Getting Started in R (2025)
Thomas O’Neil
Last updated: 2025-01-06
Checks: 2 0
Knit directory: analysis-user-group/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 8eec2ce | DrThomasOneil | 2025-01-06 | Initial Deployment |
| html | 660b0f8 | DrThomasOneil | 2025-01-06 | Build site. |
| html | 2e79a1d | DrThomasOneil | 2025-01-06 | Build site. |
| Rmd | 451a21f | DrThomasOneil | 2025-01-06 | Initial Deployment |
| Rmd | 2eeb8dc | DrThomasOneil | 2024-12-26 | first draft |
This 10-session workshop series is designed to introduce users to R programming with a focus on bioinformatics workflows and reproducibility. It emphasizes the mindset shift required to transition from manual tools like Excel to programmatic data analysis.
Session 0: What Is Programming? Shifting Mindsets
Goal:
Introduce programming concepts and the mindset behind programmatic
workflows.
- Transition from manual tools (e.g. Excel & Prism) to
programming-based workflows.
- Understand the concept of handling data using code.
- Learn to navigate directories and file paths programmatically.
- Emphasize reproducibility and structured workflows.
Practice:
- Navigating directories (getwd(),
setwd()).
- Creating folders and listing files.
Session 1: Getting Started with R and RStudio
Goal: Set up R and RStudio, and get familiar with the interface.
- Installing R and RStudio.
- Exploring the interface – Console, Environment, Scripts.
- Setting up a project.
- Installing and loading packages.
Practice: - Create and save an R script. - Install
and load a package (e.g., tidyverse).
Session 2: Data Types and Structures
Goal: Learn about data types and structures in R.
- Basic data types: numeric, character, logical.
- Data structures: vectors, matrices, data frames, lists.
- Importing data (Excel and CSV files).
- Viewing and summarizing data.
Practice: - Load flow cytometry data and explore structure. - Create and index vectors and data frames.
Session 3: Data Manipulation with
dplyr
Goal: Introduce dplyr for filtering,
mutating, and summarizing data.
- Filtering rows (
filter()), selecting columns (select()). - Adding new columns (
mutate()). - Grouping and summarizing (
group_by()+summarize()).
Practice: - Calculate proportions for CD4/CD8 populations. - Add calculated columns for analysis.
Session 4: Basic Programming and Control Structures
Goal: Understand programming logic for automating tasks.
- Variables and assignments.
- Conditional statements (
if,else). - Loops (
for,while). - Writing functions.
Practice: - Write functions to calculate percentages dynamically. - Automate filtering tasks.
Session 5: Visualization with ggplot2 – Part
1
Goal: Create and customize visualizations.
- Basics of
ggplot2– scatter, bar, and boxplots. - Customizing labels, themes, and colors.
Practice: - Visualize CD4 vs CD8 proportions. - Add colors and themes.
Session 6: Advanced Visualization with ggplot2
– Part 2
Goal: Learn advanced visualization techniques.
- Faceting and small multiples.
- Combining plots (grid layouts).
- Saving high-resolution plots.
Practice: - Create faceted boxplots for subsets. - Export plots for reports.
Session 7: Statistical Analysis
Goal: Understand descriptive and inferential statistics.
- Descriptive statistics: mean, median, mode.
- Hypothesis testing (t-tests, ANOVA).
- Correlation and regression.
Practice: - Test differences in CD4 proportions. - Perform correlation analysis.
Session 8: Reproducible Reports with RMarkdown
Goal: Build dynamic and shareable reports.
- Introduction to RMarkdown.
- Combining text, code, and visuals.
- Exporting to PDF and HTML.
Practice: - Create a report summarizing flow cytometry data. - Embed visualizations and tables.
Session 9: Mini Project – Putting It All Together
Goal: Apply skills to a complete workflow.
Practice: - Load and clean data. - Summarize and visualize trends. - Run tests and compile everything into a report.
Session 10: Troubleshooting and Workflow Design
Goal: Teach debugging strategies and workflow optimization.
- Debugging errors and warnings.
- Writing modular scripts (functions).
- Organizing larger projects.
- Brief intro to Git/GitHub for version control.
Practice: - Debug a script with errors. - Restructure code for reusability.
