├── CabahFCP.jpg ├── README.md ├── R_logo.png ├── articles ├── Ávila-Thieme et al. 2023-Methods Ecol Evol.pdf ├── Elith et al. 2008-J Anim Ecol.pdf └── Thuiller et al. 2009-Ecograph.pdf ├── books ├── Dalgaard2008_Book_IntroductoryStatisticsWithR.pdf └── Zuur2009_Book_ABeginnerSGuideToR.pdf └── cheatsheets ├── 0-template.pdf ├── BaseGraphicsCheatsheet.pdf ├── Machine Learning Modelling in R.pdf ├── R short-refcard.pdf ├── R-functions-for-time-series.pdf ├── SamplingStrata.pdf ├── The_essential_R_Cheatsheet_v1_0.pdf ├── base-r.pdf ├── bayesplot.pdf ├── bcea.pdf ├── caret.pdf ├── cartography.pdf ├── collapse.pdf ├── data-import.pdf ├── data-transformation.pdf ├── data-visualization-2.1.pdf ├── data-visualization.pdf ├── datatable.pdf ├── declaredesign.pdf ├── distr6.pdf ├── estimatr.pdf ├── eurostat.pdf ├── factors.pdf ├── gganimate.pdf ├── ggplot2-cheatsheet.pdf ├── golem.pdf ├── gwasrapidd.pdf ├── h2o.pdf ├── how-big-is-your-graph.pdf ├── imputeTS.pdf ├── jfa.pdf ├── keras.pdf ├── labelled.pdf ├── leaflet.pdf ├── lubridate.pdf ├── mlr.pdf ├── mosaic.pdf ├── nardl.pdf ├── nimble.pdf ├── oSCR.pdf ├── overviewR.pdf ├── package-development.pdf ├── packagefinder.pdf ├── parallel_computation.pdf ├── plumber.pdf ├── purrr.pdf ├── quanteda.pdf ├── randomizr.pdf ├── regex.pdf ├── reticulate.pdf ├── rmarkdown-2.0.pdf ├── rmarkdown.pdf ├── rphylopic.pdf ├── rstudio-ide.pdf ├── sas-r.pdf ├── sf.pdf ├── shiny.pdf ├── sjmisc.pdf ├── slackr.pdf ├── sparklyr.pdf ├── stata2r.pdf ├── strings.pdf ├── survminer.pdf ├── syntax.pdf ├── teachR.pdf ├── tidyeval.pdf ├── tidyr.pdf ├── time-series.pdf ├── tsbox.pdf ├── vegan.pdf ├── vtree.pdf └── xplain.pdf /CabahFCP.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CABAH/learningRresources/0668ec5c9d48259195a1003e6f9b69dde56deca1/CabahFCP.jpg -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Resources for learning (and getting better with) R 2 | 3 | R 4 | 5 | Various online & other resources for learning and improving skills in the R programming language. This is a growing list. 6 | 7 | ## Online resources 8 | - Introduction to R: a free, 10-hour course to learn the core fundamentals of the R language for interactive use as well as programming 9 | - Getting Started with R: a free course designed to get new users, no matter what's holding you back, up and running quickly 10 | - 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 11 | - R4All: specialises in teaching R to beginners, and improving the workflow of experienced users 12 | - Quick-R: designed for people who want to transition to R 13 | - RforEcology: a shortcut to learning R quickly, but effectively 14 | - R Bootcamp: R shortcourses and modules 15 | - R for Data Science: will teach you how to do data science with R 16 | - Data carpentry: R for data analysis and visualisation of ecological data 17 | - Efficient R programming: online book showing ways to increase computational and programmer efficiency 18 | - Environmental Computing: a brief introduction to techniques for data organisation, graphics and analyses 19 | - Data Science for Ecologists & Environmental Scientists: a free and self-paced journey through a tailored selection of Coding Club tutorials, quizzes and practical challenges 20 | - Forecasting: Principles and Practice: a textbook providing a comprehensive introduction to forecasting methods 21 | - CRANt Touch This: notes and resources of things to check before submission to the CRAN 22 | - ggplot2: a system for declaratively creating graphics 23 | - tidyverse: an opinionated collection of R packages designed for data science 24 | - Shiny: build interactive web apps straight from R 25 | - Mastering Shiny: this book complements Shiny’s online documentation and is intended to help app authors develop a deeper understanding of Shiny 26 | - rmarkdown: a productive notebook interface to weave together narrative text and code to produce elegantly formatted output 27 | - The R Graph Gallery: a collection of charts made with the R programming language, with their reproducible code 28 | 29 | ## Species distribution modelling 30 | - 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 31 | - 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 32 | - Species distribution modelling with R (PDF): provides an introduction to species distribution modelling 33 | - Introduction to Species Distribution Models: video presentation & example code 34 | - 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 35 | - 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) 36 | - 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. 37 | 38 | ## Population dynamics 39 | - 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. 40 | 41 | ## Ecological networks 42 | - NetworkExtinction: An R package to simulate extinction propagation and rewiring potential in ecological networks (see related paper in the articles folder, or online here) 43 | - network: Tools to create and modify network objects in R 44 | - igraph: Routines for simple graphs and network analysis in R 45 | 46 | ## Boosted regression trees 47 | - 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) 48 | 49 | ## Online communities (Q & A) 50 | - stackoverflow: a collaboratively edited question and answer site for professional and enthusiast programmers 51 | - 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 52 | - R-bloggers: a blog aggregator of content contributed by bloggers who write about R 53 | - Revolutions (blog): dedicated to news and information of interest to members of the R community 54 | - R-statistics (blog): statistics with R, and open source stuff (software, data, community) 55 | - RDataMining: presents examples on using R for data mining applications 56 | - Stats and R: a blog about statistics and applications in R 57 | - 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 58 | - @rfunctionaday: R function a day to keep the madness away (Twitter account) 59 | 60 | ## R cheatsheets 61 | 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. 62 | 63 | ## Electronic books 64 | In the books folder, you can download the following books in PDF format: 65 | - Dalgaard 2008. Introductory Statistics with R 66 | - Zuur et al. 2009. A Beginner's Guide to R 67 | 68 | ## Contributed R Documentation 69 | These resources (mostly large PDFs, but some HTML sites & ZIP files) are sourced from the CRAN area for contributed documentation: 70 | 71 | - Visual Statistics. Use R! by Alexey Shipunov 72 | - Using R for Data Analysis and Graphics - Introduction, Examples and Commentary by John Maindonald 73 | - Practical Regression and Anova using R by Julian Faraway 74 | - 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. 75 | - An Introduction to R: Software for Statistical Modelling & Computing by Petra Kuhnert and Bill Venables 76 | - Introduction to the R Project for Statistical Computing for Use at the ITC by David Rossiter 77 | - Statistics Using R with Biological Examples by Kim Seefeld and Ernst Linder 78 | - IcebreakeR by Andrew Robinson 79 | - Applied Statistics for Bioinformatics Using R by Wim Krijnen 80 | - An Introduction to R by Longhow Lam 81 | - R and Data Mining: Examples and Case Studies by Yanchang Zhao 82 | - A Student's Guide to R by Nicholas J. Horton, Randall Pruim, and Daniel T. Kaplan 83 | - R for Beginners by Emmanuel Paradis 84 | - Kickstarting R (version 1.6) compiled by Jim Lemon 85 | - R for Windows Users (version 2.0) by Ko-Kang Wang 86 | - Building Microsoft Windows Versions of R and R packages under Intel Linux by Jun Yan and A. J. Rossini 87 | - The R language — a short companion by Marc Vandemeulebroecke 88 | - Fitting Distributions with R by Vito Ricci 89 | - The Friendly Beginners' R Course by Toby Marthews 90 | - An R companion to ‘Experimental Design’ by Vikneswaran 91 | - The R Guide (version 2.5) by Jason Owen 92 | - Multilevel Modelling in R by Paul Bliese: a brief introduction to R and the packages multilevel and nlme 93 | - Using R for Scientific Computing by Karline Soetaert: lecture notes and reference card for R beginners, including exercises 94 | - Creating R Packages: A Tutorial by Friedrich Leisch 95 | - Creating R Packages, Using CRAN, R-Forge, and Local R Archive Networks and Subversion Repositories by Spencer Graves and Sundar Dorai-Raj 96 | - R for Biologists by Marco Martinez 97 | - An introduction to data cleaning with R by Edwin de Jonge and Mark van der Loo 98 | - Introduction to visualising spatial data in R by Robin Lovelace and James Cheshire 99 | 100 | [Centre of Excellence for Australian Biodiversity and Heritage](http://EpicAustralia.org.au) 101 | 102 | -------------------------------------------------------------------------------- /R_logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CABAH/learningRresources/0668ec5c9d48259195a1003e6f9b69dde56deca1/R_logo.png -------------------------------------------------------------------------------- /articles/Ávila-Thieme et al. 2023-Methods Ecol Evol.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CABAH/learningRresources/0668ec5c9d48259195a1003e6f9b69dde56deca1/articles/Ávila-Thieme et al. 2023-Methods Ecol Evol.pdf -------------------------------------------------------------------------------- /articles/Elith et al. 2008-J Anim Ecol.pdf: -------------------------------------------------------------------------------- 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