TidyVerseModelsOmics

Curated resources for learning Tidyverse, Tidymodels, Tidyomics, and other packages (written in R or other programming languages) built upon the Tidy R paradigm.

R/ directory: Containing Quarto files (.qmd) that are used to document and reproduce data analyses using R.

src/ directory: Containing .qmd files that are used to document and reproduce data analyses using languages other that R.

data/ directory: Data for coding exercises.

reports/ directory: Containing single-page HTML (.html) reports generated by corresponding .qmd files, which are found in R/.

Tidyverse

• Tidyverse: The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.
• R for Data Science (2e) by Hadley Wickham, Mine Cetinkaya-Rundel, and Garrett Grolemund
• Posit Cheatsheets: readr/readxl/googlesheets4, dplyr, tidyr, ggplot2, stringr, purrr, forcats, lubridate
• duckplyr: A DuckDB-backed version of dplyr for ultra-fast data manipulation.
• tidypolars: An ultra-fast library powered by Rust-based polars as the backend for fast data manipulation using dplyr API. Also, note that Python's polars and R's polars share a very similar API (replace pl. with pl$ in R in most cases), so it is super easy to translate polars codes from R to Python or vice versa.
• ggplot2: Elegant Graphics for Data Analysis (3e) by Hadley Wickham
• Data Wrangling and Visualization with R by Rafael Irizarry
• Statistics and Prediction Algorithms Through Case Studies by Rafael Irizarry
• Statistical Inference via Data Science–A ModernDive into R and the Tidyverse by Chester Ismay and Albert Y. Kim
• Modern R with the tidyverse by Bruno Rodrigues
• DataCamp skill tracks for tidyverse:
  • Data Manipulation with R
  • Data Visualization with R
  • Tidyverse Fundamentals with R
  • Intermediate Tidyverse Toolbox

Tidymodels

• Tidymodels Official Website
  • Explore tidymodels: Search all packages and functions about tidymodels
  • Use parsnip::parsnip_addin() to select and write out model specifications to R script
• Tidy Modeling with R by Max Kuhn and Julia Silge (the textbook for tidymodels)
• ISLR tidymodels labs by Emil Hvitfeldt
• Machine learning with tidymodels: posit::conf workshop materials (github)
• Feature Engineering and Selection: A Practical Approach for Predictive Models by Max Kuhn and Kjell Johnson
• Three reasons to use Tidymodels — Julia Silge — R-Ladies East Lansing (English). Julia Silge's slides.
• Supervised Machine Learning Case Studies in R by Julia Silge
• Julia Sige's Blog: Contain a lot of tutorials on Tidymodels
• DataCamp courses for tidymodels:
  • Modeling with tidymodels in R
  • Machine Learning with Tree-Based Models in R
  • Feature Engineering in R

Tidyomics

• Tidyomics: Open project to create tidy analysis packages for omics data (Hutchison et al., 2024).
• plyranges (Lee, et al., 2019): A Grammar of Genomic Data Transformation
  • Tidy Ranges Tutorial by Michael Love
• Tidy Transcriptomics:
  • tidybulk (Mangiola, et al, 2021): An R tidy framework for modular bulk RNA-seq data analysis
  • tidyseurate (Mangiola, et al, 2021): An R tidy framework for modular scRNA-seq data analysis
• tidyHeatmap (Mangiola and Papenfuss, 2020): An R package for modular heatmap production based on tidy principles
• tidyCoverage (Serizay and Koszul, 2024): This framework enables tidy manipulation of collections of genomic tracks and features using `tidySummarizedExperiment` methods.
  • Applying tidy principles to investigating chromatin composition and architecture by Jacques Serizay