 
    SummarizedExperiment
    , 
    SingleCellExperiment
    , 
    GRanges
    .
  • Package Management: 
    BiocManager
    .
  • Workflow: Integration with downstream tools (DESeq2, edgeR, limma).

5. Statistics

  • Hypothesis Testing: t-tests, ANOVA, linear models (
    lm
    , 
    glm
    ).
  • Dimensionality Reduction: PCA, t-SNE, UMAP.
  • Correction: False Discovery Rate (FDR), Benjamini-Hochberg.

6. Package Development

  • Structure: DESCRIPTION, NAMESPACE, R/ directory.
  • Documentation: roxygen2.
  • Testing: testthat.
  • Check: 
    R CMD check
    compliance.

Learning Path

  1. Novice: Master 
    dplyr
    verbs and 
    ggplot2
    basics. Understand factors.
  2. Intermediate: Learn to use 
    Bioconductor
    classes (
    SummarizedExperiment
    ). Perform differential expression analysis.
  3. Advanced: Build reusable R packages. Optimize code with 
    Rcpp
    . Create interactive Shiny apps.

Key Resources