R/parallel : An easy-to-use toolkit for Parallel Computing in R Impatient R Translations français: Translated by Kate Bondareva. Serbo-Croatian: Translated by Jovana Milutinovich from Geeks Education. Preface This is a tutorial (previously known as “Some hints for the R beginner”) for beginning to learn the R programming language. You are probably impatient to learn R — most people are. This page has several sections, they can be put into the four categories: General, Objects, Actions, Help. General Introduction Blank screen syndrome Misconceptions because of a previous language Helpful computer environments R vocabulary Epilogue Objects Key objects Reading data into R Seeing objects Saving objects Magic functions, magic objects Some file types Packages Actions What happens at R startup Key actions Errors and such Graphics Vectorization Make mistakes on purpose Help Introduction I asked R users what their biggest stumbling blocks were in learning R. > search()
Climate Charts & Graphs | My R and Climate Change Learning Curve Cookbook for R » Cookbook for R Geographic maps in R | Smart Data Collective The maps library for R is a powerful tool for creating maps of countries and regions of the world. For example, you can create a map of the USA and its states in just three lines of code: library(maps) map("state", interior = FALSE) map("state", boundary = FALSE, col="gray", add = TRUE) The coordinate system of the graph is latitude and longitude, so it's easy to overlay other spatial data on this map. Unfortunately, the data for the maps library isn't sufficient for some applications. GADM is a spatial database of the location of the world's administrative boundaries, and as Claudia Engel discovered the map information is available as native R objects that can be plotted directly with the spplot function (from the sp package). library(sp)con <- url(" Sweet! AnthroSpace: Download Global Administrative Areas as RData files Link to original post
R Beginner's Guide and R Bloggers Updates 1/1/2011 Update: Tal Galili wrote an article that revisits the first year of R-Bloggers and this post was listed as one of the top 14. Therefore, I decided to make a small update to each section. I start by describing the initial series of tutorials that I wrote. Since October 2009, I have written 13 articles [many more now, of course] for the R Tutorial Series blog. Introduction to R Descriptive Statistics Summary and Descriptive Statistics Data Visualization Scatterplots Correlation Zero-Order Correlations Regression I also have two additional R-related items to update you on. 1/1/2011 Update: I originally reported that 50 blogs composed the R Bloggers network. R Tutorial Series on R Bloggers R Bloggers ( is a website that aggregates over 50 different blogs that focus on R. R Beginner's Guide 11/1/2010 Update: Statistical Analysis with R is now available! Lastly, I want to let you know that I am working on a beginner's guide for R.
reshape. had.co.nz Reshape is anR package for flexibly restructuring and aggregating data. It is available on all platforms supported by R (Linux, OS X, Windows, ...). The current version is 0.8. Reshape (hopefully) makes it easy to do what you have been struggling to do with tapply, by, aggregate, xtabs, apply and summarise. It is also useful for getting your data into the correct structure for lattice or ggplot plots. Along with ggplot, reshape won the 2006 John Chambers Award for Statistical Computing. ggplot provides a new way of making plots in R, based on the grammar of graphics. How to install Reshape is available from CRAN so you can install it using the following R command: install.packages("reshape") Documentation The best place to start is “Reshaping data with the reshape package”, published in the journal of statistical software. You might also be interested in the slides (pdf, 140k) and paper (pdf, 260k) I presented at Directions in Statistical Computing 2005. Mailing list Demos Get involved
The R programming language for programmers coming from other programming languages IntroductionAssignment and underscoreVariable name gotchasVectorsSequencesTypesBoolean operatorsListsMatricesMissing values and NaNsCommentsFunctionsScopeMisc.Other resources Ukrainian translation Other languages: Powered by Translate Introduction I have written software professionally in perhaps a dozen programming languages, and the hardest language for me to learn has been R. R is more than a programming language. This document is a work in progress. Assignment and underscore The assignment operator in R is <- as in e <- m*c^2. It is also possible, though uncommon, to reverse the arrow and put the receiving variable on the right, as in m*c^2 -> e. It is sometimes possible to use = for assignment, though I don't understand when this is and is not allowed. However, when supplying default function arguments or calling functions with named arguments, you must use the = operator and cannot use the arrow. At some time in the past R, or its ancestor S, used underscore as assignment. Vectors Sequences
R Programming - Manuals R Basics The R & BioConductor manual provides a general introduction to the usage of the R environment and its basic command syntax. Code Editors for R Several excellent code editors are available that provide functionalities like R syntax highlighting, auto code indenting and utilities to send code/functions to the R console. Programming in R using Vim or Emacs Programming in R using RStudio Integrating R with Vim and Tmux Users interested in integrating R with vim and tmux may want to consult the Vim-R-Tmux configuration page. Finding Help Reference list on R programming (selection)R Programming for Bioinformatics, by Robert GentlemanAdvanced R, by Hadley WickhamS Programming, by W. Control Structures Conditional Executions Comparison Operators equal: ==not equal: ! Logical Operators If Statements If statements operate on length-one logical vectors. Syntax if(cond1=true) { cmd1 } else { cmd2 } Example if(1==0) { print(1) } else { print(2) } [1] 2 Avoid inserting newlines between '} else'. Loops Syntax
R for Psych Research This is one page of a series of tutorials for using R in psychological research. Much of material has also covered been covered in number of short courses or in a set of tutorials for specific problems. This particular page is an update of a previous guide to R which is being converted to HTML5 to be more readable. (For a very abbreviated form of this guide meant to help students do basic data analysis in a personality research course, see a very short guide. In addition, a short guide to data analysis in a research methods course offers some more detail on graphing.) There are many possible statistical programs that can be used in psychological research. It has been claimed that "The statistical programming language and computing environment S has become the de-facto standard among statisticians. The R project, based upon the S and S+ stats packages, has developed an extremely powerful set of "packages" that operate within one program. Back to Top Installing R on your computer entering ?
Statistical Computing with R: A tutorial 0.1. What is R?R is a software package especially suitable for data analysis and graphical representation. Functions and results of analysis are all stored as objects, allowing easy function modification and model building. R provides the language, tool, and environment in one convenient package. It is very flexible and highly customizable. On the other hand, R has a few weaknesses. 0.2 Where do I get R? and follow the download instructions. 0.3 Invoking RIf properly installed, usually R has a shortcut icon on the desktop screen and/or you can find it under Start|Programs|R menu. To quit R, type q() at the R prompt (>) and press Enter key. Commands you entered can be easily recalled and modified. Interactive graphics can serve as a great learning tool. Effect of kernel choice, sample size and bandwidth can be conveniently illustrated by the following demonstration: 2.1 ComputationFirst of all, R can be used as an ordinary calculator.
R Tutorial 321a Boyd Graduate Studies University of Georgia Athens, Georgia 30602 Introductory Materials¶ These materials are designed to offer an introduction to the use of R. Thank You! I have received a great deal of feedback from a number of people for various errors, typos, and dumb things. I have also written a book about programming R.