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I was inspired by the Revolution Analytics blog post http://blog.revolutionanalytics.com/2009/11/charting-time-series-as-calendar-heat-maps-in-r.html on the d3.js style calendar heat map that Paul Bleicher from Humedica developed in R. In an effo...
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Plusieurs articles récents viennent éclairer l’état actuel de la sociologie française. Un article de Demazière, un autre de Stéphane Beaud. Je voulais apporter ma contribution au débat.
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.
R-statistics blog
What I would like is a nice list of all of credible sources on the Internet for finding data to use with R projects. I know that this is a crazy idea, not well formulated (what are data after all) and loaded with absurd computational and theoretical challenges. (Why can't I just google "data R" and get what I want?) So, what can I do?
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Inspired by this tutorial, I thought that it would be nice to have the possibility to have access to weather forecast directly from the R command line, for example for a personalized start-up message such as the one below: Fortunately, thanks to the always useful Duncan Temple Lang's XML package (see here for a tutorial about XML programming under R), it is straightforward to write few lines of R code to invoke the google weather api for the location of interest, retrieve the XML file, parse it using the XPath paradigm and get the required informations: Times ago I came to the conclusion that the best way to organize my R code is to create packages even for basic tasks. I know that It seems too much effort for this trivial task (and it was in the past) but fortunately, thanks to the Hadley Wickham's devtools package development It has become a piece of cake process (sort of)! Below I present the minimal workflow I used to create this simple package.
One R Tip A Day
This post provides links to a range of resources related to the use and interpretation of correlations. I wanted to provide a page with links to a number of additional resources that would be useful both for those of my students who might be keen to learn more and for anyone else who might be interested. Specifically, this post provides links to: (a) introductory book-style chapters on correlation, (b) resources related to assorted issues in correlation (i.e., discussion of causal inference, correlation with various variable types, range restriction, statistical power, correlation interpretation, and significance testing), (c) tutorials on computing correlations using SPSS and R, and (d) tips for reporting correlations in APA Style.
Psychology and Statistics
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. Douglas's point — and it's a valid one — is that simply pumping data through any software tool, without an understanding of the problem you're trying to solve and how statistical models apply to it, can lead to getting the wrong answers to the wrong questions: If you ask the wrong question, you will be able to find statistics that give answers that are simply wrong (or, at best, misleading).