Noelle Beckman
This list contains resources for learning the R software environment for statistical computing and graphics.
Source: http://seedscape.github.io/BeckmanLab/Resources.html
R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.
simecol allows to implement ecological models (ODEs, IBMs, ...) using a template-like object-oriented stucture. It helps to organize scenarios and may also be useful for other areas.
R Resources compiled by Paul J. Hurtado.
Book by Faraway on regression and ANOVA using R
This gives an overview of using R, including managing data, hypothesis testing, and plotting.
This is the companion website for “Advanced R”, a book in Chapman & Hall’s R Series. The book is designed primarily for R users who want to improve their programming skills and understanding of the language. It should also be useful for programmers coming to R from other languages, as it explains some of R’s quirks and shows how some parts that seem horrible do have a positive side.
R news and tutorials contributed by (573) R bloggers
Analysis of Biological Assay (Bioassay)
R has a full library of tools for working with spatial data. This includes tools for both vector and raster data, as well as interfacing with data from other sources (like ArcGIS) and making maps. These tutorials — which build off Claudia Engel’s excellent GIS in R tutorials — are designed for users with some familiarity … Continue reading GIS in R →
Analysis of Ecological and Environmental Data
This site is dedicated to compiling information on the practical use of generalized linear mixed models, primarily intended for ecologists and evolutionary biologists but also possibly of use to others with similar backgrounds and statistical challenges.
Welcome to the Cookbook for R. The goal of the cookbook is to provide solutions to common tasks and problems in analyzing data.
This is a complete list of all available rOpenSci packages.
A web application framework for R
Turn your analyses into interactive web applications
No HTML, CSS, or JavaScript knowledge required
dplyr is a new package which provides a set of tools for efficiently manipulating datasets in R. dplyr is the next iteration of plyr, focussing on only data frames. dplyr is faster, has a more cons...
Learn the fundamentals for R programming and gain the tools needed for doing data science.
RStudio Cheat Sheets The cheat sheets below make it easy to learn about and use some of our favorite packages. From time to time, we will add new cheat sheets to the gallery. If you'd like us to drop you an email when we do, let us know by clicking the button to the
Tutorial for learning ggplot
An FAQ on the weirder parts of R
