Concluding Remarks 🎉

Materials adapted from Adrien Osakwe, Larisa M. Soto and Xiaoqi Xie.

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1. What We Learned Today

We have covered the foundational pillars of R programming for bioinformatics:

• The Ecosystem: Understanding the difference between Base R, RStudio, CRAN, BioConductor, and GitHub.

• The Basics: Mastering data types (vectors, factors, lists) and the “home base” of the Working Directory.

• Data Wrangling: Using dplyr verbs and the Pipe (%>%) to turn messy spreadsheets into clean tables without creating intermediate “clutter.”

• Visualization: Moving beyond basic plots to the layered “Grammar of Graphics” in ggplot2.

• Reproducibility: Learning how to write robust functions and manage project scripts.

2. What’s Next?

Today was about the mechanics of R. In the next stage, we move toward interpretation.

Statistics in R Workshop (Part 2)

• Data Wrangling: Advanced joining of datasets (merging clinical data with genomic counts).

• Statistical Analysis: Performing T-tests, ANOVA, and correlation studies.

• Regression: Building linear models to understand the relationship between variables.

• Reporting: Using Quarto to create automated PDF or HTML reports for your PI.

3. Resources for Continued R Learning

As you continue your R study, these resources will be your best friends:

• Bioconductor Tutorials

• R Cheat Sheets

• R for Data Science (2e)

• R Graphics Cookbook, 2nd edition

• R in Action, Second Edition

• Hands-On Programming with R

• The Epidemiologist R Handbook

• R for Non-Programmers: A Guide for Social Scientists

• Codecademy: Learn R Free Course: Good for those who prefer an interactive, “in-browser” coding experience to practice syntax.

• McGill CDSI Workshops: Our colleagues at the Computational and Data Systems Institute offer excellent deep-dives into specific data topics.

• McGill QLS-MiCM Workshops: Keep an eye on future MiCM workshops for specialized bioinformatics and high-performance computing training.

4. More workshops

https://www.mcgill.ca/micm/training/workshops-series

The following is a list of QLS-MiCM workshops offered in the previous year:

• How to think in Code

• Intro to Git & GitHub

• Intro to Unix

• Intro to R (Part 1)

• Statistics in R (Part 2)

• Intro to Python (Part 1)

• Intermediate Python (Part 2)

• Intro to MATLAB

• RNA-seq Bootcamp (DNA2RNA || CDSI)

• Data Processing in Python

• Intro to Machine Learning

• Intro to Molecular Simulations

• Proteogenomics

• Data Processing for Genetics

• Polygenic Risk Scores

5. Thank you for attending! & Feedback Form

You are welcome to scan the QR code below or just click here to share your feedback on today’s workshop. Any suggestions for improvement are warmly welcome!