R 1
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Rapid - I - RapidAnalytics
RapidAnalytics Server (Overview); click to enlarge RapidAnalytics enriches RapidMiner by a Compute Server, a Repository Server, a Report and Result Server, Process Scheduling, sophisticated User Management and additional administration capabilities.
The description on the main R web page is good, and needn’t be repeated here; it describes a bit about R’s history and technical capabilities. Some things you might want to know about R if you’re encountering it for the first time: R is (according to the description linked above) “a language and environment for statistical computing and graphics”; you can think of it as a combination of a statistics package and a programming language. R is not supported by any commercial enterprise, but it has a very active development community, and there are companies that offer training courses etc..
getting-started:what-is-r:what-is-r [R Wiki]
Clustergram: A graph for visualizing cluster analyses (R code) | R-statistics blog
How to Extract Citation from a Body of Text Say, you have a text and you want to retrieve the cited names and years of publication. You wouldn't want to this by hand, wouldn't you?Try the following approach:(the text sample comes from THIS freely available publication)library(stringr)(txt […] Beta is not volatility
The R programming language includes many abbreviations. Abbreviations exist in function names, argument names, and allowed values for arguments. This post expands on over 150 R abbreviations with the aim of making it easier for users new to R who are trying to memorise R commands. Context Abbreviations save time when typing and can make for less cumbersome code.
150+ R Abbreviations
start [R-Node]
R-Node is a web front-end to the statistical analysis package R . Using this front-end, you can from any web browser connect to an R instance running on a remote (or local) server, and interact with it, similar to how you interact with R through the R console. The R-Node server supports most standard commands, and should work with any package that provides textual output, or output via the standard graphing mechanisms. The Introduction to R book has been used to test the system. Graphs are created for the most part on the server, and are presented to the user over the network, where they can be saved by downloading. In some instances simple client-side SVG implementations of the graphing functionality has been implemented (e.g. plot() and hist()), though it is possible this will be replaced with the more complete rwebvis implementation which generates graphs server-side.
R-Node - Gitorious
R-Node is a web front-end to the statistical analysis package R. more… Using this front-end, you can from any web browser connect to an R instance running on a remote (or local) server, and interact with it, sending commandsMore mirrors are needed to keep this application online. If you are able to supply or sponsor a dedicated or virtual server, please contact the author. People who have made this possible: Hadley Wickham, Jeffrey Horner, Michael Driscoll, Nicolás Della Penna, Jan de Leeuw, Robert Gould, Jose Hales-Garcia, Verghese Nallengara (Let me know me if I forgot to mention your name).
yeroon.net/ggplot2
A Practical Guide to Geostatistical Mapping | by Tomislav Hengl | 291 p. | ISBN 978-90-9024981-0
This figure is from: Hengl, T., Heuvelink, G.B.M., Percec Tadic, M., Pebesma, E., 2011. "Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images". Theoretical and Applied Climatology .
The followings introductory post is intended for new users of R. It deals with the restructuring of data: what it is and how to perform it using base R functions and the {reshape} package. This is a guest article by Dr. Robert I. Kabacoff , the founder of (one of) the first online R tutorials websites: Quick-R . Kabacoff has recently published the book ” R in Action “, providing a detailed walk-through for the R language based on various examples for illustrating R’s features (data manipulation, statistical methods, graphics, and so on…).
R-statistics blog
Douglas Merrill , former CIO/VP of Engineering at Google, writes in Forbes about using the R language for data analysis: Most folks with math-oriented graduate degrees will have written something in R, a non-commercial option for your big data analysis. So, great graduates from great graduate schools know great tools. His post is titled 'R Is Not Enough For "Big Data" ', and you might be surprised to learn that I agree that title, although for a different reason.
Revolutions: Interactive stock visualizations with R
ggplot. had.co.nz
ggplot2 is a plotting system for R, based on the grammar of graphics, which tries to take the good parts of base and lattice graphics and none of the bad parts. It takes care of many of the fiddly details that make plotting a hassle (like drawing legends) as well as providing a powerful model of graphics that makes it easy to produce complex multi-layered graphics. You are welcome to ask ggplot2 questions on R-help, but if you'd like to participate in a more focussed mailing list, please sign up for the ggplot2 mailing list:Statistics
Introduces the basic concepts, logic, and issues involved in statistical reasoning. Topics include Exploratory Data Analysis, Producing Data and Study Design, Probability and Statistical Inference . Select one of the two new courses below. Both courses include all expository text, simulations, case studies, comprehension tests, interactive learning exercises, and the Stat Tutor labs. Both courses contain all of the instructions for the four statistics packages options we support.Feb 02 2010 Information graphics , or infographics , are visual representations of information, data or knowledge. The graphics are used where complex information needs to be explained quickly and clearly, such as on signs and maps and in journalism, technical writing and education.
Stunning Infographics and Data Visualization - Noupe
Incanter: Statistical Computing and Graphics Environment for Clojure
Interactive, dynamic, functional statistical-programming on the JVM Incanter leverages both the power of Clojure, a dynamically-typed, functional programming language, and the rich set of libraries available on the JVM for accessing, processing, and visualizing data. At its core are the Parallel Colt numerics library, a multithreaded version of Colt , the JFreeChart charting library, the Processing visualization library, as well as several other Java and Clojure libraries. Clojure’s seamless integration with Java makes leveraging these libraries much simpler than is possible in R, and Incanter’s R-like semantics and interactive shell makes statistical programming much simpler than is possible in pure Java.Known for its versatility, the free programming language R is widely used for statistical computing and graphics, but is also a fully functional programming language well suited to scientific programming. An Introduction to Scientific Programming and Simulation Using R teaches the skills needed to perform scientific programming while also introducing stochastic modelling. Stochastic modelling in particular, and mathematical modelling in general, are intimately linked to scientific programming because the numerical techniques of scientific programming enable the practical application of mathematical models to real-world problems.