This reproducible R Markdown
analysis was created with workflowr (version
1.7.1). The Checks tab describes the reproducibility checks
that were applied when the results were created. The Past
versions tab lists the development history.
Great! You are using Git for version control. Tracking code development
and connecting the code version to the results is critical for
reproducibility.
The results in this page were generated with repository version
451a21f.
See the Past versions tab to see a history of the changes made
to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Note that any generated files, e.g. HTML, png, CSS, etc., are not
included in this status report because it is ok for generated content to
have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown (analysis/0_start.Rmd) and HTML
(docs/0_start.html) files. If you’ve configured a remote
Git repository (see ?wflow_git_remote), click on the
hyperlinks in the table below to view the files as they were in that
past version.
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.
- What is programming and why use R?
- Moving from manual Excel edits to reproducible code.
- Working with directories and file paths.
- Scripts vs manual edits – automating tasks.
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.
Final Thoughts
This series is designed to equip participants with both conceptual
and practical skills to confidently approach programming and data
analysis. It emphasizes reproducibility, scalability, and structured
workflows, preparing learners for real-world bioinformatics
challenges.