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: 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
polarsas the backend for fast data manipulation usingdplyrAPI. Also, note that Python's polars and R's polars share a very similar API (replacepl.withpl$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:
- 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:
- 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.