General R resources
Building Apps in R
- Shiny from RStudio - Shiny is an R package that makes it easy to build interactive web apps straight from R. You can host standalone apps on a webpage or embed them in R Markdown documents or build dashboards. You can also extend your Shiny apps with CSS themes, htmlwidgets, and JavaScript actions.
- Mastering Shiny (book) - Build interactive apps, reports and dashboards powered by R
- Flex Dashboard - Easy interactive dashboards for R
R Markdown & Quarto
R Markdown presentations
R Markdown and Reproducible Reports
- Examples: Scheel et al. 2020, novice analysis in R of rOpenSci twitter data , thesis in markdown,
- A Reproducible Data Analysis Workflow With R Markdown, Git, Make, and Docker (Peikert and Brandmaier 2021)
- WORCS
- RMarkdown for writing reproducible scientific papers by Frank & Hartgerink
- Packages: spelling, renv, {write_bib} function from {knitr} for auto-citations, groundhogr for reproducibility of packages, KablExtra for tables (Linesep: avoid breaks in the lines for no apparent reason), Flextable, Reproducer, deposits
- Getting started with Zotero, Better BibTeX, and RMarkdown
- Higher, further, faster with Marvelous R Markdown
- bookdown: Authoring Books and Technical Documents with R Markdown and R Markdown: The Definitive Guide
- R Markdown Cookbook
- An Introduction to Writing Reproducible Manuscripts Using RMarkdown
- Pandoc documentation provides more details on automatic section IDs and implicit header references.
- Image sizes in an R markdown Document
Building Websites in R
- Blogdown - Provides a powerful and customizable website output format for R Markdown
- Build a website with blogdown by Tatjana Kecojevic
- Binder - Have a repository full of Jupyter notebooks? With Binder, open those notebooks in an executable environment, making your code immediately reproducible by anyone, anywhere.
- Netlify - Build, deploy & scale modern web projects
Dependency management & Docker
Data Visualisation
Ggplot2
Colours
Data validation
Data cleaning and data manipulation
Integrations with other Software
- Using R and Tableau - Tableau Desktop can now connect to R through calculated fields and take advantage of R functions, libraries, packages and even saved models. These calculations dynamically invoke the R engine and pass values to R via the Rserve package, and are returned back to Tableau.
- Exploratory.io - Exploratory Desktop provides a Simple and Modern UI experience to access various Data Science functionalities including Data Wrangling, Visualization, Statistics, Machine Learning, Reporting, and Dashboard. It is built on R so you can easily Extend it with thousands of open source packages to meet your needs.
- JASP - GUI based in R that allows you to conduct statistical analyses in seconds, without programming. Offers both frequentist and Bayesian analysis methods.
- Jamovi - free and open statistical software built on R. Would you like the R code for your analyses? Jamovi can provide that too.
- JAMOVI / JASP / R / Rmarkdown collaborative manual by Gilad Feldman
R + Python
- Reticulate - R interface to Python
- Python and R for the Modern Data Scientist (book) - This book guides data scientists from the Python and R communities along the path to becoming bilingual. By recognizing the strengths of both languages, you’ll discover new ways to accomplish data science tasks and expand your skill set.
R + GitHub
R package repositories
- ROpenSci - help develop R packages for the sciences via community driven learning, review and maintenance of contributed software in the R ecosystem.
R for Reproducible Research
- Reproducibility course in R by Elio Campitelli and Paola Corrales
- R Workflow by Frank E Harrell Jr
- PsyTeachR - curriculum that emphasizes essential ‘data science’ graduate skills that have been overlooked in traditional approaches to teaching, including programming skills, data visualisation, data wrangling and reproducible reports.
- R for Reproducible Research - This course focuses on data and project management through R and Rstudio, will introduce students to best practice and equip them with modern tools and techniques for managing data and computational workflows to their full potential.
- Reproducible Research in R - An introductory workshop on modern data analyses and workflows.
- Reproducible Analyses in R (beginner)
- Reproducible Research with R (research compendia and rrtools, Holepunch and Binder, ReproHack)
- Reproducible research with workflowr
- Reproducible Science with R and Rstudio by Olivier Gimenez
- Improve your workflow for reproducible science (intermediate)
- https://workflowr.io/, a central website for discovering and sharing reproducible research projects created with the #rstats package
- Tidy data, paper by Hadley Wickham explaining a central concept of the tidyverse
- Filling your bag of workflow tricks by María Paula Cadras
- Automating Computational Reproducibility in R using renv, Docker, and GitHub Actions by Nathaniel Haines
- Building reproducible analytical pipelines with R by Bruno Rodrigues
R beginner materials
R & Statistics
Spatial
Data Cleaning
R Package development
Tables
Creating beautiful tables in R with {gt}
Other