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R news and tutorials contributed by (563) R bloggers

R news and tutorials contributed by (563) R bloggers
Le Monde puzzle [#869] A Le Monde mathematical puzzle once again in a Sudoku mode: In an nxn table, all integers between 1 and n appear n times. If max denotes the maximum... Read more » Great Resource for Teaching Statistics with R If you're having trouble teaching statistics using R, then you'll just love the statsTeachR collaboration.It's being launched officially at the 2014 New England Statistics Symposium today.Here's what...

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statistics by Terry M. Therneau Ph.D.Faculty, Mayo Clinic About a year ago there was a query about how to do "type 3" tests for a Cox model on the R help list, which someone wanted because SAS does it. The SAS addition looked suspicious to me, but as the author of the survival package I thought I should understand the issue more deeply. The R language, for programmers IntroductionAssignment and underscoreVariable name gotchasVectorsSequencesTypesBoolean operatorsListsMatricesMissing values and NaNsCommentsFunctionsScopeMisc.Other resources Introduction I have written software professionally in perhaps a dozen programming languages, and the hardest language for me to learn has been R.

In-depth introduction to machine learning in 15 hours of expert videos In January 2014, Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, An Introduction to Statistical Learning with Applications in R (ISLR). I found it to be an excellent course in statistical learning (also known as “machine learning”), largely due to the high quality of both the textbook and the video lectures. And as an R user, it was extremely helpful that they included R code to demonstrate most of the techniques described in the book. If you are new to machine learning (and even if you are not an R user), I highly recommend reading ISLR from cover-to-cover to gain both a theoretical and practical understanding of many important methods for regression and classification. It is available as a free PDF download from the authors’ website. Chapter 1: Introduction (slides, playlist)

Our top 10 Data Science articles in 2014 2014 has been a year of growth for us. We now get 10x traffic compared to what we used to get 12 months back. It gives us immense satisfaction to be able to create something which is helping more and more people every day. We only hope that we could get some more time to create more content for our audience! R by example Basics Reading files Graphs Prediction model for the FIFA World Cup 2014 Like a last minute goal, so to speak, Andreas Groll and Gunther Schauberger of Ludwig-Maximilians-University Munich announced their predictions for the FIFA World Cup 2014 in Brazil – just hours before the opening game. Andreas Groll, with his successful prediction of the European Championship 2012 already experienced in this field, and Gunther Schauberger did set out to predict the 2014 world cup champion based on statistical modeling techniques and R. A bit surprisingly, Germany is estimated with highest probability of winning the trophy (28.80%), exceeding Brazil’s probability (the favorite according to most bookmakers) only marginally (27.65%).

Useful new R packages for data visualization and analysis The following is from a hands-on session I led at the recent Computer Assisted Reporting conference. There's a lot of activity going on with R packages now because of a new R development package called htmlwidgets, making it easy for people to write R wrappers for existing JavaScript libraries. The first html-widget-inspired package I want us to demo is Leaflet for R. If you’re not familiar with Leaflet, it’s a JavaScript mapping package. Must read books for Analysts (or people interested in Analytics) One of the ways I continue my learning is reading. I read for 30 minutes before hitting the bed every day. This not only makes sure that I learn some thing daily, but also ends my day in a fulfilling manner.

Statistics with R Warning Here are the notes I took while discovering and using the statistical environment R. However, I do not claim any competence in the domains I tackle: I hope you will find those notes useful, but keep you eyes open -- errors and bad advice are still lurking in those pages... Should you want it, I have prepared a quick-and-dirty PDF version of this document. The old, French version is still available, in HTML or as a single file. You may also want all the code in this document.

Learn R for beginners with our PDF With so much emphasis on getting insight from data these days, it's no wonder that R is rapidly rising in popularity. R was designed from day one to handle statistics and data visualization, it's highly extensible with many new packages aimed at solving real-world problems and it's open source (read "free"). If you're ready to learn, we have just the ticket: A free PDF of Computerworld's "Beginner's guide to R." Included in this 45-page guide: Introduction to R for Data Mining For a quick start: Find a way of orienting yourself in the open source R worldHave a definite application area in mindSet an initial goal of doing something useful and then build on it In this webinar, we focus on data mining as the application area and show how anyone with just a basic knowledge of elementary data mining techniques can become immediately productive in R. We will: Provide an orientation to R’s data mining resourcesShow how to use the "point and click" open source data mining GUI, rattle, to perform the basic data mining functions of exploring and visualizing data, building classification models on training data sets, and using these models to classify new data.Show the simple R commands to accomplish these same tasks without the GUIDemonstrate how to build on these fundamental skills to gain further competence in RMove away from using small test data sets and show with the same level of skill one could analyze some fairly large data sets with RevoScaleR

R Tutorial — R Tutorial 321a Boyd Graduate Studies University of Georgia Athens, Georgia 30602 Shiny - Tutorial The How to Start Shiny video series will take you from R programmer to Shiny developer. Watch the complete tutorial here, or jump to a specific chapter by clicking a link below. The entire tutorial is two hours and 25 minutes long. Part 1 - How to build a Shiny app Part 3 - How to customize appearance You will get the most out of these tutorials if you already know how to program in R, but not Shiny. Installing swirl on Linux · swirldev/swirl Wiki · GitHub swirl and its dependencies require R version 3.0.2 or later as well as a recent version of libcurl. This page is our attempt to collect any information that might be helpful to Linux users wanting to install swirl. Ubuntu and its derivatives

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