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R in Action - early thoughts. I was invited to review the book R in Action written by Rob Kabacoff.

R in Action - early thoughts

Since I consider the Quick-R website, created by the same smart guy, one of the most valuable resources about R, It is both an honor and a pleasure to have the opportunity to take an early look at his book and to express some thoughts about it. First, this book is distributed under an early access policy that means, as it is stated on the editor's web site, that: This Early Access version of the book enables you to receive new chapters as they are being written.

You can also interact with the authors to ask questions, provide feedback and errata, and help shape the final manuscript on the Author Online. This is a nice publishing approach, the editor settled up an ad-hoc forum which allows real-time feedback from early adopters. Since only the initial part of the book is available, this short review will be at most incomplete and present only preliminary thoughts. The author makes large use of working example. Guide to Getting Started in Machine Learning. Someone at work recently asked how he should go about studying machine learning on his own.

Guide to Getting Started in Machine Learning

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). The pdf is available online. 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. Another great resource is the machine learning course MIT has posted on their OpenCourseWare site.

The R programming language for programmers coming from other pro. 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.

The R programming language for programmers coming from other pro

An Introduction to R. Table of Contents This is an introduction to R (“GNU S”), a language and environment for statistical computing and graphics.

An Introduction to R

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. 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.

How Google and Facebook are using R

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. Dataspora has experience leveraging predictive analytics in numerous industry verticals. Via Science has integrated the knowledge acquired, and will continue to target the core sectors Dataspora pioneered.

About Via Science Via Science = Big (Math + Computing + Data) The R Project for Statistical Computing.