Getting Started in R (2025)
Thomas R. O’Neil
Last updated: 2025-01-07
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| File | Version | Author | Date | Message |
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| Rmd | 0ecb65d | DrThomasOneil | 2025-01-07 | Updated Getting Started in R. Chapter 1 completed |
| html | c893d70 | DrThomasOneil | 2025-01-06 | Build site. |
| 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
Goals:
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.
Chapter 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).
Chapter 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.
Chapter 3: 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.
Chapter 4: 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.
Chapter 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.
Chapter 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.
Chapter 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.
Chapter 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.
Chapter 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.
Chapter 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.
R version 4.4.0 (2024-04-24)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.3
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Australia/Sydney
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] vctrs_0.6.5 httr_1.4.7 cli_3.6.3 knitr_1.49
[5] rlang_1.1.4 xfun_0.49 stringi_1.8.4 processx_3.8.4
[9] promises_1.3.2 jsonlite_1.8.9 glue_1.8.0 rprojroot_2.0.4
[13] git2r_0.35.0 htmltools_0.5.8.1 httpuv_1.6.15 ps_1.8.1
[17] sass_0.4.9 fansi_1.0.6 rmarkdown_2.29 tibble_3.2.1
[21] jquerylib_0.1.4 evaluate_1.0.1 fastmap_1.2.0 yaml_2.3.10
[25] lifecycle_1.0.4 whisker_0.4.1 stringr_1.5.1 compiler_4.4.0
[29] fs_1.6.5 pkgconfig_2.0.3 Rcpp_1.0.13-1 rstudioapi_0.17.1
[33] later_1.4.1 digest_0.6.37 R6_2.5.1 utf8_1.2.4
[37] pillar_1.9.0 callr_3.7.6 magrittr_2.0.3 bslib_0.8.0
[41] tools_4.4.0 cachem_1.1.0 getPass_0.2-4 
