Last updated: 2025-12-23

Checks: 1 1

Knit directory: data_visualization_in_R/

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.

R Markdown file: uncommitted changes

The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

Repository version: bf3d061

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 bf3d061. 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: 


Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    .claude/

Unstaged changes:
    Modified:   analysis/02_intro_to_ggplot2.Rmd
    Modified:   analysis/03_heatmap_demystified.Rmd
    Modified:   analysis/04_practical_scRNAseq_viz.Rmd
    Modified:   analysis/about.Rmd
    Modified:   analysis/index.Rmd

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/about.Rmd) and HTML (docs/about.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.

File Version Author Date Message
Rmd 8c9b351 crazyhottommy 2025-11-02 first commit
html 8c9b351 crazyhottommy 2025-11-02 first commit
Rmd 9e1856e crazyhottommy 2025-10-07 Start workflowr project.

About This Workshop

This Data Visualization in R Workshop is designed specifically for biology students, researchers, and data analysts who want to master the art and science of creating compelling visualizations using R and ggplot2.

Workshop Philosophy

Most genomics papers rely on just six types of figures: - Scatter plots - Bar plots
- Line plots - Box plots or violin plots - Histograms - Heatmaps

By mastering these six visualization types, you can reproduce 90% of the figures in any genomics paper. This workshop focuses on practical, hands-on learning using real cancer genomics data from The Cancer Genome Atlas (TCGA).

What Makes This Workshop Different

  • Real Data: We use actual TCGA cancer genomics datasets, not toy examples
  • Practical Focus: Every lesson builds skills you’ll use in real research
  • Publication Ready: Learn to create figures suitable for scientific journals
  • Reproducible: Built using the workflowr framework for reproducible research

Target Audience

This workshop is ideal for: - Biology and biomedical students - Researchers working with genomics data - Data analysts in life sciences - Anyone wanting to improve their R visualization skills

Prerequisites

  • Basic knowledge of R programming
  • Familiarity with data frames and basic data manipulation
  • Interest in biological data analysis

Technical Requirements

  • R (version 4.0 or higher)
  • RStudio or Positron IDE
  • Required R packages: tidyverse, ggplot2, readr, dplyr, Polychrome, forcats

Data Sources

The workshop uses gene expression data from The Cancer Genome Atlas (TCGA), one of the largest and most comprehensive cancer genomics datasets available. This provides students with experience working with real-world, high-dimensional biological data.

Wait, I am jumping ahead of myself, actually we are using a pre-processed RNAseq Transcript per million (TPM) data from here.

But if you want to start with the Raw counts file, I have a blog post on how to get the data and convert it to TPM in this blog post.

The single cell RNAseq visualization part will use the commonly used PMBC3k dataset from SeuratData.

Learning Outcomes

After completing this workshop, participants will be able to:

  1. Apply data visualization best practices
  2. Create publication-quality figures using ggplot2
  3. Analyze and visualize cancer genomics data
  4. Customize plots with appropriate order, themes, colors, and annotations
  5. Choose the right visualization type for different data types and research questions
  6. Create the plots from scratch instead of relying on the Seurat wrappers.

Workshop Structure

The workshop is organized as a progressive series of lessons, each building on previous concepts while introducing new techniques. The modular structure allows participants to focus on specific topics or work through the entire curriculum.

Happy visualizing! 📊🧬