Last updated: 2025-10-15
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This workshop is designed to teach you the fundamentals of creating compelling, publication-quality visualizations using R and ggplot2. Whether you’re a biology student, researcher, or data analyst, mastering data visualization is essential for exploring your data and communicating your findings effectively.
By the end of this workshop, you will be able to:
Learn the fundamental principles of effective data visualization, including when to use different plot types and best practices for visual communication.
Master the core visualization types using real TCGA cancer genomics data: - Scatter plots and correlation analysis - Histograms for distribution exploration - Boxplots and violin plots for group comparisons - Bar plots for summary statistics
Deep dive into creating and customizing heatmaps - the 6th essential plot type for genomics research.
Specialized techniques for visualizing single-cell RNA sequencing data.
tidyverse, readr,
dplyr, ggplot2We’ll be working with real-world data from The Cancer Genome Atlas (TCGA), focusing on gene expression across different cancer types. This provides practical experience with the types of data you’ll encounter in genomics research.
install.packages(c("tidyverse", "readr", "dplyr", "ggplot2", "Polychrome", "forcats"))This workshop uses the workflowr framework for
reproducible research. Each lesson builds upon the previous one, so we
recommend following them in order.
sessionInfo()R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.1
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: America/New_York
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_1.48
[5] rlang_1.1.4 xfun_0.52 stringi_1.8.4 processx_3.8.4
[9] promises_1.3.0 jsonlite_1.8.8 glue_1.8.0 rprojroot_2.0.4
[13] git2r_0.35.0 htmltools_0.5.8.1 httpuv_1.6.15 ps_1.7.7
[17] sass_0.4.9 fansi_1.0.6 rmarkdown_2.27 jquerylib_0.1.4
[21] tibble_3.2.1 evaluate_0.24.0 fastmap_1.2.0 yaml_2.3.10
[25] lifecycle_1.0.4 whisker_0.4.1 stringr_1.5.1 compiler_4.4.1
[29] fs_1.6.4 pkgconfig_2.0.3 Rcpp_1.0.13 rstudioapi_0.16.0
[33] later_1.3.2 digest_0.6.36 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_4.4.1 cachem_1.1.0 getPass_0.2-4