README.md

Resources for learning (and getting better with) 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

• Introduction to species distribution modelling in R: a short, half-day introduction to species distribution modelling in R, including a brief overview of the concept of species distribution modelling, and an introduction to the main modelling steps
• A very brief introduction to species distribution models in R: this introductory tutorial will show you how to turn your coordinate data into a range map
• Species distribution modelling with R (PDF): provides an introduction to species distribution modelling
• Introduction to Species Distribution Models: video presentation & example code
• Species Distribution Model: Generalized Linear Models: a model that can be applied in univariate and multivariate applications, and used to estimate an ecological response as a linear combination of independent predictor variables
• Presentation Manual for BIOMOD (PDF): a platform for ensemble forecasting of species distributions, enabling the explicit treatment of model uncertainties and the examination of species-environment relationships (see related paper in the articles folder, or online here)
• Introduction to species distribution modelling in R: a short, half-day introduction to species distribution modelling in R, including a brief overview of the concept of species distribution modelling, and an introduction to the main modelling steps.

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:

• Visual Statistics. Use R! by Alexey Shipunov
• Using R for Data Analysis and Graphics - Introduction, Examples and Commentary by John Maindonald
• Practical Regression and Anova using R by Julian Faraway
• Statistical Computing and Graphics Course Notes by Frank E. Harrell: includes material on S, LaTeX, reproducible research, making good graphs, brief overview of computer languaes, etc.
• An Introduction to R: Software for Statistical Modelling & Computing by Petra Kuhnert and Bill Venables
• Introduction to the R Project for Statistical Computing for Use at the ITC by David Rossiter
• Statistics Using R with Biological Examples by Kim Seefeld and Ernst Linder
• IcebreakeR by Andrew Robinson
• Applied Statistics for Bioinformatics Using R by Wim Krijnen
• An Introduction to R by Longhow Lam
• R and Data Mining: Examples and Case Studies by Yanchang Zhao
• A Student's Guide to R by Nicholas J. Horton, Randall Pruim, and Daniel T. Kaplan
• R for Beginners by Emmanuel Paradis
• Kickstarting R (version 1.6) compiled by Jim Lemon
• R for Windows Users (version 2.0) by Ko-Kang Wang
• Building Microsoft Windows Versions of R and R packages under Intel Linux by Jun Yan and A. J. Rossini
• The R language — a short companion by Marc Vandemeulebroecke
• Fitting Distributions with R by Vito Ricci
• The Friendly Beginners' R Course by Toby Marthews
• An R companion to ‘Experimental Design’ by Vikneswaran
• The R Guide (version 2.5) by Jason Owen
• Multilevel Modelling in R by Paul Bliese: a brief introduction to R and the packages multilevel and nlme
• Using R for Scientific Computing by Karline Soetaert: lecture notes and reference card for R beginners, including exercises
• Creating R Packages: A Tutorial by Friedrich Leisch
• Creating R Packages, Using CRAN, R-Forge, and Local R Archive Networks and Subversion Repositories by Spencer Graves and Sundar Dorai-Raj
• R for Biologists by Marco Martinez
• An introduction to data cleaning with R by Edwin de Jonge and Mark van der Loo
• Introduction to visualising spatial data in R by Robin Lovelace and James Cheshire