Last updated: 2025-10-15
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.
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.
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 9e1856e. 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:
Untracked files:
Untracked: analysis/01_intro_data_viz.Rmd
Untracked: analysis/02_intro_to_ggplot2.Rmd
Untracked: analysis/03_heatmap_demystified.Rmd
Untracked: analysis/04_practical_scRNAseq_viz.Rmd
Unstaged changes:
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 | 9e1856e | crazyhottommy | 2025-10-07 | Start workflowr project. |
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.
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).
workflowr framework for reproducible researchThis 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
tidyverse, ggplot2,
readr, dplyr, Polychrome,
forcatsThe 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.
After completing this workshop, participants will be able to:
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! 📊🧬