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Unveiling the beauty of statistics for a fact based world view.

Unveiling the beauty of statistics for a fact based world view.

Gapminder: Unveiling the beauty of statistics for a fact based world view. Poverty Myths By almost any measure, the world is better than it has ever been. People are living longer, healthier lives. Many nations that were aid recipients are now self-sufficient. You might think that such striking progress would be widely celebrated, but in fact, Melinda and I are struck by how many people think the world is getting worse. The belief that the world can’t solve extreme poverty and disease isn’t just mistaken. Curriculum Vitae (mis à jour le: 25/01/2011) Pierre Lafaye de Micheaux Né le 27 mars 1973 à Paris. Marié avec deux enfants. Nationalités : canadienne, française, suisse. Séjours (courts) dans d’autres laboratoires de recherche universitaire Conférences invitées1 Sydney, Australie (2014). Mini-cours et tutoriels Invitation de chercheurs Bourses et subventions Distinction académique Fonctions électives Responsabilités administratives Université de Montréal, Département de Mathématiques et de Statistique Université Pierre Mendès France, Département STID de l’IUT2 Arbitrage d’articles de revues Bernoulli, Canadian Journal of Statistics, Cognitive Computation, Computational Statistics, Computational Statistics and Data Analysis, Frontiers Neuroscience, Journal of Multivariate Analysis, Journal of Statistical Planning and Inference, Journal of Statistical Software, Mathematical Reviews, Medical Image Computing and Computer Assisted Intervention (MICCAI) Proceedings, Statistical Methodology. Comités éditoriaux Thèmes de recherche privilégiés 2014?

Being Human 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. 1. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

RStudio Server Amazon Machine Image (AMI) - Louis Aslett Current AMI Quick Reference (27nd Jun 2015)Amazon instance type reference Click to launch through AWS web interface: What’s new recently? Easy Dropbox setup to make syncing files on/off server easy, including selective folder sync. Preinstalled RStudioAMI R package for server control. HVM AMIs for full current generation instance support. Defaults to high speed SSD drives (faster, zero IO costs, only $1pm in most regions). < Back to homepage Amazon’s EC2 platform provides a convenient environment for rapidly procuring computational resources in the cloud. To get started with the Amazon cloud, you must first signup for an AWS account if you don’t already have one. Click here for a simple video guide to using the AMIs listed here, or for more detailed information read on. What is this? If you want to run a server in the Amazon cloud, you have to select what system you are going to bootup. In particular, many common tools and dependencies are built-in. Why an RStudio AMI? AMI Release History

Interactive graphics for data analysis: principles and examples - Martin Theus, Simon Urbanek Visualizing Data: Challenges to Presentation of Quality Graphics—and Solutions Naomi Robbins, a consultant and seminar leader who specializes in the graphical display of data, offers training on the effective presentation of data. She also reviews documents and presentations for clients. She is the author of Creating More Effective Graphs. Three common challenges statisticians and others face when preparing data for presentation include poor options and defaults in many software packages used for creating graphs, managers and colleagues who are socialized to expect figures that attract attention, and poor instructions from conference organizers. This article addresses each of these challenges and offers some tips for dealing with them. Poor Options and Defaults in Many Software Packages Many software programs for drawing charts and graphs offer defaults and options that are full of fancy embellishments that detract from the clear and accurate communication of data. The pseudo three-dimensional problem is easily solved by using a two-dimensional bar chart. Summary

Graphics, Traditional - R for SAS and SPSS Users and R for Stata Users contain examples for advanced users. Paul Murrell's book R Graphics (right) also offers excellent coverage of traditional graphics in great detail. Bar Plots Bar Plots of Counts If you have pre-summarized data, it is easy to get a bar plot of it. > barplot( c(40,60) ) This bar plot summarizes the variable q4. > plot( as.factor(q4) )> barplot( table(q4) ) This plots a factor, gender. > plot(gender) # or...> barplot( table(gender) ) Using traditional graphics functions you can turn a graph on its side by adding the argument, horiz = TRUE. > plot(workshop, horiz=TRUE) # or...> barplot( table(workshop), horiz=TRUE) A stacked bar plot is like a rectangular pie chart. > barplot( as.matrix( table(workshop) ), beside = FALSE) You can visualize frequencies on two factors by using plot(). > plot(workshop, gender, xlab="", ylab="") You can also do this plot using the mosaicplot() function. > mosaicplot( table(workshop, gender), main = "" ) Pie Charts Dot Charts

d3.js ggplot. Learning R The Likert Question Question | Data Revelations Overview I’ve been looking at various examples over at Stephen Few’s Perpetual Edge web site and one example (this one) really resonates with me as it deals with how to visualize Likert Scale questions. For the past five years I’ve used Tableau to analyze literally thousands of survey questions, but I’ve never really been happy with how I presented Likert Scale questions results. That said, a new version of Tableau has brought new capabilities… But first, just what is a Likert Scale question? You’re probably familiar with it, you just may not have known that it had a particular name. The “scale” part of the “Likert Scale” comes from applying values to each of the answers. Poor = 1 Fair = 2 Average = 3 Good = 4 Excellent = 5 I’ve purposely set up an example that asks ten questions of the survey taker (only six are shown above). Note: An interactive dashboard that presents all the different approaches may be found at the bottom of this blog post. Approach One – Pie Charts Yuck. Good question.