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Resources for learning (and getting better with) R

R

Various online & other resources for learning and improving skills in the R programming language. This is a growing list.

Online resources

  • Introduction to R: a free, 10-hour course to learn the core fundamentals of the R language for interactive use as well as programming
  • Getting Started with R: a free course designed to get new users, no matter what's holding you back, up and running quickly
  • R for the Rest of Us — Resources: a carefully curated collection of resources to help you find packages and learning resources to help you on your R journey
  • R4All: specialises in teaching R to beginners, and improving the workflow of experienced users
  • Quick-R: designed for people who want to transition to R
  • RforEcology: a shortcut to learning R quickly, but effectively
  • R Bootcamp: R shortcourses and modules
  • R for Data Science: will teach you how to do data science with R
  • Data carpentry: R for data analysis and visualisation of ecological data
  • Efficient R programming: online book showing ways to increase computational and programmer efficiency
  • Environmental Computing: a brief introduction to techniques for data organisation, graphics and analyses
  • Data Science for Ecologists & Environmental Scientists: a free and self-paced journey through a tailored selection of Coding Club tutorials, quizzes and practical challenges
  • Forecasting: Principles and Practice: a textbook providing a comprehensive introduction to forecasting methods
  • CRANt Touch This: notes and resources of things to check before submission to the CRAN
  • ggplot2: a system for declaratively creating graphics
  • tidyverse: an opinionated collection of R packages designed for data science
  • Shiny: build interactive web apps straight from R
  • Mastering Shiny: this book complements Shiny’s online documentation and is intended to help app authors develop a deeper understanding of Shiny
  • rmarkdown: a productive notebook interface to weave together narrative text and code to produce elegantly formatted output
  • The R Graph Gallery: a collection of charts made with the R programming language, with their reproducible code

Species distribution modelling

Population dynamics

  • Quantitative methods for population dynamics: This two-day workshop deals with the analysis and modelling of population dynamics, including population-projection matrix models, population viability analyses, estimation of demographic parameters (e.g., survival, dispersal) using capture-recapture models, and estimation of population density/abundance using capture-recapture, N-mixture, and distance sampling models.

Ecological networks

  • NetworkExtinction: An R package to simulate extinction propagation and rewiring potential in ecological networks (see related paper in the articles folder, or online here)
  • network: Tools to create and modify network objects in R
  • igraph: Routines for simple graphs and network analysis in R

Boosted regression trees

  • Boosted regression trees: a working guide to boosted regression trees using the gbm package in R (see related paper in the articles folder, or online here)

Online communities (Q & A)

  • stackoverflow: a collaboratively edited question and answer site for professional and enthusiast programmers
  • R-help: the main R mailing list, for announcements about the development of R and the availability of new code, questions and answers about problems and solutions using R, enhancements and patches to the source code and documentation of R
  • R-bloggers: a blog aggregator of content contributed by bloggers who write about R
  • Revolutions (blog): dedicated to news and information of interest to members of the R community
  • R-statistics (blog): statistics with R, and open source stuff (software, data, community)
  • RDataMining: presents examples on using R for data mining applications
  • Stats and R: a blog about statistics and applications in R
  • Nice R Code: by ‘nicer’ we mean code that is easy to write and read, runs fast, gives reliable results, is easy to reuse in new projects, and is easy to share with collaborators
  • @rfunctionaday: R function a day to keep the madness away (Twitter account)

R cheatsheets

In the cheatsheets folder, you can download any of the 70+ different R cheatsheets as a PDF, covering everything from the basics, plotting, cartography, databasing, applications, time series analysis, machine learning, time & date, building packages, parallel computing, resampling methods, markdown, and more.

Electronic books

In the books folder, you can download the following books in PDF format:

  • Dalgaard 2008. Introductory Statistics with R
  • Zuur et al. 2009. A Beginner's Guide to R

Contributed R Documentation

These resources (mostly large PDFs, but some HTML sites & ZIP files) are sourced from the CRAN area for contributed documentation:

Centre of Excellence for Australian Biodiversity and Heritage