Things I tend to forget
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
An Introduction to R
Table of Contents This is an introduction to R (“GNU S”), a language and environment for statistical computing and graphics. R is similar to the award-winning1 S system, which was developed at Bell Laboratories by John Chambers et al. It provides a wide variety of statistical and graphical techniques (linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, ...). This manual provides information on data types, programming elements, statistical modelling and graphics. This manual is for R, version 3.1.0 (2014-04-10). Copyright © 1990 W. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Preface This introduction to R is derived from an original set of notes describing the S and S-PLUS environments written in 1990–2 by Bill Venables and David M. Comments and corrections are always welcome. Suggestions to the reader 1.1 The R environment Try ?
Quick-R: Home Page
Learning R
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. A few more have been added since and even more planned in the upcoming year. As always, an up to date listing of my articles can be found on the R Tutorial Series blog. 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
Guide to Getting Started in Machine Learning
Someone at work recently asked how he should go about studying machine learning on his own. So I’m putting together a little guide. This post will be a living document…I’ll keep adding to it, so please suggest additions and make comments. Fortunately, there’s a ton of great resources that are free and on the web. The very best way to get started that I can think of is to read chapter one of The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2009 edition). Once you’ve read the first chapter, download R. Once you’ve installed R, maybe played around a little, then check out this page which describes the major machine learning packages in R. Oh, by the way, if you want to start playing around with machine learning in R, you’ll need data. I’d suggest next reading more of The Elements of Statistical Learning. If you’re looking for perhaps a more passive experience, or want the feel of a classrom, Andrew Ng of Stanford has posted all of his lectures online.
Technical resources - The IAVS Vegetation Classification Methods Website
This section lists technical resources (data sources, software, on-line tutorials...) that can be helpful to perform classifications of vegetation. Readers are encouraged to report any new resource that is missing by contacting any of the current contributors of VCM. Millions of vegetation plots have been collected and partly digitized for local and regional purposes. Exchange formats: Veg-X A new effort to develop a core semantic model for observational data in the ecological and environmental sciences is underway (TDWG Observations Task group). Stand-alone programs The program JUICE (Tichý 2002) was designed as a Microsoft Windows application for editing, classification and analysis of large phytosociological tables and databases. MULVA-5 (Wildi & Orloci 1996) is a program package designed to apply multivariate statistical methods to vegetation and site data as a means of investigation in plant ecology. R packages Package 'cluster' Package 'ecodist' Package 'indicspecies' Package 'isopam'
Cookbook for R » Cookbook for R
How Google and Facebook are using R
Cambridge, Mass. – March 4, 2011 – Via Science announced the acquisition of Dataspora, a predictive analytics firm that helps companies solve complex big data problems. The acquisition helps strengthen Via Science’s positioning to support the consumer packaged goods and retail sectors, areas of focus for Dataspora. REFS™ provides the ability to leverage causal mathematics at scale with its supercomputing platform. This allows decision-makers to make better use of data with mathematical models that can diagnose problems or predict future outcomes. Via Science has invested over 10 years and $25 million to prove the value of REFS™ in high-stakes problem areas such as precision medicine and quantitative trading. Via Science has integrated the knowledge acquired, and will continue to target the core sectors Dataspora pioneered. About Via Science Via Science applies “big math” to solve high-value, complex problems. Via Science = Big (Math + Computing + Data)
